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Transcript of Meteosat Second Generation (MSG) Cloud Mask, Cloud ... · cloud heights were greater than 3000m....
Meteosat Second Generation (MSG) Cloud
Mask, Cloud Property Determination and
Rainfall Comparison with In-situ Observations
Peter Silla Masika
March, 2007
Meteosat Second Generation (MSG) Cloud Mask,
Cloud Property Determination and Rainfall
Comparison with In-situ Observations
By
Peter Silla Masika
Thesis submitted to the International Institute for Geo-information Science and Earth Observation in
partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science
and Earth Observation in Water Resources and Environmental Management Programme
Specialisation: Advanced use of Remote Sensing in Water Resources Management, Irrigation and
Drainage
Thesis Assessment Board
Prof. Dr. Ir. Z. Su Chairman (ITC, Enschede)
Dr. Ir. M. Booij External Examiner (Twente University, Enschede)
Dr. B. H. P. Maathuis Primary Supervisor (ITC, Enschede)
Dr. T. H. M. Rientjes Member (ITC, Enschede)
INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION
ENSCHEDE, THE NETHERLANDS
Disclaimer
This document describes work undertaken as part of a programme of study at the International
Institute for Geo-information Science and Earth Observation. All views and opinions expressed
therein remain the sole responsibility of the author, and do not necessarily represent those of
the institute.
Dedicated to my wife, daughter and departed soul of my son &
To my parents
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Abstract
To obtain accurate estimates of surface and cloud parameters from satellite data an algorithm has to
be developed which identifies cloud-free and cloud-contaminated pixels. Data from the Spinning
Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG)
satellites have been available since February 2004. The data is accessible to National Meteorological
and Hydrological Services (NMHSs). Unfortunately for NMHSs from developing countries the data
until recently has never been exhaustively exploited for rainfall estimation, one very important
variable in the atmosphere. Developed countries through research institutions have to some extent set
in place ways and means of exploiting the MSG data that has been made possible through data
distribution policy of EUMETSAT (free access to the data for research and education).
This study attempts to utilize available MSG data for developing simple cloud mask and height
algorithms and thereafter compare and determine the relationship between cloud height and observed
rainfall on a ground station. A multispectral threshold technique has been used: the test sequence
depends on solar illumination conditions and geographical location whereas most thresholds used here
were empirically determined and applied to each individual pixel to determine whether that pixel is
cloud-free or cloud-contaminated. The study starts from the premise of an acceptable trade-off
between calculation speed and accuracy in the output data. For this reason, only three infrared
channels of MSG satellite were used alongside climatological data provided by National
Oceanographic and Atmospheric Administration (NOAA) and also land surface climatological data
available from the WorldClim website.
The accurate measurement of spatial and temporal variation of tropical rainfall around the globe
remains one of the critical unresolved problems in the field of meteorology. This study attempted to
compare computed cloud height and observed rainfall on ground station (CGIS-Butare, Rwanda) and
derived cloud height-total rainfall relationship from storms over the same station.
Results from the simple cloud mask algorithm were validated using EUMETSAT cloud mask products
for a tropical region (≈ 11°N - 14°S and ≈ 6° - 51°E) over Africa. Overall accuracy of the simple
cloud mask developed here was found to be 87% for four scenes which were during day- and night-
time as well as twilight time as defined by sun elevation angles. Analysis of recorded rainfall at CGIS
and comparison of the same with computed cloud height showed that rainfall mainly occurred when
cloud heights were greater than 3000m. Further, deriving a relationship between the observed rainfall
and the cloud height was found to follow a Gaussian model in which clouds at approximate heights
between 4000m and 5000m produced higher amounts of rainfall. Below and above this height range,
rainfall amounts were found to be generally low. The derived cloud height-total rainfall relationship
was applied to other storms over this station. Initial results show low correlation between estimated
and observed rainfall. More synoptic observations have to be used to evaluate the derived
relationship. Next to this a better procedure to differentiate nimbostratus and cumulonimbus has to be
incorporated. Different relations between height and observed rainfall for the two types of clouds may
be derived which may improve the overall results.
KEY WORDS: MSG-SEVIRI, cloud mask, cloud height/type, rainfall comparison/estimation.
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Acknowledgements
First and foremost I am grateful to the government of the Netherlands for providing me the
scholarship under the Netherlands Fellowship Programme to pursue the M.Sc. course in Water
Resources and Environmental Management in this unique institution, ITC. I am equally grateful to my
organization, Kenya Meteorological Department for allowing me to fulfil my long-term dream of
attaining M.Sc. in Remote Sensing-related course.
My greatest gratitude goes to my supervisor, Dr. Ben H.P. Maathuis for his critical comments and
inputs. I wish to say your support was tremendous. Great thanks to all WREM staff especially Prof. Z.
Zu, Dr. A. Gieske, Dr. T. Rientjes, and Dr. R. Becht, for their valuable critical comments in this study.
This study could not have been accomplished without EUMETSAT’s favourable data distribution
policy for research and education. Special thanks go to this great organization and equally appreciate
their efforts in promoting satellite meteorology.
Yet again many thanks go to all ever dedicated staff members of WREM department at ITC for
imparting this valuable knowledge during that past 18 months. Equally thanks to the Programme
Director, Dr. Arno van Lieshout for his excellent assistance and cooperation.
I would like to appreciate MSG laboratory staff, specifically Mr. G. Reinink, for his tireless assistance
in retrieving satellite data.
I am grateful to all WREM 2006 course mates especially Essayas, Jose, Beyene, Edna, Anoja, Irena,
Marie, Musefa, and Mohammad for their continuous support and friendship bestowed on me during
that one and half years.
Special thanks go to all my friends with whom I shared my days in Enschede for eighteen months.
Fellow Kenyans cannot be left out for their encouragement during that period was great.
To my dear wife, Nancy and our lovely daughter, Faith thanks for your patience, prayers and daily
expectation of seeing me again. I sincerely owe you alot for all these. To my caring parents, my
brothers and sisters, and all friends in Kenya, your support and prayers cannot fail to be appreciated
too.
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List of Acronyms
A/MSU Advanced/Microwave Sound Unit
ANN Artificial Neural Networks
APOLLO AVHRR Processing Scheme Over cLouds, Land and Ocean
A/TOVS Advanced/Tiros-N Operational Vertical Sounder
AVHRR Advanced Very High Resolution Radiometer
BSC Bi-spectral Spatial Coherence
BTH Bi-spectral Threshold and Height
CCD Cold Cloud Duration
CCS Cloud Classification System
CGIS Geographic Information Systems and Remote Sensing Regional Outreach Centre in
Butare, Rwanda
CPC Climate Prediction Centre
CST Convective Stratiform Technique
DEM Digital Elevation Model
DMSP Defence Meteorological Satellite Program
EUMETcast European Organisation for the Exploitation of Meteorological Satellites’ Broadcast
System for Environmental Data
EUMETSAT European Organisation for the Exploitation of Meteorological Satellites
GAC Global Area Coverage
GHCC Global Hydrology and Climate Centre
GIS Geographic Information Systems
GOES Geostationary Operational Environmental Satellite
GRIB General Regularly-distributed Information in Binary form (GRIded Binary)
HIRLAM High Resolution Limited Area Model
HIRS High-resolution Interferometer Sounder
HRPT High Resolution Picture Transmission
HRV High Resolution Visible
IAPP International Advanced/Tiros-N (Television Infrared Observation Satellite-Next
generation) Operational Vertical Sounder (A/TVOS) Processing Package
IDL Interactive Data Language
ILWIS Integrated Land and Water Information System
ITCZ Inter-tropical Convergence Zone
ITPP International Tiros-N (Television Infrared Observation Satellite-Next generation)
Operational Vertical Sounder (TVOS) Processing Package
KLAROS Royal Netherlands Meteorological Institute (KNMI) Local APOLLO Retrievals in
an Operational System
KNMI Royal Netherlands Meteorological Institute
LAC Local Area Coverage
MPE Multi-sensor Precipitation Estimate
MSG Meteosat Second Generation
NOAA National Oceanographic and Atmospheric Administration
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NOAA-HL National Oceanographic and Atmospheric Administration (NOAA) High Latitude
NODC National Oceanographic Data Centre of the National Oceanographic and
Atmospheric Administration (NOAA)
NMHS National Meteorological and Hydrological Service
NWP Numerical Weather Prediction
NWS National Weather Service
PERSIAN Precipitation Estimation from Remotely Sensed Information using Artificial Neural
Networks
SAFNWC Satellite Application Facility for supporting Nowcasting and very short range
forecasting
SCM Simple Cloud Mask
SCH/T Simple Cloud Height/Type
SEVIRI Spinning Enhanced Visible and Infrared Imager
SSM/I Special Sensor Microwave Imager
SST Sea Surface Temperature
SYNOP World Meteorological Organization synoptic code for weather observations
TAMSAT Tropical Applications of Meteorology using SATellite
TIP-data TIROS-N (Television Infrared Observation Satellite-Next generation) Information
Processor data
TRMM Tropical Rainfall Measuring Mission
USGS United States Geological Survey
WMO World Meteorological Organization
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Table of contents
Abstract ……………………………………………………………………………………………….. i
Acknowledgements …………………………………………………………………………………… ii
Table of contents ……………………………………………………………………………………... iii
List of figures ………………………………………………………………………………………… v
List of tables …………………………………………………………………………………………. vii
List of Acronyms ………………………………………………………………………………….... viii
1. Introduction ....................................................................................................................................1
1.1. Background.............................................................................................................................1
1.2. Significance of the Study........................................................................................................2
1.3. Research Objectives................................................................................................................3
1.4. Research questions..................................................................................................................3
1.4.1. General ...............................................................................................................................3
1.4.2. Specific ...............................................................................................................................4
1.5. Research Hypothesis...............................................................................................................4
1.6. Scope of Study........................................................................................................................4
1.7. Logical Sequence of Research Approach / Methodology ......................................................5
1.8. Outline of the Thesis...............................................................................................................6
2. Literature Review...........................................................................................................................7
2.1. Introduction.............................................................................................................................7
2.2. Meteosat Second Generation (MSG) Satellite........................................................................9
2.3. Cloud Masking......................................................................................................................10
2.3.1. Météo-France (SAFNWC) Cloud Mask...........................................................................11
2.3.2. Météo-France (Ocean and Sea Ice SAF) Cloud Mask .....................................................13
2.3.3. Meteosat VIS-IR and NOAA-A/TOVS Image fusion Cloud Mask .................................15
2.3.4. KNMI Cloud Mask Algorithm .........................................................................................16
2.3.5. KLAROS Cloud Mask Algorithm....................................................................................16
2.3.6. APOLLO Cloud Mask......................................................................................................17
2.3.7. GHCC Cloud Mask ..........................................................................................................17
2.3.8. AFWA Cloud Mask Algorithm........................................................................................18
2.4. Precipitation Processes .........................................................................................................20
2.5. Satellite Rainfall Estimation.................................................................................................20
2.5.1. Cloud-Indexing Methods..................................................................................................21
2.5.2. Bi-spectral Methods .........................................................................................................21
2.5.3. Life-history Methods ........................................................................................................22
2.5.4. Cloud Model-based Techniques.......................................................................................23
2.5.5. Blending Techniques........................................................................................................24
2.5.5.1. EUMETSAT Multi-sensor Precipitation Estimate (MPE) ......................................26
3. Materials and Methods ................................................................................................................27
3.1. Data Acquisition ...................................................................................................................27
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3.1.1. MSG Satellite Data.......................................................................................................... 27
3.1.2. Climatological Data......................................................................................................... 29
3.1.3. Dew Point Temperature................................................................................................... 30
3.1.4. Synoptic Data and Field work ......................................................................................... 33
3.2. Cloud Masking Method ....................................................................................................... 36
3.3. Rainfall Estimation Method................................................................................................. 37
4. Data Processing and Results....................................................................................................... 39
4.1. MSG Satellite Images .......................................................................................................... 39
4.1.1. Generation of MSG Satellite and Solar Angles............................................................... 39
4.1.2. Day-time Cloud Mask...................................................................................................... 41
4.1.3. Night-time Cloud Mask ................................................................................................... 43
4.1.4. Twilight Cloud Mask....................................................................................................... 44
4.2. Rainfall Estimation (A case of CGIS Weather station) ....................................................... 48
4.2.1. Direct Comparison of Cloud Height and Rainfall Intensity............................................ 49
4.2.2. Direct Comparison of Cloud Height and Total Rainfall ................................................. 52
5. Discussions of Results.................................................................................................................. 57
5.1. Cloud Mask Results ............................................................................................................. 57
5.2. Cloud Height/Type Results.................................................................................................. 62
5.3. Rainfall Estimation Results ................................................................................................. 63
6. Conclusions and Recommendations .......................................................................................... 67
6.1. Conclusions.......................................................................................................................... 67
6.2. Recommendations................................................................................................................ 68
References ............................................................................................................................................ 70
Appendices ........................................................................................................................................... 73
Appendix A: ILWIS Script for Simple Cloud Mask and Height Algorithms ................................... 73
Appendix B: Samples of Batch Files ................................................................................................ 84
Appendix C: Sample of CGIS Weather Station Data........................................................................ 85
Appendix D: Storms over CGIS Weather Station used for Developing Cloud height – Rainfall
Intensity Regression Function ........................................................................................................... 92
Appendix E: Storms over CGIS Weather Station used for Developing Cloud height – Total Rainfall
Regression Function.......................................................................................................................... 94
Appendix F: Sample of Rain gauge (tipping bucket) Rainfall Data (Nairobi- Dagoretti
Meteorological Station)..................................................................................................................... 96
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List of figures
Figure 1-1: MSG/SEVIRI image of 5th July 2006 at 12:00 UTC as a false Colour Composite...............4
Figure 1-2: Three major phases and important steps in the methodology ...............................................5
Figure 2-1: Cloud types grouped into different families according to height range and form (Source:
Strahler, 1965) ........................................................................................................................................8
Figure 2-2: MSG image false colour composite (BGR) of 25th December 2006 at 12:00 UTC............10
Figure 2-3: MSG cloud mask for 25th December 2006 at 12:00 UTC (EUMETSAT, 2006) .................11
Figure 2-4: GOES-East and MSG satellites SST products (in ˚C) for 25th Dec 2006 at 13:00 UTC ....14
Figure 2-5: An example of pixel array under consideration with Tmin at (i,j) ........................................23
Figure 2-6: Some factors influencing the differences between space- and time-collocated TMI and
SSMI.......................................................................................................................................................24
Figure 2-7: The PERSIAN CCS model structure (source:(Hong et al., 2004a)) ..................................25
Figure 3-1: Flow chart for simple cloud mask (SCM) and cloud height/type (SCH/T) retrieval ..........27
Figure 3-2: MSG Data Retriever window (Courtesy of ITC) ................................................................28
Figure 3-3: False colour composite (bands 1, 2, and 3 –in BGR) (left) and Band 9 (10.8µm) (right) on
19/12/2006 at 12:00 UTC.......................................................................................................................29
Figure 3-4: Climatological Temperature (in °K) images; (a) day-time (b) night-time (c) mean , of
Africa and part of Atlantic Ocean for the month of May.......................................................................30
Figure 3-5: Schematic view of temperature lapse rates in an idealized convective cloud.....................32
Figure 3-6: Locations of the four stations shown on MSG satellite false colour composite image ......33
Figure 3-7: (a) Image acquisition by SEVIRI radiometer, and (b) Schematic diagram on MSG satellite
and Rain gauge observation time ...........................................................................................................35
Figure 4-1: Flow chart for generating MSG satellite and Sun angles....................................................39
Figure 4-2: Sun (for 26th December 2006 at 15:00 UTC) and MSG Satellite (0˚N and 0˚E) ................40
Figure 4-3: Solar illumination conditions on 26th December 2006 at 15:00 UTC.................................40
Figure 4-4: Description of the test sequences for Land surface (left) and Sea surface (right) ..............41
Figure 4-5: Description of test sequence for land surface (left) and sea surface (right)........................43
Figure 4-6: Description of test sequence for land surface (left) and sea surface (right)........................44
Figure 4-7: Cloud masks for (a) day-time, (b) twilight time, and (c) night-time; for MSG-1 image of
7th March 2006 at 15:30 UTC. ...............................................................................................................45
Figure 4-8: Solar illumination conditions on 7th March 2006 at 15:30 UTC.........................................45
Figure 4-9: Cloud mask (a) and false colour composite (b) for MSG image of 07/03/2006 at 15:30
UTC........................................................................................................................................................46
Figure 4-10: Cloud height (in Meters) image (MSG image of 07/03/2006 at 15:30 UTC)...................47
Figure 4-11: Classified cloud height image of 07/03/2006 at 15:30 UTC.............................................47
Figure 4-12: Segments (yellow lines) of cloud mask of 23/11/2005 at 13:30 UTC overlaid on False
colour composite (VIS006, VIS008, and NIR016) in (BGR) ................................................................48
Figure 4-13: Diurnal cloud height and Rainfall intensity changes on (a) 5th May 2006, and (b) 10th
May 2006................................................................................................................................................50
Figure 4-14: Rainfall intensities within cloud height classes ................................................................50
Figure 4-15: Gaussian model fit, X = Average cloud height (m), Y = Average rainfall intensity
(mm/hr)...................................................................................................................................................51
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Figure 4-16: Observed and estimated rainfall intensity for different storms ........................................ 51
Figure 4-17: Diurnal cloud height and Total rainfall changes on (a) 5th May 2006, and (b) 10th May
2006....................................................................................................................................................... 52
Figure 4-18: Gaussian model fit, X= Average storm height (m), Y= Total rainfall (mm) ................... 53
Figure 4-19: Observed and Estimated total rainfall plotted with the error bars ................................... 54
Figure 4-20: Relationship between the observed and the estimated total rainfall ................................ 55
Figure 5-1: Flow chart on segmentation and visualization of EUMETSAT CLM and SCM............... 58
Figure 5-2: EUMETSAT cloud mask assigned feature classes for 26th December 2006 at 15:00 UTC ..
..................................................................................................................................... 59
Figure 5-3: Cloud mask segments of EUMETSAT CLM (yellow lines) and SCM (red lines) for 26th
December 2006 at 15:00 UTC, on a false colour composite ................................................................ 59
Figure 5-4: EUMETSAT CTH (a), SCH/T (b), and Difference (between CTH and SCH/T) (c) images
for 25th December 2006 at 11:45 UTC (height is in meters)................................................................. 63
Figure 5-5: Diurnal height and Total rainfall changes on 1st March 2006 (left) and 28th October 2006
(right) over Naivasha station ................................................................................................................. 65
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List of tables
Table 2-1: Spectral channel characteristics of SEVIRI in terms of central, minimum and maximum
wavelength of the channels and the main application areas of each channel (Source: (EUMETSAT,
2006)) ............................................................................................................................................9
Table 2-2: Definition of illumination conditions (SAFNWC); solar elevation is in degrees ................12
Table 2-3: Test sequence over land (SAFNWC) ...................................................................................12
Table 2-4: Test sequence over sea (SAFNWC).....................................................................................12
Table 3-1: Locations of the four stations within Eastern Africa............................................................33
Table 4-1: Observed storms and their total amount of rainfall ..............................................................53
Table 4-2: Storm heights and estimated total rainfall ............................................................................54
Table 5-1: Contingency table for MSG image of 25th December 2006 at 12:00 UTC ..........................60
Table 5-2: Contingency table for MSG image of 26th December 2006 at 15:00 UTC ..........................60
Table 5-3: Contingency table for MSG image of 4th January 2007 at 22:00 UTC ................................61
Table 5-4: Contingency table for MSG image of 10th January 2007 at 17:00 UTC ..............................61
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
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1. Introduction
1.1. Background
For more than 40 years, meteorological satellites have been the best way to observe the changing
weather on a large scale (EUMETSAT, 2006) . Typically, operational meteorology utilizes two types
of satellites, namely; polar orbiting and geostationary satellites, to provide the required information.
Polar orbiting satellites fly at relatively low altitudes of approximately 800km above the earth surface
and can provide information based on a high spatial resolution. Geostationary satellites, on the other
hand, are in the equatorial plane and at high altitudes of about 36000km above the earth surface. Their
revolution time is the same as that of the earth itself and therefore the satellites are always viewing the
same area on the earth. They have low spatial resolution due to their altitudes. However, they can
perform frequent imaging, in animated mode, which can depict the ever-changing atmospheric
processes.
The first generation of European meteorological satellites dates back to 1977, with the launch of
Meteosat-1. Since then this series have advanced to Meteosat-7 which is currently located around
57°E and manoeuvring to replace Meteosat-5 located at 63°E.
These series are followed by Meteosat Second Generation (MSG) satellites of which the first one
(MSG-1 now Meteosat-8) was launched on 28th August 2002 and became operational in early 2004.
The second of this series (MSG-2 now Meteosat-9) was launched on 20th December 2005. These two
satellites are located at 0°N and 0°E.
MSG satellites are spin-stabilized and capable of greatly enhanced earth observations (EUMETSAT,
2006) . The Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor on board MSG has a
high temporal resolution of 15 minutes and spatial resolution of 3 km (sub-satellite) for all channels
except 1km for high resolution visible (HRV) channel. The major improvement for this series of
satellites is the enhanced spectral resolution of 12 channels. The presence of the 3.9µm channel in the
current sensor has allowed analyses of cloud cover especially at night-time.
The primary mission of MSG satellites is the continuous observation of earth’s full disk with a multi-
spectral imager. The repeat cycle of 15 minutes for full-disk imaging provides multi-spectral
observations of rapidly changing phenomena such as deep convection. They also provide better
retrieval of wind fields which are obtained from the tracking of clouds, water vapour and ozone
features. In this study, main attention is given to cloud properties, such as cloud height, that may be
associated with rainfall amounts observed a ground station.
Presence and characteristics of clouds gives information about the state of the atmosphere. For many
cloudy situations, the reflected visible radiation and the emitted thermal radiation are not simple to
interpret because the cloud is not the only reflecting/radiating source (Dlhopolsky and Feijt, 2001). Of
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
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importance is to determine cloud properties by first distinguishing cloud-free pixels from cloud-
contaminated pixels. Quantitative data sets obtained from the cloud-contaminated pixels have several
potential applications one of which is for water resources and environmental management.
In general, effective integrated water resources management requires timely, accurate and
comprehensive meteorological, hydrological and other related information. Use of satellites in
observing variables such as rainfall, evaporation and soil moisture has enhanced provision of these
data in a timely and effective manner for the water resources management sector. These
meteorological variables needs to be monitored effectively and since they are associated with
atmospheric moisture hence clouds, there is need to identify the clouds first through masking all
cloud-contaminated areas in satellite images.
Cloud masking allows identifying cloud-free areas where other products such as land or sea surface
temperatures may be computed. It also allows identifying cloudy areas where other products (e.g.
cloud types and cloud top temperature/height) may be derived. Cloud type on the other hand provides
a detailed cloud analysis. It may be used as input to an objective meso-scale analysis which in turn
may be used in a simple nowcasting scheme (Météo-France, 2005b). Cloud type product is essential
for generation of cloud top temperature and height products and for identification of precipitating
clouds which in turn may be used to estimate rainfall intensity/amount.
1.2. Significance of the Study
For a considerable long time, series of precipitation amounts are recorded worldwide. Such amounts,
mainly expressed in millimetres (mm) and collected during a day or an hour are not only useful for
general meteorological and climatological practices, but are of special interest for hydrology and
agricultural meteorology. Surface-based observations of precipitation is accomplished primarily by
gauges and, where economically viable, by radars. Over the world’s oceans these measurements are
often done on buoyancies which are few worldwide. On the other hand, over the land areas the
coverage from surface observations is not uniform. Worse still, ground measurements from the
conventional rain gauges have deteriorated over the last couple of years and thus an alternative is
being sought to continue providing precipitation measurements not only on spatial basis but also on
higher temporal scale. The field of remote sensing has advanced and through various meteorological
satellites precipitation estimation has been made possible to reasonable scales, for instance, 3km
(spatial resolution) and 15 minutes (temporal resolution) for MSG satellites.
However, satellites measure cloud properties (e.g. brightness temperature) an important product that
provides crucial information that can be used to infer rainfall intensity and/or total rainfall.
Understanding these properties, and the crucial information that can directly or indirectly be used in
water resources management, is important. Thus there is need for determining cloud type/height using
readily available MSG satellite data in order to estimate rainfall. (Maathuis et al., 2006) showed that
MSG retriever software developed at ITC can be used to retrieve MSG data and to estimate rainfall
over the entire MSG field of view which covers the whole of Africa and part of Europe. The results in
the study showed bias for low and high intensity rainfall amounts due to different types of clouds.
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
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Further, potential applications of developing cloud masking and cloud type algorithm are many. Some
of the most important ones nowadays are, in operational weather forecasting and in energy and water
balance studies. Clouds represent the most significant source of error in the extraction of earth surface
energy and water balance parameters out of meteorological satellite data (Valk et al., 1998) . Energy
and water balance models are used to estimate fluxes in cloudy conditions. In order to develop an
accurate energy balance mapping algorithms, cloud masking is essential.
Cloud mask and type software modules have been developed by the Centre de Météologie Spatiale of
Météo-France and are embedded in the Satellite Application Facility for supporting NoWCasting and
very short range forecasting (SAFNWC)/MSG software package that is distributed by EUMETSAT
(Derrien and Le Gléau, 2005). These cloud mask and type algorithms uses transfer functions derived
from atmospheric models which are not published. Most of National Hydrological Services, especially
in Africa, have no access to these transfer functions and even then may not be in a position to derive,
on their own, the transfer functions. Besides, due to financial limitations for most of these National
Hydrological Services, shareware or freeware (such as ILWIS) can be used for masking clouds and
determining the cloud type through semi-automated processing.
Thus there is a need for masking out clouds and determining their basic properties in order to improve
forecasted rainfall estimates from MSG. It is envisaged that improving rainfall estimation will assist
most of National Hydrological Services to provide information on the status of water resources within
their area of jurisdiction. It is also expected that it would further improve timely decision making for,
areas prone to disasters related to weather such as floods, landslides or areas frequently affected by
droughts. This therefore calls for a need to develop simple cloud mask (SCM) and cloud type/height
(SCH/T) algorithms which may be embedded in readily available shareware such as ILWIS.
1.3. Research Objectives
This study addressed the following two main objectives;
� Determination of cloud mask and cloud height/type on all daily MSG images; and
� Relating derived cloud height with rainfall at a ground rainfall station.
Specifically the study focused on:
� Developing simple cloud mask and height algorithms;
� Analyzing relationship between MSG cloud height images and observed rainfall intensity
and/or total rainfall at a ground station; and
� Using derived relation to estimate total rainfall from storms at various heights.
1.4. Research questions
1.4.1. General
Can simple cloud mask (SCM) and cloud height/type (SCH/T) algorithms be developed, using
ancillary input data from general climatology databases, and applied to MSG images covering the
whole of Africa?
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
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1.4.2. Specific
a) Can the cloud height from the masked cloud at every moment be used to determine
the cloud type?
b) Is there any relationship between the rainfall intensity and/ or total rainfall and the
cloud height/type?
c) Can the cloud mask and height algorithms developed be able to process
automatically MSG satellite data as they are received from EUMETSAT through
EUMETCast every 15 minutes?
1.5. Research Hypothesis
The study set the following hypotheses:
� The smaller the number of cloud forms appear in the atmosphere, the easier it becomes to
identify them in satellite imagery;
� The more complex the cloud mask algorithm structure becomes, the better it performs; and
� Satellite retrieved cloud properties can be related to rainfall observed on the earth surface.
1.6. Scope of Study
The study was conducted on the MSG field of view which covers the whole of Africa (≈ 39˚N - 38˚S
and ≈ 34˚W - 53˚E). MSG images as in figure 1-1 were subjected to the developed cloud mask and
cloud height algorithms.
However, for validation of the results a small portion (≈ 11°N - 14°S and ≈ 6°E - 51°E) of the view
was considered. Rainfall data from Geographic Information Systems and Remote Sensing Regional
Outreach Centre (CGIS), Rwanda was used. Also for the same purpose in-situ rainfall data was
collected from Nairobi-Dagoretti, Kisumu, and Naivasha in Kenya.
Figure 1-1: MSG/SEVIRI image of 5th July 2006 at 12:00 UTC as a false Colour Composite
(BGR) of bands 1, 2, and 3 (EUMETSAT, 2006)
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1.7. Logical Sequence of Research Approach / Methodology
The methodology in this study consists of three phases namely; pre-field work, field work campaign,
and post field work as shown in figure 1-2.
Figure 1-2: Three major phases and important steps in the methodology
Literature
review
Retrieving
MSG data
Retrieving
land surface
temperature
Retrieving
sea surface
temperature
Developing cloud mask and
height algorithms
Rainfall data
collection
Rainfall data
analysis
MSG image
processing with
developed algorithm
Comparing cloud height and total
rainfall and finding statistically
significant relation between total
storm rainfall and cloud height
Validation of the derived relation
Rainfall Estimation
Pre-field work Phase
Field work Phase
Post field work Phase
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1.8. Outline of the Thesis
The thesis consists of six chapters.
Chapter 1 is the current chapter within which this section is contained. As has been noted, this chapter
briefly introduces the study, outlining its justification, the objectives, and the scope of the study.
General approach to carry out the study has also been shown.
Chapter 2 contains literature review on various methodologies adopted for cloud mask algorithm. A
few of these algorithms are presented in this chapter. Also presented here is an overview of satellite
rainfall estimation methods.
Chapter 3 provides general approach to this current study with details of data requirement and
acquisition. Sources of various data are pointed out in this chapter. Steps undertaken to process some
of the data are explained.
Chapter 4 elaborates on the data processing and presents the results on various stages in the study.
Chapter 5 provides detailed analysis of various results and discussions attached to these results.
Chapter 6 finally presents conclusions and recommendations drawn from the study.
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2. Literature Review
2.1. Introduction
Condensation or deposition of water above the earth’s surface creates clouds which develop in an air
mass that becomes saturated. The air mass may have passed over warm bodies of water, or over wet
surfaces and carried upward by turbulence or convection. Saturation occurs by way of atmospheric
mechanisms that the temperature of the air mass is cooled to its dew point. The lifting required for
cooling and condensing this water vapour results from several processes, and study of these processes
provides a key for understanding the distribution of rainfall in various parts of the world.
The major mechanisms or processes that occur causing cloud development include:
A) Orographic uplift: - Occurs where air is forced to rise because of the physical presence of
elevated land. As the air parcel rises, it cools as a result of adiabatic expansion at a rate
approximately 1°C per 100m until saturation. Beyond saturation level the parcel rises moist
adiabatically at a rate of 0.6°C per 100m (Strahler, 1965).
B) Convectional lifting: - Associated with surface heating of the air at the ground surface. Once
enough heating occurs, the air mass becomes warmer and lighter than the surrounding
environment and then rise expanding and cooling. When sufficient cooling takes place,
saturation occurs forming clouds. This is the common phenomena within tropics forming
cumulus cloud and or cumulonimbus clouds (thunderstorms).
C) Convergence or Frontal lifting: - Takes place when two air masses come together of which
in most cases they have different temperature and moisture characteristics. In frontal lifting,
one of the air masses is usually warm and moist while the other is cold and dry. The leading
edge of the cold dry air acts as an inclined wall or front causing the moist warm air to be lifted
thereby cooling and saturation is finally reached. This is common phenomena in mid-latitudes
whereas near the equator winds from both northern and southern hemisphere meet at the Inter-
tropical Convergence Zone (ITCZ) and lifting, cooling, saturation of the air mass occur
forming clouds.
D) Radiative cooling:- Occurs when the sun no longer supplies the earth surface or water body
surface and overlying air with energy derived from solar insolation (e.g. at night). Here the
surface of the earth now begins to lose energy in the form of longwave radiation which cools
the ground and air above it. Clouds that result from this type of cooling take the form of
surface fog.
Basically clouds consist of extremely tiny droplets of water (≈ 0.02 to 0.06mm) in diameter, or minute
crystals of ice. Clouds appear white when thin or when sun shines on the outer surface. When dense
and thick, clouds appear grey or dark underneath. The presence and movement of clouds is often the
only clue that indicates a significant meteorological process occurring in the atmosphere. They
indicate the presence of moisture and some type of cooling mechanism.
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Cloud types may be classified on the basis of two characteristics: general form and altitude (Strahler,
1965). There are two major forms of clouds namely; stratiform or layered types and cumiliform.
Stratiform are further subdivided according to the level of elevation at which they lie. Clouds lie
within high (cirrus and its related forms), middle (alto-stratus and alto-cumulus) and low (stratus,
nimbostratus, and stratocumulus) levels. Vertically developed clouds mainly brought about by
thermal convection or frontal lifting consists of fair weather clouds and cumulonimbus type of clouds.
Figure 2-1 below shows different cloud types grouped into different families according to their
altitudes.
Figure 2-1: Cloud types grouped into different families according to height range and form (Source: Strahler,
1965)
Based on cloud altitudes, three major families are classified namely; family A (high level clouds),
family B (middle level clouds), and family C (low level clouds). From figure 2-1, the approximate
altitudes of these families are shown. In addition and of high importance in precipitation occurrence is
a fourth family type D, which includes clouds with vertical developments mainly due to convection.
Within these families and particularly family C and D, there exist the multi-layered clouds which are
those with higher depths which give precipitation hydrometers a better environment to develop and
grow. Some of the clouds which exist as multi-layered are nimbostratus and cumulonimbus.
Nimbostrati are considered multi-layered clouds because their vertical extent often goes well into the
middle cloud region. These clouds are dark, usually overcast, and are associated with large areas of
continuous precipitation. Cumulonimbuses on the hand are clouds that can produce lightning, thunder,
heavy rains, hail, and strong winds. They are the tallest of all clouds that can span all cloud layers.
They usually have large anvil-shaped tops which form due to strong winds at high levels of the
atmosphere.
Thus in the atmosphere the most relevant type of clouds in relation to contribution to precipitation are
the multi-layered clouds.
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2.2. Meteosat Second Generation (MSG) Satellite
Meteosat Second Generation (MSG) satellites provide vital data for meteorology and climatology at
frequent intervals and over wide areas. These series of satellites provides information for the entire
African continent at much higher spatial and temporal resolutions as compared to the earlier
meteorological satellite series. Coupled with these characteristics is the higher spectral resolution (12
bands/channels) of the SEVIRI instrument as provided in table 2-1.
Table 2-1: Spectral channel characteristics of SEVIRI in terms of central, minimum and maximum
wavelength of the channels and the main application areas of each channel (Source:
(EUMETSAT, 2006))
Band Spectral Characteristics of Spatial Main observational
No. Band spectral band Resolution application
(µm) (µm) (km, Sub-
satellite)
λcen λmin λmax
1 VIS0.6 0.635 0.56 0.71 3 Surface, clouds, wind fields
2 VIS0.8 0.81 0.74 0.88 3 Surface, clouds, wind fields
3 NIR1.6 1.64 1.50 1.78 3 Surface, Cloud phase
4 IR3.9 3.90 3.48 4.36 3 Surface, clouds, wind fields
5 WV6.2 6.25 5.35 7.15 3 Water vapor, high level
clouds, atmospheric instability
6 WV7.3 7.35 6.85 7.85 3 Water vapor, atmospheric
instability
7 IR8.7 8.70 8.30 9.10 3 Surface, clouds, atmospheric
instability
8 IR9.7 9.66 9.38 9.94 3 Ozone
9 IR10.8 10.80 9.80 11.80 3 Surface, clouds, wind fields,
atmospheric instability
10 IR12.0 12.00 11.00 13.00 3 Surface, clouds, atmospheric
instability
11 IR13.4 13.40 12.40 14.40 3 Cirrus cloud height,
atmospheric instability
12 HRV Broadband (≈0.4-1.1) 1 Surface, clouds
One of the key objectives of setting the MSG programme by EUMETSAT was the extraction of
meteorological and geophysical fields from satellite image data in support of general meteorological,
climatological and environmental activities. Of importance among the products of MSG satellite is
cloud information which as stated earlier gives information about the state of the atmosphere. In order
to study the behaviour and properties of clouds, they must first be identified from the satellite image.
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The following section briefly discusses a number of cloud extraction methods already developed by
individuals and institutions in an attempt to identify all cloudy pixels in various satellite(s) images.
2.3. Cloud Masking
Cloud detection from remote sensing data is required for many applications. Some of these are such as
determination of cloud cover, identification of cloudy pixels for the retrieval of cloud-related
parameters, or exclusion of pixels with even minor cloud contamination if further processing would
be affected by the presence of clouds (Schröder et al., 2002). Several methods can be used to perform
cloud detection. Some of these methods are such as multispectral thresholding techniques that can be
applied to individual pixels (Saunders and Kriebel, 1988) , (Derrien et al., 1993), (Stowe et al., 1999).
Dynamic cloud cluster analysis relying on histogram analysis was suggested by (Desbois et al., 1982)
whereas (Bankert, 1994) indicated use of artificial neural networks which needs manual training.
Another approach was suggested by (Ebert, 1987) which involve pattern recognition techniques based
on large scale texture analysis.
Figure 2-2 is a false colour composite (NIR01.6, VIS0.8, and VIS0.6 as Blue, Green, and Red
respectively) image of 25th December 2006 at 12:00 UTC for a small portion of Eastern Africa
continent, which indicates presence of various types of clouds and how they appear in MSG satellite
image.
Figure 2-2: MSG image false colour composite (BGR) of 25th December 2006 at 12:00 UTC
Kenya
Tanzania
Rwanda
Uganda
Burundi
Convection
Thin cirrus
Low level clouds
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Usually low level clouds such as stratus are difficult to identify on an infrared image but on a visible
bands colour enhancement (and false colour composite) they appear white to grayish. They appear
layered and as semi-transparent. Deep convective clouds on a false colour composite appear cyan in
colour and with sharp edges i.e. with distinct boundaries with the rest of nearby clouds. On the other
hand high level clouds such as thin cirrus appear cyan in colour and feather-like in pattern. These
features can be seen on figure 2-2 as indicated for each type of these clouds.
Therefore clouds in such an image could be masked for various studies, one of which is for rainfall
estimation. Methods as enumerated in the following sections were an attempt to detect and mask all
clouds in various satellite images including MSG.
Some of these methods proved to have some disadvantages. For instance, (Dlhopolsky and Feijt,
2001), indicated that the histogram analysis method is time consuming and in many cases cannot
make accurate threshold without human interaction. Various authors and institutions have developed
cloud mask algorithms for detection of clouds. The most relevant algorithms are explained in the
proceeding sections.
2.3.1. Météo-France (SAFNWC) Cloud Mask
Satellite Application Facility for supporting NoWCasting (SAFNWC), within Météo-France and run
by a consortium of institutions namely; Spanish Meteorological Institute (INM), Météo-France, the
Swedish Meteorological Institute, and the Austrian Meteorological Institute, is tasked to develop and
maintain a software package allowing the extraction from MSG/SEVIRI imagery a set of 12 products
useful for nowcasting purposes on any user defined area in the MSG (Derrien and Le Gléau, 2005). In
the software, cloud mask and cloud type software modules are implemented. The cloud mask
algorithm is based on multispectral threshold technique applied to each pixel of the image. Figure 2-3
below shows an example of cloud mask as developed by SAFNWC and accessed from EUMETSAT
through EUMETCast.
Figure 2-3: MSG cloud mask for 25th December 2006 at 12:00 UTC (EUMETSAT, 2006)
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The cloud mask algorithm developed here follow series of tests, the first being to identify pixels
contaminated by clouds or snow/ice and applied to land or sea which depend on the solar illumination
and on the viewing angles as defined in the table 2-2 with the tests presented in tables 2-3 and 2-4.
Most of the thresholds are determined from satellite-dependent look-up tables and NWP forecast
fields data and also from ancillary data (elevation and climatological data). Some thresholds are
computed at a spatial resolution defined by the user and others are empirical constants.
The second process is applied to all pixels, even already classified cloud-free or contaminated pixels,
to identify dust clouds and volcanic ash clouds. Spatial filtering is applied to reclassify isolated pixels
having a class type different from their neighbours.
Table 2-2: Definition of illumination conditions (SAFNWC); solar elevation is in degrees
Night-time Twilight Day-time Sunglint
Solar elevation<-3 -3<Solar elevation<10 10<Solar elevation Solar elevation>15
Table 2-3: Test sequence over land (SAFNWC)
Day-time Twilight Night-time
Snow detection Snow detection T10.8
T10.8 T10.8 T10.8-T12.0
R0.6 R0.6 T8.7-T10.8
T10.8-T12.0 T10.8-T12.0 T10.8-T8.7
T8.7-T10.8 T8.7-T10.8 T10.8-T3.9
T10.8-T3.9 T10.8-T8.7 T3.9-T10.8
T3.9-T10.8 T10.8-T3.9 Local spatial texture
Local spatial texture T3.9-T10.8 T8.7-T3.9
Local spatial texture
T8.7-T3.9
Table 2-4: Test sequence over sea (SAFNWC)
Day-time Sunglint Twilight Night-time
Ice detection Ice detection Ice detection SST
SST SST SST T10.8-T12.0
R0.8(R0.6) T10.8-T12.0 R(0.8)R0.6 T8.7-T10.8
R1.6 T8.7-T10.8 T10.8-T12.0 T10.8-T3.9
T10.8-T12.0 Local spatial texture T8.7-T10.8 T12.0-T3.9
T8.7-T10.8 R0.8(R0.6) T10.8-T8.7 T3.9-T10.8
T10.8-T3.9 T10.8-T3.9 T10.8-T3.9 Local spatial texture
T3.9-T10.8 Low clouds in sunglint T3.9-T10.8
Local spatial texture Local spatial texture
T8.7-T3.9
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Some of the notable tests are such as those using IR10.8 and IR12.0 (here in the table indicated as
T10.8 and T12.0 respectively) in which over the sea a pixel is flagged as cloudy when its estimated
sea surface temperature (SST) value is lower than a monthly climatological SST value by 4K. The sea
surface temperatures are estimated from T10.8 and T12.0 brightness temperatures using a non-linear
split window algorithm (Le Borgne et al., 2003). If this test is not applied, IR10.8 is used in which
case the threshold is determined from surface temperatures forecast by using Numerical Weather
Prediction (NWP) model. This test is applied over both the land and the sea surfaces. More
importantly is the fact that the threshold is derived from a global Pathfinder night-time bulk SST
climatology covering a period of 10 years and available at a 1/9th degree (approximately 12 km)
horizontal resolution.
Over land, IR8.7 together with IR10.8 is used in which the difference (IR10.8-IR8.7) should be
greater than 3.5+0.3/cos (θsat), during night-time or in case of low sun elevation e.g. at twilight, for
any pixel to be flagged cloud-contaminated. θsat is the satellite zenith angle. Usually, low clouds are
characterized at night-time by high IR10.8-IR3.9 brightness temperature differences, which allows
their identification over land (Derrien and Le Gléau, 2005). This detection may be less efficient at
large viewing angles hence the need to use a different channel (here IR8.7). An empirical test
(3.5+0.3/cos (θsat)) has been developed based on the observation that decrease of IR8.7- IR10.8 with
the satellite zenith angle is much stronger for low clouds than for vegetated areas. However, during
day-time the empirical test threshold is greater (-4.5-1.5*(1/cos (θsat)-1); where 1/cos (θsat) is the
secant of the satellite zenith angle, for any pixel to be flagged cloudy.
Most of the other test thresholds provided in tables 2-3 and 2-4 are computed from simulation of the
surface (ocean, land or snow) top of atmosphere reflectance (for the visible and near infrared bands)
by adding an offset and a correction factor. Top of atmosphere reflectance is simulated as:
( ))210 *1/* surfsurftoa RaRaaR −+= (2.1)
Where: a0, a1, and a2 are coefficients computed from satellite and solar angles, water vapour
and ozone content using look-up tables.
Rsurf is the land, ocean or snow surface reflectance.
Offsets of various percentages and correction factors are added to the above expression. However,
they are not described further in the literature. Hence this makes this method difficult to apply.
The dynamic thresholds applied to thermal bands differences are obtained by interpolation into look-
up tables using the satellite zenith angles and the NWP forecast using radiative transfer models. It is
no doubt that the tests are many and therefore require special software to handle. In addition, many
African National Meteorological and Hydrological Services (NMHSs) have no capability to handle
numerical weather prediction (NWP) model forecasts using radiative transfer models.
2.3.2. Météo-France (Ocean and Sea Ice SAF) Cloud Mask
The developments of the Ocean and Sea Ice Satellite Application Facility (O&SI-SAF) Sea surface
temperature algorithms of Météo-France for the determination of Atlantic sea surface temperature
require cloud masking. The SST products have three components namely; GOES-East, MSG and
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NOAA-HL derived SSTs (Météo-France, 2005a). In all these components cloud mask algorithm
applied is the same and is based on multispectral thresholding technique. Specific adaptation to
marine conditions was introduced in the development of the algorithm. These conditions mainly
consider temporal stability of SST and climatology. An example of combination of cloud-free GOES-
East and MSG satellites SST product in MSG georeference for 25th December 2006 at 13:00 UTC is
provided in figure 2-4 below.
Figure 2-4: GOES-East and MSG satellites SST products (in ˚C) for 25th Dec 2006 at 13:00 UTC
Temporal stability of SST was suggested by (Wu et al., 1999) and is applied in Ocean and Sea Ice
SAF under the following form: for a clear sky pixel, channel 11µm (IR10.8) temperatures at time H
(T11H) are compared to the maximum value of the corresponding temperatures at time H-30 minutes
(T11H1) and time H+30 minutes (T11H2). If T11H-Max (T11H1, T11H2) <Threshold, the pixel is
considered as cloudy (Météo-France, 2005a). Threshold set here as suggested by (Wu et al., 1999) is -
0.5K. The process tries to address change of temperature over a pixel. A negative change implies
setting in of cloud in that pixel which is in most cases colder that the pixel temperature within a time
range (1 hour). This process implies that all pixels’ real time state will not be determined since it
requires the future (next 30 minutes) state. Furthermore, the comparison process may take some time.
In considering climatology, the climatologic minimum temperature at any time of the year in question
is compared with calculated SST value. Too low SST is indicative of cloud contamination and too low
threshold depends on the distance of the considered pixel to the pre-calculated cloud mask and the
location of the pixel with respect to the coast. Here it is assumed that near a cloud, too cold
temperatures are more suspect and the control of the calculated SST against climatology should be
more severe. The scheme works as follows:
If Tsmin-Ts > ∆t, the pixel is considered as cloudy
where: Tsmin is the climatologic minimum temperature
Ts is the calculated SST
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∆t is the threshold
SST is calculated from a non-linear algorithm in the form of expression given below:
CorrSCCTTTBSBBTSAAT guessS +++−++++= 1021010 )0.128.10)((8.10)( (2.2)
where: A0, A1, B0, B1, B2, C0, and C1 are constants for which in case of MSG are: 0.98826, 0, 0,
1.18116, 0.07293, 1.10718, and 0 respectively
S = sec(θ)-1, with θ: satellite zenith angle
Tguess is the climatological SST
Corr is correction factor and is 0.2 for MSG
Climatologic minimum temperature, Tsmin is derived from the Pathfinder archive (AVHRR data from
1985 to 1995). The climatology has been made on a decadal (10 day) basis and includes minimum and
mean values re-mapped over the MSG disk at same resolution as the thermal infrared bands in MSG.
Various thresholds used depend on the position of the pixel with respect to the coast hence it applies
only over the sea. The method appears less complicated as compared to the previous one and therefore
easy for implementation.
2.3.3. Meteosat VIS-IR and NOAA-A/TOVS Image fusion Cloud Mask
(Casanova et al., 2005) developed an automatic method of cloud classification for direct application
in civil aviation. Here visible and infrared channels of Meteosat satellite were used alongside data
provided by the A/TOVS (Advanced/Tiros-N Operational Vertical Sounder) onboard NOAA polar
satellites. Different spectral techniques were used for different purposes. In their study, an automatic
method of cloud classification which provided, in real time, the cloud cover over civil airports on the
Iberian Peninsula was developed emphasizing on rain clouds.
The method consisted of a series of algorithms based on the physical properties of cloud surfaces and
thermodynamic state of the atmosphere. TIP-data included in the telemetry of the high resolution
picture transmission (HRPT) satellites were taken and processed through International TOVS
Processing Package (ITPP) or International ATOVS Processing Package (IAPP) software, depending
on whether the datum type was TOVS or A/TOVS, in order to transform the high-resolution
interferometer sounder (HIRS) and advanced/microwave sound unit (A/MSU) sensors’ radiances into
atmospheric data.
In this method, albedo classification was performed which revealed that most clouds were good
reflectors since it depends first on their thickness and to some extent on the nature of the cloud
particles. To avoid sunglint contamination, illumination geometric conditions reflectance thresholds
were set. From here reflectivity image was obtained which was further reclassified according to
various categories based on surface type. Threshold tests were performed by comparing historic data
of mean temperatures at ground level to the calculated brightness temperature values. This allowed
detection of non-cloudy pixels which were to be removed before further processing in order to obtain
the linear relationship between height and cloud top temperature. This was done by applying the
temperature value to the equation obtained through the geopotential and temperature images provided
by the A/TOVS data calculated at different pressure levels.
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This method can be seen to use thermodynamic sounding of the atmosphere which in many cases is
not available at many meteorological weather stations. Many African National Hydrological Services
are also not able to handle the atmospheric sounding due to the cost involved.
2.3.4. KNMI Cloud Mask Algorithm
The Royal Netherlands Meteorological Institute (KNMI) has developed an algorithm for cloud
detection and characterization called MetClock (METeosat CLOud Characterisation KNMI).
According to (Valk et al., 1998), MetClock algorithm comprises two threshold tests to perform cloud
detection on the basis of Meteosat IR data, the relative and absolute infrared tests. A cloudy pixel is
determined by comparing Meteosat apparent brightness temperatures with the earth surface
temperatures. The relative infrared test uses images of different observation times to compare changes
in temperature at the earth surface with changes in temperature of a pixel. A pixel is classified as
cloudy when the change in pixel temperature exceeds the change of the earth surface temperature with
a certain threshold.
On the other hand absolute infrared test uses a single image to directly compare pixel temperatures
with the earth surface temperatures. Here again a pixel is classified as cloudy when it exceeds a
certain threshold. From these two tests it is clear that the results depend strongly on the accuracy of
the surface temperature maps. The surface temperatures are provided by Numerical Weather
Prediction (NWP) model, the High Resolution Limited Area Model (HIRLAM).
The relative test group consists of five tests, thresholding on the IR imagery and VIS imagery between
various time difference including previous imageries and also future imageries either in hours or days.
This is the shortcoming of this particular cloud classification method since it takes into account
forecast environment which may not be always correct. Besides, surface temperature over complex
terrain and high mountains may not be accurate and therefore the method may not be very much
applicable in such areas. The algorithm was developed for the first series of Meteosat satellites
(Meteosat 1-7) and can be as well applied to MSG satellites.
2.3.5. KLAROS Cloud Mask Algorithm
In their study (Dlhopolsky and Feijt, 2001) developed KNMI Local implementation of APOLLO
retrievals in an Operational System (KLAROS) algorithm for processing MSG data. AVHRR data
was used as prototype data set with which to produce cloud products expected to be derived with
MSG.
KLAROS is more or less the same as MetClock. However this method was aimed at improving cloud
detection by using a radiative transfer model to help link radiances from the different wavelengths and
produce physically meaningful variables. The method was designed to work with use of thresholds
defined in databases. The temperature database is derived from the HIRLAM NWP model while
reflectivity database was created from two years of NOAA AVHRR data clear skies and verified with
synoptic observations.
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It is further stated that KLAROS consists of a set of programs for cloud detection and cloud property
retrieval. The two modes of operation are one in C tool which does the data processing and the other
is user interface written with the Interactive Data Language (IDL).
2.3.6. APOLLO Cloud Mask
AVHRR Processing scheme Over cLouds, Land and Ocean (APOLLO) was designed to make use of
all five spectral channels during day-time and to discretize all AVHRR data into four different groups
called cloud-free, fully cloudy, partially cloudy, and snow/ice, before deriving physical properties
(Saunders and Kriebel, 1988). Within APOLLO, clouds are discretized into three layers according to
their top temperature (Kriebel et al., 2003). The layer boundaries are set to 700hPa and 400hPa and
the associated temperatures are derived from standard atmospheres.
Each cloudy pixel is checked to see whether it is thick or thin cloud, depending on its channel 4 and 5
temperatures and, during day-time, its channel 1 and 2 reflectances. Thin clouds are taken as ice
clouds, i.e. cirrus, whereas thick clouds are treated as water clouds. APOLLO is designed to process
AVHRR HRPT (High Resolution Picture Transmission) data as well as Local Area Coverage (LAC)
and Global Area Coverage (GAC) data. Those pixels in which the solar elevation is more than 5°
above the horizon are processed by means of the day-time algorithm whereas all others are processed
by the night-time algorithm.
APOLLO uses five threshold tests applied to each pixel and this allows establishing the group of
cloud-free and contaminated pixels. These tests are based on AVHRR channels 1, 2, 4 and 5 and rely
on simple physical principles. Every pixel which is brighter than a threshold in the solar channels or
colder than another threshold in the thermal channels is called cloudy. The use of physical parameters
and self-adjusting thresholds minimizes the influence of differences between the instruments aboard
different satellites. However, since AVHRR is polar-orbiting it is not to establish meaningful time
series of cloud products. Thus the thresholds needs to be carefully selected in order to establish
whether a pixel is cloudy or is partially cloudy or is cloud-free.
2.3.7. GHCC Cloud Mask
The Global Hydrology and Climate Centre (GHCC) in Huntsville, Albama receives GOES-East and
West satellite data in real-time from their ground stations and produces a number of products from the
Imager and Sounder in support of research and operational activities. Cloud detection method used
here is bi-spectral spatial coherence (BSC) which uses two spatial tests and one spectral threshold to
identify clouds in the GOES Imager or Sounder imagery (Jedlovec and Laws, 2003). The performance
of the BSC method is adequate during the day; however it performs poorly near sunrise/sunset and at
night.
Bi-spectral Threshold and Height (BTH) which is built on BSC uses spatially and temporally varying
thresholds. This method also provides cloud top pressure information with the cloud mask. The
underlying principle in this cloud detection method with GOES imagery is that the emissivity
difference of clouds at 10.7µm and 3.9µm varies from that of the surface (land or ocean) and can be
detected from channel brightness temperature differences. (Jedlovec and Laws, 2003) noted that while
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emissivity of clouds at 3.9µm is considerably less than at 10.7µm, reflected solar radiation at 3.9µm
makes effective brightness temperatures (sum of emission and reflective components) quite large.
The key to cloud detection in the BTH technique is the use of multispectral channel differences to
contrast clear and cloudy regions. The 10.7µm and 3.9µm channels on the Imager and similar
channels on the Sounder are used to produce an hourly difference image (longwave minus shortwave)
for this purpose (Jedlovec and Laws, 2003). Two composite images are created for each hour which
represent the smallest negative and smallest positive difference image values (values closest to zero)
from the preceding 20 day period (for each time). These composite images serve to provide spatially
and temporally varying thresholds for the BTH method. An additional 20 day composite image is
generated for each hour using the warmest longwave (10.7µm) brightness temperature for each
location from the 20 day period. This composite image is assumed to represent a warm cloud-free
thermal image for each time period.
BTH method uses the images generated as enumerated above in a four step cloud detection procedure.
These include testing adjacent pixels, one-dimensional spatial variability (which fills-in between the
cloud edges), minimum difference (which compares the current difference image value to the
composite images), and Infrared threshold (which uses an hourly 20-day composite of the warmest
10.7µm channel values at each pixel location.
It is no doubt that this method requires high memory space for storing the images generated. It also
implies that a lot of iterations have to be done every time a comparison and composite images are to
be generated.
2.3.8. AFWA Cloud Mask Algorithm
Kidder et al., (2005) developed various Meteosat Second Generation (MSG-1) cloud mask algorithms
for implementation at the United States Air Force Weather Agency (AFWA). These algorithms are
named; cloud mask, nocturnal cloud mask, daytime cirrus, nocturnal thin cirrus, precipitating clouds,
and multi-channel skin temperature.
Cloud mask algorithm uses difference-from-background technique by constructing 10-day infrared
background for each hour of the day. The process assumes that in 10 days each pixel is observed to be
cloud-free at least once. This method exploits the tendency of clouds being colder than the underlying
surface. Pixels whose radiance is less than the background radiance by more than a threshold value
are flagged as cloudy. Here 8.7µm channel is used in constructing the 10 days background image.
Nocturnal cloud mask test uses the brightness temperature at 10.8µm and the albedo at 3.9µm to
detect ice clouds, liquid water clouds, and clear scenes. 8.7µm channel data are used to screen desert
pixels. Various empirical thresholds are used in the test. Albedo and brightness temperature
background database contains the 3.9µm albedo and 10.8µm brightness temperature data observed
over MSG-1 pixel each day for the previous 10-day period.
Day-time cirrus test for MSG-1 data utilizes three reflective channels, 0.6µm, 0.8µm, and 1.6µm. The
measured radiances are converted to albedos (0 to 1) by dividing the radiances by the solar irradiance
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and by the cosine of the solar zenith angle, then multiplying by phi. This test utilizes the fact that
liquid water clouds are highly reflective at all three wavelengths and thus will appear white. Ice
clouds (and snow on the ground) are highly reflective at 0.6µm and 0.8µm, but poorly reflective at
1.6µm and therefore they appear cyan in colour. When the albedos are represented in the RGB colour
cube, cirrus is having a cyan colour due to ice particles.
Nocturnal thin cirrus test uses albedo at 3.9µm, which is calculated from measured radiances at 3.9µm
and 10.8µm (Kidder et al., 2005). Radiation from below the cloud leaks through thin cirrus, which
results in a negative albedo. They indicated that nothing else results in a negative 3.9µm albedo, hence
this is a very sensitive test for thin cirrus at night.
Precipitating clouds test uses the brightness temperature difference between the 6.2µm water vapour
channel and the 10.8µm window infrared channel to detect high, thick clouds, which are likely to be
precipitating. As clouds are usually formed within troposphere, the maximum water vapour content is
also expected within this level. Casonova et al., (2005) indicated that in an adiabatic atmosphere, the
relation between the height and temperature within troposphere is linear. This linear relation can be
obtained by use of A/TOVS data for pressure levels between 1000 hpa (approximately sea level) and
300 hpa (approximately tropopause). It is for this reason that WV06.2 is appropriate to use with
IR10.8 in order to extract the most probable raining clouds.
Kidder et al., (2005) indicated that at 6.2µm the atmosphere is opaque due to water vapour absorption
and that low clouds are not sensed at 6.2µm. Only deep clouds penetrate the water vapour to be
sensed at both 6.2µm and 10.8µm, and when this happens then the brightness temperature difference
at these two wavelengths becomes small (Kidder et al., 2005). Empirically determined threshold
temperature difference of 11K is used to flag out cloudy pixels, (i.e. if WV06.2-IR10.8 < 11K, then
the pixel is rain cloud).
Multi-channel skin temperature procedure employs two channels (10.8µm and 12.0µm). Both 10.8µm
and 12.0µm radiances are affected by water vapour in the column between the surface and the
satellite, but 10.8µm is less affected than 12.0µm. The difference between the brightness temperatures
at 10.8µm and 12.0µm can be used to correct the 10.8µm brightness temperature for water vapour
absorption to yield an estimate of the skin temperature, that is, the brightness temperature which
would be observed if there was no water vapour in the atmosphere (Jedlovec and Laws, 2003). The
temperature retrieved here is that of the surface only and not the air temperature. However, the
method applies to clear-sky pixels only.
Most of the above cloud mask methods have not revealed the actual threshold values used. However,
under the strength of the arguments in the theories used in these methods to develop the mask
algorithm, a simple cloud mask (SCM) could be attempted which could further be used in other
applications such as rainfall estimation, weather forecasting, water and energy balance studies, among
others.
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2.4. Precipitation Processes
Precipitation is water in some form, falling out of the air, and settling on the surface of the earth.
Precipitation occurs due to condensation in the atmosphere and not condensation that occurs at the
surface such as dew.
The major two models of precipitation formation are: collision-coalescence and ice crystal models.
An important distinction between the two processes is the temperature of the cloud. Warm clouds are
the ones whose mass lies above freezing level while cold clouds primarily exist where the temperature
is below freezing level. The collision-coalescence model applies to warm clouds that form in the
tropics whereas ice crystal model applies to the process of precipitation in the mid and high latitudes.
For precipitation to form under collision-coalescence model there needs to be a variety of different
size condensation nuclei. Large condensation nuclei will create large water droplets while smaller
condensation nuclei create small ones. In the ice crystal model, cloud water exists in liquid form even
though the temperatures are cold enough to freeze water. Water has a temperature below freezing but
still in liquid state i.e. super-cooled water.
The following section discusses various methods of estimating rainfall (one of the major form of
precipitation) from space by use of satellites. Some of these methods attempt to address rain
formation processes as explained in this section.
2.5. Satellite Rainfall Estimation
The measurement of the surface precipitation is very important to studies of the hydrological cycle,
water management planning, flash flood identification, input to hydrological and agricultural models,
verification of weather modification experiments and the study of convective systems (Kamarianakis
et al., 2006). Rainfall affects lives and economies of a majority of the earth’s population. Heavy rain
systems are crucial to sustaining the livelihood of many countries. Excess rainfall can cause floods,
landslides and loss of property.
Rainfall is among the atmospheric parameters, one of the most difficult to measure because of its high
temporal and spatial variability and discontinuity. Moreover the coverage of precipitation
measurements by ground conventional means (rain gauge networks or weather radars) is much less
than adequate especially in the African continent.
With the advent of meteorological satellites, improved identification and quantification of
precipitation at time scales consistent with the nature and development of cloud has been realised
(Levizzani et al., 2002). Meteorological satellites expand the coverage and time span of conventional
ground-based rainfall data for a number of applications, above all hydrology and weather forecasting.
The primary scope of satellite rainfall monitoring is to provide information on rainfall occurrence,
amount and distribution over the globe for meteorology at all scales, climatology, hydrology, and
environmental sciences (Levizzani et al., 2002). The uneven distribution of rain gauges and weather
radars over most part in the world has called for this new technology of satellite rainfall measurement.
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It is also a well know fact that precipitation is one of the most variable quantity in both space and time
especially within the tropics.
Geostationary weather satellite visible (VIS) and infrared (IR) imagers provide the rapid temporal
update cycle needed to capture the growth and decay of precipitating clouds. The swath widths of
satellites in tropical orbit such as the Tropical Rainfall Measuring Mission (TRMM) and of sensors in
polar orbits like the Special Sensor Microwave Imager (SSM/I) series leave substantial gaps all over
the globe (Levizzani et al., 2002). The quantitative rainfall determination from a variety of
precipitating systems differ both dynamically and microphysically and this prompts for non-unique
solutions based on physics of precipitation formation processes.
Based on a number of earlier studies, (Levizzani et al., 2002) reviewed several satellite-based rainfall
estimation methods. In this section a few of these satellite-based rainfall estimation methods that use
thermal infrared are briefly explained. They revealed the four main categories of cloud classification
as: cloud-indexing, bi-spectral, life-history, and cloud model-based. Each of these categories stresses a
particular aspect of sensing cloud physics properties using satellite imagery and in final rainfall
estimation.
2.5.1. Cloud-Indexing Methods
According to (Kidder and Haar, 1995), cloud-indexing is the oldest precipitation estimation technique
which assumes that it is fairly easy to identify cloud types in satellite imagery. This method assigns a
rain rate to each cloud type. The rain at a particular location or in a particular area can be written as:
i
i
i frR ∑= , (2.2)
Where ri is the rain rate assigned to cloud type i, and fi is the fraction of time that the point is covered
with (or fraction of the area covered by) cloud type i.
This method lacks the validity of assigning a constant rain rate to a particular cloud type. Depending
on the cloud formation processes, rain rate may vary significantly even for a particular type of cloud.
2.5.2. Bi-spectral Methods
Bi-spectral methods are based on the very simple, although not always true, relationship between cold
and bright clouds and high probability of precipitation, which is characteristic of cumulonimbus
(Levizzani et al., 2002). Lower probabilities are associated to cold but dull clouds (thin cirrus) or
bright but warm (stratus). Generally cirrus clouds are cold but do not produce as much precipitation as
some warmer clouds.
Techniques in this category are based on cloud classification and using either radar derived rainfall or
good network of ground stations as training data. From here rainfall estimation over a given array of
pixels can be derived.
The underlying assumption here is that all rain bearing clouds are successfully classified, which is not
always the case.
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2.5.3. Life-history Methods
These are the methods mainly in the family of techniques that specifically require geostationary
satellites images. They rely upon a detailed analysis of the clouds life cycle, which is particularly
relevant for convective clouds (Levizzani et al., 2002).
One of the most famous techniques which use this method is Tropical Applications of Meteorology
using SATellite (TAMSAT) of the Reading University, United Kingdom. The assumption inherent in
the TAMSAT procedure is that the relationship between the quantity of rainfall and the cold cloud
duration (CCD) is linear, provided there is adequate averaging of data either in space and time. The
averaging procedure reduces the false alarms that CCD may cause especially over a short time period
or over a very small area which is associated with convective clouds.
(Dugdale and Milford, 1985) developed the concept of cold cloud duration (CCD), using the thermal
channel of Meteosat, to generate time series of cloud temperature for tropical altitudes, where most
rainfall comes from convective activities. They suggested that the duration above a certain threshold
temperature value is representative of the amount of rain that is generated.
However, according to (Grimes et al., 1999) the basic assumptions in this method are:
1) Rainfall is predominantly convective in origin and that the raining clouds can be
identified as those with cloud top temperatures below a threshold temperature (T),
2) The number of hours for which a given pixel is colder than T (the CCD) is linearly
related to the rainfall over the same time period, that is:
eDaaRs ++= 10 (2.3)
where: Rs is the rainfall over the pixel,
D is the CCD over the pixel,
e is the error with zero mean, E[e] =0, and homogeneous variance
Var[e]=ε2
3) The threshold temperature, T and the parameters a0, a1, can be estimated for a given
region and a given time of year by the analysis of historic data for that region and time of
year, that is:
DaaRs 10′+′=′ (2.4)
where: Rs, a0, and a1 are estimated values.
Rs, a0, and a1 are calculated for each month for a number of empirically determined calibration zones.
This method has proved to be successful in tropical regions especially for convective clouds occurring
in the region of Inter-tropical Convergence Zone (ITCZ) in which case the first two assumptions are
reasonable. However, due to inter-annual variability in rainfall-CCD relationship, there could be over-
or underestimation of rainfall for a particular month locality if fixed calibration is applied.
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2.5.4. Cloud Model-based Techniques
Cloud model-based techniques aim at introducing the cloud physics into the retrieval process for
quantitative improvement deriving from the overall better physical description of the rain formation
processes (Levizzani et al., 2002). (Adler and Mack, 1984) derived a one-dimensional cloud model by
first identifying locations of convective cells and then assigning rain parameters. Associated anvil
stratiform rainfall area is identified by a threshold brightness temperature, the value of which is
calculated from the satellite data. The model calculates maximum rain rate and maximum volume rain
rate from a sequence of model runs as a function of maximum cloud height or minimum cloud model
temperature. The use of the one-dimensional cloud model to account for ambient temperature,
moisture and shear conditions provides a stronger physical (less empirical) basis for the cloud height -
rain relationships.
(Adler and Negri, 1988) utilized data from Geostationary Operational Environmental Satellite
(GOES) infrared (10.5-12.6µm) channel, in 30 minutes, for an area in the southern Florida for a one-
dimensional cloud model relating cloud top temperature to rain rate and rain area in the Convective
Stratiform Technique (CST). The method here followed a few steps, first of which is to identify the
candidate thunderstorm or regions of enhanced convection by searching for the minimum in the
GOES temperature Tb array.
The second step involves eliminating local minima temperature that represent thin, non-precipitating
cirrus by calculating slope parameter for each minimum temperature Tmin as:
min61 TTS −= −
−
(2.2)
where, 61−
−
T is the average temperature of the six closest pixels. If the Tmin is located at (i,j),
6/)( 1,1,,2,1,1,261 −+++−−−
−
+++++= jijijijijiji TTTTTTT (2.3)
Note that due to pixel offset along the scan line which is approximately half as large as that across the
scan, 6 pixels (highlighted in figure 2-5) are taken as the closest pixels to the one under consideration.
i , j
Figure 2-5: An example of pixel array under consideration with Tmin at (i,j)
After this step empirical discrimination of thin cirrus, from active convection, in the
temperature/slope plane using radar and visible imagery data is derived (Adler and Negri, 1988). Once
this is done, correction for the field of view between the GOES field of 8km and that of the cloud
model (approximately 1km in horizontal dimension) as used by (Adler and Mack, 1984). Rain
parameters are then assigned to the feature based on one-dimensional cloud model and thereafter a
threshold temperature is used to identify the anvil stratiform region. This threshold is expected to
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coincide with the relatively thick portion of mature anvils hence modal Tb in the frequency
distribution of anvil Tb is used as anvil background temperature.
The above four methods/techniques have been widely applied in satellite rainfall estimation with
different scientists endeavouring to optimally estimate rainfall over different space and time ranges.
The following section briefly explains the blending techniques used to estimate rainfall over a given
area.
2.5.5. Blending Techniques
With the advent of passive microwave measurements, several VIS/IR techniques have been re-
examined and integration sought that could help adjusting some of the well known problems of the
top–down approach of these methods, which generally derive precipitation only from cloud top
information (Levizzani et al., 2002). These methods have suggested combining the observations
delivered by satellite instruments of different type to improve averaged rainfall estimation by using
multi-source data. Combining IR data from geosynchronous satellites attempt to take advantage of
both IR and MW techniques. They benefit from the fact that there is excellent time and space
coverage of IR images and from the direct connection of the MW observations with precipitation.
However, these techniques require some precautions as (Turk et al., 2003) indicated that raining and
idealized non-raining conditions as observed by low orbiting earth may cause discrepancies as a result
of viewing geometries (see figure 2-6). This depends upon the three-dimensional structure of the
cloud and the azimuthal direction that the sensor views it. Moreover, the timing and foot print offsets
usually cause significant difference between geostationary satellites and polar orbiting satellites.
Figure 2-6: Some factors influencing the differences between space- and time-collocated TMI and SSMI
observations under idealized precipitating cloud conditions (Source: (Turk et al., 2003))
These methods often use statistical integration of the satellite IR and MW data. The choice of which
variables to match in order to provide the final product may rest on one part the extent of accuracy
required and on another the processing time. (Marzano et al., 2005) indicated a possibility of direct
combination of microwave brightness temperatures and thermal infrared radiances, in which the
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advantage is the exploitation of the observable information without any post-processing and the
disadvantage of space-time collocation matching.
Maathuis et al., (2006) applied blending technique using TRMM derived rainfall intensity to calibrate
MSG image in which a relation between thermal infrared observations and the passive microwave
observations was derived. In order to obtain good correlation between these two variables, as well as
to solve collocation problems, they used equal temperature classes to average rainfall intensities. The
regression function obtained here was applied to MSG images for regionalization of rainfall intensity
over eastern part of Africa.
Statistical methods in these techniques are significant and are applied to empirically-trained retrieval
algorithms in order to estimate rainfall to a reasonable accuracy. However, the best approach would
be based on physically-based retrieval algorithms which, on the other hand, would need a
climatological and microphysical tuning. This again would resort to approaches whose aim would be
cumulative estimates but not instantaneous estimates.
Besides, most of the above methods consider rainfall events and not a specific type of cloud. Different
clouds and different stages of development (e.g. for convective clouds) may have different cloud
height- rainfall intensity/amount relationship. This calls for an approach that may address the problem
of comparing rainfall and cloud height of different clouds that are at different stages of their
development.
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks
(PERSIAN) Cloud Classification System (CCS) as described by (Hong et al., 2004a) addressed the
problem of comparing different type of clouds by extracting cloud features from infrared
geostationary satellite imagery. The PERSIAN algorithm fits the pixel brightness temperature and its
neighbour temperature textures, in terms of means and standard deviations, to the calculated pixel rain
rates based on an Artificial Neural Networks (ANN). The general approach in the algorithm is as
provided in figure 2-7 below.
Figure 2-7: The PERSIAN CCS model structure (source:(Hong et al., 2004a))
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Firstly images are pre-processed through cloud segmentation procedure as in (a). Several inputs of
feature extraction of the cloud patches are applied as in (b). Once cloud patches are identified cloud
classification follows clustering them accordingly as in (c). The final step is to develop non-linear
temperature and rainfall fitting for all classified cloud clusters as in (c). The parameters of the
temperature – rainfall curves are calibrated based on rain estimates from sources such as Radar
networks over a region under study.
This method offers probably the best idea in comparing cloud properties and rainfall intensity or
rainfall amount. However, the enormous data requirement in this algorithm, which is unavailable in
many regions, renders it inapplicable in many regions. Consequently a simple satellite rainfall
estimation method is vital for regions with low passive microwave datasets. Despite low reliability
results that may be obtained in using few satellite cloud data, rainfall estimation can be derived as first
approximation for users such as hydrologists or general water management resource organisations.
2.5.5.1. EUMETSAT Multi-sensor Precipitation Estimate (MPE)
The EUMETSAT multi-sensor precipitation estimate (MPE) has been developed in order to derive
instantaneous rainfall intensities from MSG. The method is based on the blending of brightness
temperatures of the MSG infrared channels with rainfall intensities from Special Sensor
Microwave/Imager (SSM/I) on the United States Defence Meteorological Satellite Program (DMSP)
satellites (Heinemann, 2003). The basic assumption of the Multi-sensor Precipitation Estimate (MPE)
method is that colder clouds are more likely to produce precipitation than warmer clouds. Here
Heinemann, (2003) pointed out that the relationship between the cloud top temperature and the
surface rainfall intensity is non-linear and that it depends strongly on the current weather situation.
Temporally and spatially co-registered SSM/I and MSG measurements are used to derive look-up
tables which describe rainfall intensities as a function of the MSG infrared brightness temperature.
The look-up tables are applied to MSG images in order to derive rainfall intensities in full spatial and
temporal resolution. This method is indicated to efficiently estimate the spatial distribution and
strength of convective precipitation over not only large scale tropical convection but also small scale
convective processes and cold fronts. It is however not suitable for estimating precipitation from
warm fronts and also orographically induced precipitation which is usually detected but miss-located
to great distances, sometimes upto 100km (Heinemann, 2003).
These products are available in http://oiswww.eumetsat.org/SDDI/html/product_description.html
(EUMETSAT, 2007) and can be downloaded in GRIB2 format. MPE products can be imported into
any geographic information systems (GIS) packages such as ILWIS by using windows based GRIB2
import package which can be obtained from
ftp://ftp.cpc.ncep.noaa.gov/wd51we/wgrib2/Windows_XP/ (NOAA-NWS-CPC, 2005). The respective
rainfall intensities can then be viewed in ILWIS. GRIB viewer from SatSignal software and available
in http://www.david-taylor.pwp.blueyonder.co.uk/software/grib-viewer.htm (David, 2006) can as well
be used to view the amounts at a desired location.
The following chapter outlines materials and methods used in this study with detailed information on
data acquisition required for cloud masking and also for rainfall comparison as well as rainfall
estimation.
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3. Materials and Methods
3.1. Data Acquisition
The study attempted to use various data whose source was either straightforward to obtain or needed
pre-processing. The general approach in developing the cloud mask is as given in figure 3-1. The
proceeding sections explain how each of the retrieval and computation processes was done with
ILWIS scripts in Appendix A.
Figure 3-1: Flow chart for simple cloud mask (SCM) and cloud height/type (SCH/T) retrieval
3.1.1. MSG Satellite Data
The Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard MSG satellite provides
data to EUMETSAT at Darmstadt (Germany) which is processed and then uplinked to HOTBIRD-6 in
wavelet compressed format (Gieske et al., 2004). The images are received and archived at ITC in
compressed form on external drives which are linked to the ITC network and hence accessed through
ordinary personal computers. The image geocoding and radiometric calibration coefficients are
supplied in so called EPI and PRO files. The data is not atmospherically corrected. Therefore direct
ground observation(s) can only be related to the satellite observation(s) (at the required resolution)
after atmospheric correction of the images. In this study this step is not necessary since the focus is on
Not ok Ok
Dew point temp
calculation and set
temp threshold(s)
Merge SST and LST
Time series (K)
Perform Cloud
Mask
Retrieve data
from MSG
online archive
Retrieve land
Surface
Temperature
(LST) (°C)
Retrieve Sea
Surface
Temperature
(SST) (°C)
Classify into different
height classes
TOA temperature
(IR_039, IR_108,
IR_120)
VIS Range
(VIS006, VIS008,
VIS016) – Day-
time only
Cloud mask
algorithm
Visualize the
cloud mask on
Colour
composite
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clouds which are the main atmospheric parameters aimed at removing from satellite images for direct
ground observation(s) relations.
The retrieval of MSG data is straightforward using import utilities developed. External batch files
were created using the MSG data Retriever software available at ITC. For more details about the
software refer to (Maathuis et al., 2005). Figure 3-2 shows MSG data Retriever window with the
command line indicating all parameters that are to be retrieved from MSG image.
Figure 3-2: MSG Data Retriever window (Courtesy of ITC)
The executing commands are saved as a text file and therefore any time a different image is required
the changes are only done using a text editor and saved as a batch file in ILWIS software. These batch
files are provided as Appendix B. The software has been used intensively in this study as the MSG
geometric model is implemented. In this study, only bands 4, 9, and 10 (3.90µm, 10.8µm, and 12.0µm
respectively) were used in the cloud masking process. Visible bands 1, 2, and 3 (0.06µm, 0.08µm, and
1.60µm respectively) were only for visualization in order to ensure optimal (visual) cloud mask
validation especially during the day.
Examples of raw images of band 9 (10.8µm) and visible bands 1, 2, and 3 (0.06µm, 0.08µm, and
1.60µm respectively), with the visible bands viewed as false colour composite, are provided in figure
3-3.
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Figure 3-3: False colour composite (bands 1, 2, and 3 –in BGR) (left) and Band 9 (10.8µm) (right) on
19/12/2006 at 12:00 UTC
On the left figure cyan areas are cloud patches and dark areas are water bodies. Right figure with
pseudo colour representation shows colder areas in deep blue to green while warmer areas appear
orange to red in colour.
3.1.2. Climatological Data
Climatological data required in this study were minimum, maximum, and mean land surface
temperature as well as sea surface temperature. Minimum, maximum, and mean land surface
temperature were obtained from ‘WorldClim’ database available for download from
http://www.worldclim.org (Hijmans et al., 2005b). This dataset contains global climate grids with a
spatial resolution of a square kilometre and can be used for mapping and spatial modelling in GIS
(Hijmans et al., 2005a). Sea surface temperatures (SST) were derived from climatological data using
the NOAA National Oceanographic Data Centre (NODC) and the University of Miami Rosenstiel
School of Marine and Atmospheric Science (RSMAS) AVHRR Version 5.0 Pathfinder SST dataset
available at ftp://data.nodc.noaa.gov/pub/data.nodc/pathfinder/Version5.0_Climatologies/ (NOAA-
NODC, 2006) for the period 1985 to 2001. This averaged data was already resolved to 4km and in 5-
day, 7-day, 8-day, monthly, seasonal, and annual periods and each period provided daytime-only,
night-time-only, and day-night combined.
Here day-night combined monthly mean sea surface temperature HDF file dataset was imported to
ERDAS and then into ILWIS. The dataset provided needs to be rescaled and transformed to represent
SST in degree Kelvin. The scale and offset provided are 0.075 and -3°K respectively, so that the
expression for calculation of SST appears as given in equation 3.1.
15.273)3075.0*( +−= origK SSTSST (3.1)
where: SSTK and SSTorig are corrected SST (in °K) and original SST (in °C), respectively.
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At this stage the land surface minimum, maximum, and mean temperatures given in monthly were
merged with the generated mean monthly SST. The final images of climatological monthly day-time,
night-time, and mean temperature of the entire globe were generated. These were used in computing
the dew point temperature as well as performing cloud mask. Figures 3-5(a - c) shows these
climatological images for the month of May for the African continent and part of Atlantic Ocean. The
temperature is in ˚K.
Figure 3-4: Climatological Temperature (in °K) images; (a) day-time (b) night-time (c) mean , of Africa and part
of Atlantic Ocean for the month of May
3.1.3. Dew Point Temperature
Dew point temperature is an important geophysical parameter that indicates the state of moisture
content in the air under given conditions (Hubbard et al., 2003). This is the critical temperature at
(a)
(b)
(c)
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which air is fully saturated and below which condensation normally occurs. Figure 3-5 shows
schematic diagram of idealized atmosphere in which a convective cloud is developing vertically.
In their study, Hubbard et al., (2003) presented a temperature-based (daily maximum, minimum, and
mean) daily dew point temperature estimation method for historical studies in the Northern Great
Plains in USA. They developed four regression-based methods incorporating daily maximum,
minimum, and mean temperatures and also daily precipitation for different locations in the plains.
After statistical analysis of the results obtained from the four methods, they concluded that the model
that performed satisfactorily was in the form:
λγβα +−++= )()()( nxnmd TTTTT (3.2)
where: Td is the daily dew point temperature in °C
Tm is the daily mean air temperature in °C
Tn is the daily minimum air temperature in °C
Tx is the daily maximum air temperature in °C
α, β, γ, and λ are coefficients of the regression equation.
The method was further supported by the fact that the associated data set required are easily available
in most typical meteorological weather stations. The model as Hubbard et al., (2003) pointed out can
estimate dew point temperature with sufficient accuracy under varied climatic conditions. Moreover,
they also indicated that the climatic conditions observed within the Northern Great Plains are
representative of many other regions in the world.
Based on the above statements, dew point temperature was therefore computed by use of the model as
given in equation 3.2 and consequently equation 3.3 with all the associated coefficients was adopted.
However, here use of the climatological monthly mean, maximum, and minimum temperatures was
made instead of the daily temperatures. Thus monthly climatological dew point temperature was
obtained as follows:
0119.1)(0072.0)(9679.0)(0360.0 +−++−= nxnmd TTTTT (3.3)
where: Td is the calculated monthly climatological dew point temperature in °C
Tm is the mean monthly temperature in °C
Tn is the minimum monthly temperature in °C
Tx is the maximum monthly temperature in °C
Minimum, maximum and mean monthly temperatures used here were those obtained from the centres
mentioned in section 3.1.2 above. However, it is expected that some slight differences may occur in
the final dew point temperature values obtained since there was no recalibration of the model with
local (African region) data which would otherwise provide more suitable coefficients and
subsequently more accurate estimation of dew point temperature. In addition to this, differences due
to use of monthly instead of daily temperatures are expected since the regression is based on daily
temperatures.
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Figure 3-5: Schematic view of temperature lapse rates in an idealized convective cloud.
From dew-point concept as visualised in figure 3-5, cloud height can be extracted. The reference point
is the earth surface. Dry adiabatic lapse rate of 1°C per 100m and saturated/moist adiabatic lapse rate
of 0.6°C per 100m were used here as suggested by Strahler, (1965) and widely accepted in many
studies. An example of cloud height (considering only one pixel value) calculation is given here
below:
Supposing that: surface maximum monthly (e.g. for May) climatological temperature is 300.4K; dew
point temperature (at the base of the cloud) is 299.3K (as obtained from equation 3.3); and that
brightness temperature observed by the satellite at the top of the cloud is 242.7K. Here brightness
temperature was taken as the mean of IR10.8 and IR12.0. In addition supposing an ideal situation of
unstable atmosphere where change of temperature with height (lapse rate) is approximately 1°C per
100m (for dry adiabatic) and approximately 0.6°C per 100m (for moist adiabatic),
THEN: The cloud top height would be calculated as given in the following expression.
)100*6.0*)(()100*1*)(( bddx TTTTH −+−= (3.4)
3506)100*6.0*)7.2423.299(()100*1*)3.2994.300(( =−+−=
where: H is the cloud height in meters
Tx is the maximum monthly climatological temperature in °K
Td is the dew point temperature (in °K) as calculated from equation 3.3
Tb is the brightness temperature (in °K) at the top of the cloud
Thus in this example the cloud top height would be 3506m
Lapse rate =
1°C/100m
Dew point
temperature
Clo
ud
hei
gh
t Condensation level
Earth surface
Top of the cloud
Lapse rate =
0.6°C/100m
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3.1.4. Synoptic Data and Field work
Synoptic data was collected (from Eastern part of Africa) during a fieldwork campaign and also
through a request made to CGIS, Rwanda. Fieldwork activity focused on collecting rainfall data
which was measured by setting two rain gauges (tipping bucket type with data loggers); one at
Dagoretti corner (Nairobi, Kenya) and one at Kisumu Airport (Kenya). Rainfall data was also
collected from a rain gauge at the Ministry of Water and Irrigation, Naivasha (Kenya). Locations of
the four stations are as shown in table 3-1 (with the time of rain gauge recording interval) as well as in
figure 3-7.
Table 3-1: Locations of the four stations within Eastern Africa
Station Coordinates Time of Recording Interval
Latitude Longitude
Nairobi (Dagoretti) 1° 18´S 36° 46´E not regular (on tipping)
Naivasha 0° 24´S 36° 18´E not regular (on tipping)
Kisumu 0° 06´S 34° 36´E not regular (on tipping)
CGIS, Butare 2° 36´S 30° 06´E 30 minutes
Figure 3-6: Locations of the four stations shown on MSG satellite false colour composite image
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The choice of CGIS station was based on availability of long term series of data that is recorded after
every 30 minutes from an hour (for instance; 12:00, 12:30, 13:00, 13:30 UTC etc) (see also appendix
C). With this temporal scale it would be easy for comparison with the MSG satellite images which are
received in 15 minutes. Besides, CGIS is far enough from the other stations in Kenya and sparsely
distributed rainfall stations in this current study are important especially in the rainfall estimation for
the obvious reason that at different climatological regimes rainfall estimation methods may have
different regression functions.
The stations from Kenya (Dagoretti –Nairobi, and Kisumu) were selected again due to their distance
from one another which is about 400km. In addition Kisumu is situated next to Lake Victoria and thus
in cloud mask it would be interesting to investigate the influence of water body (e.g. lake or sea) to
cloud mask. Setting own rain gauge (tipping bucket) was therefore necessary to accurately observe the
rainfall amount over these two stations. The tipping bucket type of rain gauge used here has the
capability of showing the date and time of rainfall observation (see Appendix F). It is therefore easy
to compare observations with MSG satellite images which are acquired in after every 15 minutes.
However, the limitation here was that the time the tipping occurs could be between MSG image
acquisition times. This is well explained by figure 3-7 (b) with the shaded area showing time between
two tips and in which satellite image is acquired between the two tips.
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(a)
(b)
Figure 3-7: (a) Image acquisition by SEVIRI radiometer, and (b) Schematic diagram on MSG satellite and Rain
gauge observation time
In addition figure 3-7 (a) shows the time stamping of MSG satellite image data, which indicates that
within the equatorial region the data is acquired between the two times, say for12:00 UTC image,
between 12:00 UTC and 12:12 UTC from south to north of the whole disk. The remaining 3 minutes
MSG image
acquisition time
Time
Rain gauge observation
times
Image acquisition by the SEVIRI
radiometer
(Source: Meteosat Second
Generation System Overview,
EUM TD 07– Issue 1.1, 25 May
2001) (EUMETSAT, 2001)
Archive repeat cycle-time (e.g. 12:12UTC)
Dissemination repeat cycle-time (e.g.12:00UTC)
Appr. 12:06UTC at
equator
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are used in calibrating and turning of the radiometric mirror ready for the next image acquisition.
Thus from the two diagrams, it is clear that MSG image acquisition time and gauge observations may
not necessarily be at exactly the same time.
Thus, in order to solve the problem of different time of observations between the rain gauge and the
satellite, there was a need to average over a period of time and space. Here observations within one
hour, four MSG top of atmosphere brightness temperatures of the three infrared channels (IR03.9,
IR10.8, and IR12.0) as well as total amount of rainfall observed at the station, were averaged.
In addition to the two stations in Kenya, rainfall data was collected from a rain gauge at the Ministry
of Water and Irrigation, Naivasha. The rain gauge (tipping bucket) was installed in the year 2004 and
therefore long period of data was available. Sample of data set collected from the field using this type
of rain gauge is given in Appendix F for Nairobi (Dagoretti) which is similar to that of Kisumu and
Naivasha.
3.2. Cloud Masking Method
Cloud mask method chosen in this study was based on multispectral thresholding technique. This
included creating ILWIS scripts in order to generate the necessary images required for processing the
cloud mask image.
In this method, a number of tests that allowed identification of pixels contaminated by clouds were
performed. The main characteristic of these tests, applied to sea or land pixels, depended on the solar
illumination conditions and on the satellite viewing angle. The definitions of day-time, night-time, and
twilight are as given in table 2-2. The quality of the cloud detection process was assessed by
visualizing with the visible bands (for day-time) of the same day and time. Night cloud mask were
checked by using one thermal band.
Here use of non-linear algorithm, as developed by (Météo-France, 2005a), was made in order to
compute sea surface temperature using climatological SST. Split window approach was used with
IR10.8 and IR12.0 (brightness temperature of bands 9 and 10) averaged and applied in the algorithm
which is in the form given below.
( )( ) ( ) 2.010718.1*07293.01sec18116.198826.0 0.128.108.10 ++−+−+= TTSSTTTs θ (3.5)
where: sT is the calculated sea surface temperature (SST) in °C
8.10T and 0.12T are brightness temperature (in °C) of bands 9 and 10 respectively
SST is the climatological sea surface temperature (°C)
Sec θ is the inverse of cosine of the satellite zenith angle.
Here climatological night-time temperature was used as SST. A number of tests were performed in an
attempt to extract cloud-contaminated pixels. These tests are explained in the next chapter as well as
in the ILWIS scripts which are given in Appendix A. The final cloud mask obtained was then ready
for further cloud classification.
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Cloud mask results were also validated using available cloud mask products from EUMETSAT,
thanks to the ITC Geodata software development section that recently developed the GRIB2 decoder.
During initial stages of this study, validation was not possible since there were no available cloud
mask products in the same format as MSG products readily available within ITC. An example of
cloud mask from EUMETSAT is as given in figure 2-3 showing cloud as white, clear land as green,
and clear sea as blue.
The following section outlines how the mask was classified according to heights and used to compare
and estimate rainfall from clouds (here going to be referred to as storms).
3.3. Rainfall Estimation Method
As earlier pointed out, rainfall is one of the most difficult atmospheric parameter to measure due to its
variability in space and time. However, (Jobard, 2001) stated that rainfall can be inferred from
infrared satellite observations in which case brightness temperature for thermal bands for e.g from
10.8µm and 12.0µm measured over cloudy area is related to the cloud height. Clouds with very cold
top temperature indicate deep convection which is associated with observed surface precipitation
especially in the tropics. The relationship between infrared temperature and rainfall intensity or
amount is entirely indirect (Jobard, 2001). Generally it is difficult to discriminate the convective part
of the system producing heavy rainfall from the stratiform part of the system or cirrus clouds which
are also cold at the top and hardly produce rainfall. In this case therefore, it is worthy attempting to
relate cloud height with observed rainfall. The height has to be obtained from methods as explained in
the previous section 3.2 in which an attempt has been made to extract clouds at various heights.
One-dimensional cloud model-based technique was used in this study to find the basic relationship
between cloud height and observed rainfall at a ground station. This was based on relating cloud top
height to rainfall amount. Regression-based model as given in equation (3.3) was used to obtain cloud
height images. Cloud top height was processed from the cloud top temperature as recorded by MSG
satellite. Here brightness temperatures for bands 9 and 10 were averaged. Figure 3-5 indicates
schematic temperature lapse rate in relation to cloud height. The general assumption here was that
temperature lapse rate, as depicted in the schematic diagram holds in an unstable atmosphere in which
case deep convective clouds are formed.
In this study, rainfall estimation was based on comparison between point observation and satellite
estimation using the one-dimensional cloud-model based technique as mentioned above. In this case
therefore, and as Maathuis et al., (2006) pointed out; there is a need to incorporate an averaging
procedure in order to account for the collocation problems such as spatial and timing offsets. Here
spatial average was carried over 5x5 pixels and temporal average was carried for an hour (four MSG
images in an hour). Retrieved temperatures of infrared bands 4, 9, and 10 were averaged and used in
the simple cloud mask algorithm developed as an ILWIS script given in Appendix A. See also the
next chapter for explanations of different thresholds used. Cloud height obtained in this algorithm was
used to compare and estimate rainfall from storms.
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The method used in this study follows the idea that clouds produce different amount of rainfall or
have different rainfall intensity at different stages of development. This is best represented by (Hong
et al., 2004b) in their study on cloud patch-based rainfall estimation using satellite image
classification approach. It should be noted here that due to different climatological regimes, empirical
relations (either temperature versus rainfall intensity or cloud height versus rainfall intensity) derived
may vary significantly. (Adler and Mack, 1984) studied the impact of the regime-to-regime various on
empirical rain estimation schemes based on satellite-observed cloud height or cloud temperature
information in which curves representing coastal and inland regimes were strikingly different. They
pointed out that these differences had obvious implications for the application of an empirical satellite
rain estimation derived in one location and applied in other climatological regimes even with a simple
local adjustment. Varying synoptic situations may also cause these types of differences.
Rainfall data from CGIS station in Rwanda was investigated to identify storms which produced
rainfall over a given period (recorded after every 30 minutes). The observations were recorded at e.g.
1000hrs, 1030hrs, 1100hrs, 1130hrs, etc. The data was in local time and was converted to Universal
Time Convention (UTC). For the case of Rwanda, 2 hours are subtracted from local time to change to
UTC and for Kenya 3 hours are subtracted from local time.
Appendix C shows sample of meteorological records including rainfall obtained from CGIS weather
station and used to investigate cloud height variation over a given period. This would give an idea on
how the two relate over the selected region which may provide water resource management
authorities first approximations of rainfall amount expected from a storm at a particular height.
Twelve storms of different days and time from CGIS station were used to develop a regression
function between the height and observed rainfall. The function was consequently used to estimate
rainfall amount from other storms over the same station to validate the performance of the method
developed and thresholds selected during the simple cloud mask algorithm development.
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4. Data Processing and Results
This chapter attempts to analyse the processed MSG images in detail and the final cloud mask
obtained as well as the cloud height classification images. Analysis of field data and rainfall estimates
drawn from developed regression function(s) are also presented.
4.1. MSG Satellite Images
The first step was retrieval of relevant bands as well as calculation of solar illumination angles. Solar
illumination angles were based on conditions as given in table 2-2. MSG satellite and solar zenith
angles were also generated since this change due to earth rotation about its axis.
4.1.1. Generation of MSG Satellite and Solar Angles
Generating MSG satellite and sun angles was done by creating a batch file which could be adapted for
any date and time in case new angles were required. This particular applet, which can be executed into
an active directory, works in a java environment which must be installed in the system. Generated
angles were imported into ILWIS for further processing. Figure 4-1 shows the flow chart for
generating the satellite and solar angles. The processing was done for mainly MSG field of view
covering Africa (≈ 39˚N - 38˚S and ≈ 34˚W - 53˚E).
Figure 4-1: Flow chart for generating MSG satellite and Sun angles
Create MSG and Sun zenith angles
(In Java environment)
Import satellite and sun zenith angles
into ILWIS
Add MSG georeference
Resample the satellite and sun
angles into required georeference
Calculate secant angle of the satellite
and the sun
MSG secant
angle image
Sun secant angle
image
Apply sun elevation angle
& generate threshold maps
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Figure 4-2 shows examples of the sun and MSG satellite zenith angles. Sun position as from the left
image can be seen to be overhead in the south west of the image (brightest part) for 26 December
2006 at 15:00 UTC. MSG satellite is situated at 0˚N and 0˚E and can be seen to appear at the same
position (brightest part) in the right image.
Figure 4-2: Sun (for 26th December 2006 at 15:00 UTC) and MSG Satellite (0˚N and 0˚E)
Zenith angles (left and right hand image respectively)
After calculating sun elevation angle, solar illumination conditions were generated by use of threshold
mentioned in table 2-2 in which the condition is day-time when the sun elevation angle is greater than
10° and night-time when the sun elevation angle is less than -3°. The condition is twilight, that is,
either before night-time or before day-time when the sun elevation angle is between -3° and 10°. An
example of such an image of 7th March 2006 at 15:30 UTC is provided in figure 4-3.
Figure 4-3: Solar illumination conditions on 26th December 2006 at 15:00 UTC
As earlier pointed out, the algorithm is based on a multispectral threshold technique applied to each
pixel of the image. A number of tests for each solar illumination condition in which an example of
0˚
23˚
23˚
46˚
46˚
0° 0°
0˚N 0˚E
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cloud mask is given in figure 4-7 were applied. The tests applied in the algorithm attempted to address
both the land and sea surfaces based on their characteristics. Flow charts for these tests are given in
the following respective sections.
4.1.2. Day-time Cloud Mask
Figure 4-4: Description of the test sequences for Land surface (left) and Sea surface (right)
Figure 4-4 shows the description of test sequence used in cloud mask during day-time. Over the land
surface, during the day cloud contaminated pixels were identified by using standard deviation from
the climatological surface temperatures (mean, minimum, and maximum) which should be greater
than 1K. Minimum surface temperature was taken as the monthly climatological night-time
temperature as processed from the ‘WorldClim’ database whereas maximum surface temperature was
taken as day-time temperature and the mean surface temperature was average of the day-time and
night-time surface temperatures.
To remove false cloud assignment to pixels over desert areas, brightness temperature of band 9
(IR10.8) less than 293.15K was applied otherwise the pixels with a higher temperature were
considered cloud free. Further, all pixels already defined as cloudy were subjected to tests in order to
avoid cool areas or higher elevated areas. These involved using monthly climatological temperature
standard deviation (amplitude). Cloudy pixels with brightness temperature (IR10.8) less than
maximum (Tmax) day-time monthly climatological temperature less half the monthly standard
deviation are assigned cloudy else not cloudy. This does not affect the ocean areas since the
climatological standard deviation is very small. This test allows us to reduce misclassifications to the
minimum except in high elevated areas and desert areas.
Over the sea surface, cloud-contaminated pixels were identified by using standard deviation from the
climatological surface temperatures (mean, minimum, and maximum) which should be less than 1K.
Further, small difference of -1K (and above) between the local sea surface temperature (as calculated
Stddev<1
Clear
IR10.8>293.15K
IR10.8> (Tmax -
stddev/2)
Cloud
Yes
Yes
Yes
No
No
No
Stddev>1
and
Tsmin-SSTcal<-1K
Clear Cloud
Yes
No
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using equation 3.5), here referred to as SSTcal, and minimum monthly climatological sea surface
temperature (here referred to Tsmin) was also used to mask cloudy pixels over the sea surfaces.
As earlier explained, Météo-France have developed cloud mask in which this study adopted some of
the basic ideas to develop some of the thresholds used. Estimating SST by using IR10.8 and IR12.0
brightness temperature together with minimum monthly climatological SST was used by Météo-
France. Here the same two bands are used, as top of atmosphere (in °K) together with minimum
monthly SST (here taken as the night time temperature). Météo-France took a small difference of 4K
between estimated SST (by using IR10.8 and IR12.0 brightness temperatures) and the monthly
climatological minimum SST. Monthly climatological minimum SSTs are derived from a global
Pathfinder night-time bulk SST climatology. The bulk night-time SST, as (Derrien and Le Gléau,
2005) pointed out, does not account for the thermal heating at midday observed in infrared satellite
measurements. In this study this difference was set at -1K over the sea surface as stated above.
Brightness temperature of band 9 (IR10.8) was applied by Météo-France as well as by (Kidder et al.,
2005) in which the idea was to estimate the temperature that would be observed if there was no water
vapour in the atmosphere. Météo-France computed threshold from surface temperatures forecast by
NWP model. In this study threshold of 293.15K was set as the maximum temperature for any pixel to
be flagged cloud contaminated. Météo-France again used IR10.8 and IR12.0 difference to detect thin
cirrus clouds and cloud edges characterized by higher IR10.8-IR12.0 values than cloud-free surfaces.
Here use of IR10.8 less than the maximum climatological surface temperature (with half amplitude of
the climatological monthly minimum, maximum, and mean temperatures) was to extract thin cirrus
clouds as well as to avoid confusion of moist, warm, cloud free areas with clouds. With these few
tests day-time cloud mask was obtained of which an example is as given in figure 4-7 (a).
Notable features of this cloud mask are such as sharp boundary between the land and the sea that
appears along some coastal areas, especially in this particular case to the North West of the continent.
This depicts cloudy conditions over the ocean and non-cloudy conditions over the land, which may
not be always the case. The sharp boundary is due to the land-sea temperature effects and increases as
we move from equatorial regions to higher latitudes where temperatures are generally low over the sea
such as the case in the north-western part of the continent (over the Atlantic ocean). This is more
pronounced especially when desert (usually with high temperatures) areas lie next to water body.
Cloud mask image shows presence of clouds over the northern part of Africa whereas from the false
colour composite of the visible and near infrared bands does not show the same situation. Over central
Africa and Atlantic Ocean (the specific region of interest in this study) most of the cloudy pixels (as
can be seen from the false colour composite image) have been masked out.
Also as can be seen from the false colour composite image in figure 4-9 (b), there appears no thick
clouds in the northern part of the continent. However, the algorithm has classified the region to be
under low level clouds which are semi-transparent in the visible and near infrared bands.
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4.1.3. Night-time Cloud Mask
Figure 4-5 shows the description of the test sequences used in cloud masking at night time. Details of
these tests are explained below.
Figure 4-5: Description of test sequence for land surface (left) and sea surface (right)
During night-time, the standard deviation of the monthly climatological temperature was set at a
minimum of 1K and the mean brightness temperature (Tmean) of IR10.8 and IR12.0 was taken less than
283.15K over the land surface for any pixel to be flagged as cloudy. The difference of the monthly
minimum climatological temperature and the brightness temperature of IR10.8 is used is set to be
greater than 9K for any pixel to be assigned cloudy. This ensured avoiding cooler areas at night which
would otherwise be assigned cloud contaminated. Use of mean brightness temperature for IR10.8 and
IR12.0 followed Météo-France developed cloud mask idea in which the difference between the two is
used to detect thin cirrus clouds and cloud edges characterised by higher difference (IR10.8-IR12.0)
values than cloud-free surfaces. However, in this study the mean of the two was expected to simply
avoid the confusion of very moist, warm, cloud free areas with clouds.
Over the sea, the standard deviation of the monthly climatological temperature is less than 1K. The
difference between the local calculated sea surface temperatures (SSTcal) and the monthly mean
climatological temperature was taken to be greater than 10K. Low clouds over the sea were screened
by use of IR03.9 to scale down aggregated temperatures of IR10.8 and IR12.0 (i.e. IR10.8*IR12.0).
The difference between their mean temperatures and the scaled temperature is set at a minimum
threshold value of 2K for the cloudy pixels. This test is based on the fact that the water cloud
emissivity is lower at IR03.9 than in IR10.8 or IR12.0. The test allows detecting low clouds at night
time. The approach is the same as that of Météo-France using the difference between IR03.9 and
IR10.8. An example of night-time cloud mask is given in figure 4-7 (c).
Stddev>1K
and
Tmean-SSTcal<10K
[(IR10.8*IR12.0)/IR03.9]-
Tmean <2K
Cloud Clear
Yes
No
No
Yes
Stddev<1K
and
Tmin-IR10.8<9K
and
Tmean>283.15K
Clear Cloud
Yes
No
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4.1.4. Twilight Cloud Mask
Figure 4-6 shows the test sequences used in extracting clouds during twilight time. Details of these
tests and dynamic thresholds are explained below the figure.
Figure 4-6: Description of test sequence for land surface (left) and sea surface (right)
At twilight time the difference between climatological minimum temperature and the brightness
temperature of band 9 (IR10.8) was set at a threshold of 9K such that any pixel with greater difference
than this value and with mean brightness temperature (IR10.8 and IR12.0) less than 283.15K were
cloud contaminated. This ensured screening cloudy pixels over the land surfaces where also standard
deviation of the monthly climatological temperatures was set at a minimum of 1K.
Over the sea, the difference of mean monthly climatological SST and the calculated SST was taken to
be greater than 5K for the cloudy pixels. Here Météo-France used IR10.8 and IR12.0 brightness
temperatures to estimate SST by using a nonlinear split window algorithm. A pixel is flagged cloud
contaminated if its estimated SST value is lower than a minimum monthly climatological SST value
by 4K. However, Météo-France does not apply this test where climatological SST is lower than
270.15K. In this study low clouds were extracted by use of IR3.90 to scale down brightness
temperature of bands 9 and 10 (IR10.8 and IR12.0, respectively). Here maximum threshold value of
2K as the difference between the scaled temperature and the mean brightness temperature of IR10.8
and IR 12.0 was used. Threshold for the difference between estimated SST and the climatological
SST from Météo-France gives a threshold of 4K which is comparable to the set value in this study.
Météo-France uses IR03.9 and IR10.8 difference to extract low clouds for both day-time and twilight
time basing the fact that solar reflection at IR03.9 (approximated by the IR03.9-IR10.8 brightness
temperature difference) may be rather high for clouds (especially low clouds), which is not the case
for cloud free areas . An example of twilight cloud mask from this study is as given in figure 4-7 (b).
Stddev<1K
and
Tmin-IR10.8<9K
and
Tmean>283.15K
Cloud Clear
Yes
No
Stddev>1K
and
Tmean-SSTcal<5K
[(IR10.8*IR12.0)/IR03.9]-
Tmean>2K
Cloud Clear
Yes
No
No
Yes
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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45
Figure 4-7: Cloud masks for (a) day-time, (b) twilight time, and (c) night-time; for MSG-1 image of 7th March
2006 at 15:30 UTC.
Cloudy pixels are represented as green whereas grey represents non-cloudy pixels. Merging the three
images resulted in final cloud mask as given in figure 4-9 (a). Colour composite image of the same
day and time is as in figure 4-9 (b). Here solar illumination conditions are as given in figure 4-8
below.
Figure 4-8: Solar illumination conditions on 7th March 2006 at 15:30 UTC
(a)
(b)
(c)
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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46
Figure 4-9: Cloud mask (a) and false colour composite (b) for MSG image of 07/03/2006 at 15:30 UTC.
In the cloud mask, green are clouds and white are no cloudy pixels. False colour composite of the
visible and near infrared bands (VIS006, VIS008, and NIR016) is more visible from central Africa to
the Atlantic Ocean. This is the day-time region as can be seen from figure 4-8. To the eastern part, it
is not easy to visualise since this area already falls under twilight and night conditions.
Sharp boundary between the land and the sea can be seen to appear along some coastal areas,
especially in this particular case to the North West. This depicts cloudy conditions over the ocean and
non-cloudy conditions over the land, which may not be always the case. The sharp boundary is due to
the land-sea temperature effects. This is more pronounced especially over desert (usually with high
temperatures) areas next to water body. Cloud mask image shows presence of clouds over the
northern part of Africa whereas from a visual check using the false colour composite of the visible
and near infrared bands does not show the same scenario. Over central Africa and Atlantic Ocean
most of the cloudy pixels (as can be seen from the false colour composite image) have been masked
out.
The next step was to process heights for the extracted clouds based on the formula for estimating dew
point temperature as given in equation 3.4 in which an example is given in figure 4-10.
a b
Legend
Cloud
Cloud free
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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47
Figure 4-10: Cloud height (in Meters) image (MSG image of 07/03/2006 at 15:30 UTC)
The cloud height images were classified into three different classes namely; low clouds (50m-1500m),
middle clouds 1500m-3000m, and high clouds (> 3000m). These classes were chosen based on
occurrence of different types of clouds at different levels as is shown in figure 2-1. Based on this
classification and as explained in section 2.1, it is possible to show areas where precipitation is likely.
However, this is further investigated in the proceeding section of rainfall estimation. An example of
classified cloud height image is given in figure 4-11 below.
Figure 4-11: Classified cloud height image of 07/03/2006 at 15:30 UTC
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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48
In order to check whether all cloudy pixels have been extracted properly for the area of study,
segmentation of the classified cloud image was done and overlaid on to a false colour composite
image of visible and near-infrared bands of the same day and time. Figure 4-12 shows an example of a
small window (eastern part of Africa) of such an overlay for MSG satellite image of 23rd November
2005 at 13:30 UTC. This area was under twilight condition on 7th March 2006 at 15:30 UTC and thus
such an overlay is not provided here. Segmentation was performed on the classified cloud height
image of 23rd November 2005 at 13:30 UTC and the segments overlaid on the false colour composite
image of the same date and time.
Figure 4-12: Segments (yellow lines) of cloud mask of 23/11/2005 at 13:30 UTC overlaid on False
colour composite (VIS006, VIS008, and NIR016) in (BGR)
Clouds appear as cyan in colour in the false colour composite image. As can be observed visually
from figure 4-12, most of the cloudy pixels have been identified. This is more visible over areas where
deep cyan colour (mostly deep convective clouds) appears. Some semi-transparent clouds have not
been masked out. However, this is not of serious concern in this current study since most of these
semi-transparent clouds do not contribute to precipitation, and if they do, very little rainfall is
expected from them. Further discussions to the accuracy of the simple cloud mask algorithm
developed are provided in the next chapter.
4.2. Rainfall Estimation (A case of CGIS Weather station)
As earlier pointed out, CGIS weather station has a rain gauge which is set to measure rainfall among
other meteorological parameters in every 30 minutes. The advantage to such a type of record of data is
that it is possible to compare with MSG satellite observation(s) of parameters such as top of
atmosphere brightness temperatures of infrared bands, cloud height, and cloud type; and develop a
relationship that can be used to infer rainfall intensity or amount from observed (non atmospherically
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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49
corrected) satellite parameters. Various methods as explained in section 2.5 can be used in developing
the relations between these parameters. In this study rainfall versus processed cloud height were
investigated to get an idea on how they relate and also to find a function that can be used to forecast
rainfall intensity or amount expected on a ground station (e.g. CGIS).
An attempt was made to relate rainfall intensities from clouds of different dates and times with
processed cloud heights of the same dates and times. The relationships were generally too low. This
was mainly due to the fact that at different dates and times the cloud/storm over the station is not
necessarily at the same development stage. It is likely that in such an approach, relationships are being
drawn for storms at different stages of their development (and probably of different types) over the
station.
As explained in section 2.5.5, satellite-based rainfall estimation algorithm, Precipitation Estimation
from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud
Classification System (CCS) by Hong et al., (2004a), extracts cloud features from IR10.8
geostationary satellite imagery in estimating fine scale rainfall distribution. The algorithm processes
satellite cloud images into pixel rain rates by; separating cloud images into distinctive cloud patches,
extracting cloud features, clustering cloud patches into well-organized subgroups, and calibrating
cloud-top temperature and rainfall relationships for the classified cloud groups using gauge-corrected
radar hourly rainfall data.
Based on this method, therefore, relating rainfall intensities and cloud heights of different dates and
times was not expected to yield good relationship. Thus storms over the station were treated
separately in order to relate their rainfall intensities or total rainfall amounts with their heights.
The relationships between cloud height and the rainfall intensity as well as between cloud height and
the total rainfall from a storm were then developed. The following sections detail the results of these
approaches.
4.2.1. Direct Comparison of Cloud Height and Rainfall Intensity
Rainfall estimation method used here was based on average storm height during its existence over
ground station. Firstly, diurnal trend of observed rainfall intensity and processed cloud heights, over
the weather station, were investigated. Two days were selected for this purpose and figure 4-13 shows
how cloud height and rainfall intensity varied during the selected days. MSG images of 30 minutes
interval were processed to obtain cloud heights whereas rainfall intensities observed at the station at
the same time of MSG image acquisition were used.
In both cases it can be seen that clouds at a height of above 3000m contribute to a large fraction of the
rainfall recorded at the station. The highlighted part clearly shows the time rainfall was observed at
the station which agrees with the time of high cloud heights as processed by the simple cloud height
algorithm developed using the dew point temperature and lapse rate concepts as applied in equation
3.4.
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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50
0
1000
2000
3000
4000
5000
6000
0:30
3:30
6:30
9:30
12:30
15:30
18:30
21:30
Local time (Hrs)
Clo
ud
He
igh
t (m
)
0
10
20
30
40
50
60
Ra
infa
ll I
nte
ns
ity
(m
m/h
r)
Cloud height
R/Intensity
(a)
0
1000
2000
3000
4000
5000
6000
0:30
3:30
6:30
9:30
12:30
15:30
18:30
21:30
Local time (Hrs)
Clo
ud
He
igh
t (m
)
0
5
10
15
20
25
30
35
40
45
50
Ra
infa
ll I
nte
ns
ity
(m
m/h
r)
Cloud height
R/Intensity
(b)
Figure 4-13: Diurnal cloud height and Rainfall intensity changes on (a) 5th May 2006, and (b) 10th May 2006
Secondly, identification of storms on different days and plotting their rainfall intensities against
processed cloud height, in this case in class intervals of 500m, followed. This was meant to check
whether the above two day’s cases were a mere coincidence or is the true scenario expected from this
particular station. Here cloud heights were grouped from 2500m in intervals of 500m. Fifteen storms
with their 30 minutes interval processed heights and their respective observed rainfall intensities were
plotted as shown in figure 4-14. For details of these storms refer to Appendix D.
0
10
20
30
40
50
60
2500 3000 3500 4000 4500 5000 5500 6000
Height (m)
Ra
infa
ll In
ten
sity (
mm
/hr) Rain intensity
Figure 4-14: Rainfall intensities within cloud height classes
From this plot it is clear that high rainfall intensities are observed within cloud heights of 4000m to
6000m. This indicates the same situation as in the case of 5th May 2006 and 10th May 2006 as shown
in figure 4-13. However, in each cloud height class there are low rainfall intensity observations. This
could be associated with early stages of cloud formation or late stages (dissipating stage) of the cloud.
Rainfall intensity within each class was averaged and plotted against average cloud height in each
class. The best model fit was found to be Gaussian, whose regression function is: (y=a*exp ((-(x-
b)^2)/(2*c^2)), where: a = 9.8, b = 4745.6, and c =1200.3; with correlation coefficient of 0.95 and
standard error of 1.03). This model agrees with the fact that very low clouds (e.g. stratocumulus,
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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51
cumulus, and stratus) and very high clouds (e.g. cirrus, cirrocumulus, and cirrostratus) produces very
low rainfall.
Figure 4-15: Gaussian model fit, X = Average cloud height (m), Y = Average rainfall intensity (mm/hr)
An attempt was made to use this function to estimate rainfall intensity for various storms. Figure 4-16
represents plots of all observed and estimated values within each cloud height class. It is clear that the
function overestimated the rainfall intensities from these storms except in very few levels (height)
where it underestimated. This appears so when cloud height is between 2500m and 3000m. Thus there
was a need to adopt a different approach by either using rainfall intensity or total rainfall amount from
different storms.
0.00
2.00
4.00
6.00
8.00
10.00
12.00
2500 3000 3500 4000 4500 5000 5500 6000
Cloud height (m)
Rain
fall
Inte
nsity
(m
m/h
r)
Observed
Estimated
Figure 4-16: Observed and estimated rainfall intensity for different storms
Based on the above results, it can be observed that the developed regression function did not perform
well. There was general overestimation of rainfall intensity. Further investigation of the relationship
between cloud height and total amount of rainfall from a storm was carried out as explained in the
following section.
Av
era
ge
rain
fall
in
ten
sity
(m
m/h
r)
Average cloud height (m)
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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52
4.2.2. Direct Comparison of Cloud Height and Total Rainfall
Diurnal trend of observed total rainfall and processed cloud heights, over the weather station, were
investigated. Two days were selected for this purpose and figure 4-17 shows how cloud height and
total rainfall varied during the selected days. MSG images of 30 minutes interval were processed to
obtain cloud heights whereas total rainfall observed at the station at the same time of MSG image
acquisition was used.
0
1000
2000
3000
4000
5000
6000
0:30
3:30
6:30
9:30
12:30
15:3
0
18:30
21:3
0
Local time (Hrs)
Clo
ud
He
igh
t (m
)
0
5
10
15
20
25
30
35
To
tal
Ra
infa
ll (
mm
)
Cloud height
Total Rainfall
(a)
0
1000
2000
3000
4000
5000
6000
0:30
3:30
6:30
9:30
12:3
0
15:3
0
18:3
0
21:3
0
Local time (Hrs)
Clo
ud
He
igh
t (m
)
0
5
10
15
20
25
30
To
tal
Ra
infa
ll (
mm
)
Cloud height
Total Rainfall
(b)
Figure 4-17: Diurnal cloud height and Total rainfall changes on (a) 5th May 2006, and (b) 10th May 2006
It can be observed that rainfall was recorded at the station when the processed cloud height was at
high levels (above 3000m). It is clear then that high clouds over this station are the main rain
producing rainfall clouds. This indicated that there is a relationship between cloud height and total
rainfall produced by the cloud at certain height. The general idea followed in this comparison is that
the more the cloud is sustained at a certain height while producing rainfall, the more the rainfall is
observed at a ground station. This idea is borrowed from the case of CCD as explained in section
2.5.3 in which the relationship drawn from the life-history cycle of the storm was found to be linear
provided spatial and temporal average are considered.
However, as (Grimes et al., 1999) pointed out, the most important assumption is that rainfall is
predominantly convective in origin and that the raining clouds can be identified as those with cloud
top temperatures below a certain temperature threshold. Here, cloud height is used to compare the
total amount of rainfall observed at CGIS. A regression function can be derived using as many storms
from the station as possible.
Twelve storms were used to derive a regression function that was later used to estimate total amount
of rainfall from other storms for validation purpose. Comparison with the observed station amount
over the same period with the estimated rainfall amount showed slight overestimation for some storms
and underestimation for others. Table 4-1 shows the date and time of the storms used to develop the
relationship between cloud height and the observed total storm event rainfall. Appendix E shows
processed details of the twelve storms.
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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53
Table 4-1: Observed storms and their total amount of rainfall
Storm Date Time Duration Average Total
(UTC) (hrs) Cloud Height Rainfall
(m) * (mm)
1 07/03/2006 1530-1930 3.5 5317.5 18.0
2 07/03/2006 2230-0200 3.5 5317.3 8.6
3 18/03/2006 1230-1530 3 3074.3 2.8
4 27/03/2006 1700-2130 4.5 3361.6 12.2
5 01/04/2006 2100-0500 8 3917.6 42.0
6 14/04/2006 1530-1800 2.5 2614.3 5.2
7 05/05/2006 2030-2230 2.5 4571.7 63.0
8 10/05/2006 0630-0930 3 4819.8 38.6
9 12/05/2006 1400-1900 5 5132.8 38.2
10 20/07/2006 2200-0130 3.5 2448.3 2.2
11 22/07/2006 0430-0630 2 2455.4 1.4
12 05/08/2006 1400-1630 2.5 5716.5 11.4
* Above the terrain
Determination of the model fit showed Gaussian fit as the best for CGIS by using the 12 storms that
appeared over the station selected for this analysis. The above storms were used to determine a
regression function between the two variables. The best fit obtained was again a Gaussian model
(y=a*exp ((-(x-b)^2)/(2*c^2)); where: a = 60.6, b = 4405.3, and c = 583.0 with correlation coefficient
of 0.96 and standard error of 6.56mm. This is presented graphically in figure 4-18. The model agrees
with the fact that very low clouds (e.g. stratocumulus, cumulus, and stratus) and very high clouds (e.g.
cirrus, cirrocumulus, and cirrostratus) produces very low rainfall.
Figure 4-18: Gaussian model fit, X= Average storm height (m), Y= Total rainfall (mm)
To
tal
rain
fall
(m
m)
Average cloud height (m)
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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54
The model whose equation is (4.1) was used to estimate the total amount of rainfall from other storms
over the same station.
−−=
2
2
0.583*2
)3.4405(exp*6.60
xy (4.1)
where: y is the estimated total storm rainfall
x ix the average cloud height
Five storms were taken for estimating the total amount of rainfall expected from them and the results
are presented in table 4-2 and graphically shown in figure 4-19, together with the standard error.
Table 4-2: Storm heights and estimated total rainfall
Avg. Obs. Est.
Storm Total Total
Height Rainfall Rainfall Difference
Storm Date Time (UTC) (m) (mm) (mm) (%)
1 08/03/2006 1600-1700 5588.2 2.6 7.74 -198
2 21/04/2006 0900-1000 3127.7 5.0 5.49 -10
3 25/04/2006 1900-2000 2615.5 5.8 0.54 91
4 14/05/2006 1900-2130 5487.2 13.0 10.83 17
5 16/05/2006 1400-1500 2730.2 2.8 0.98 65
From these results it can be seen that two out of five storms have been estimated to a reasonable
accuracy. These were the storms of 21st April 2006, and 14th May 2006. The 8th March 2006 storm
was overestimated by 198% which is quite high whereas that of 21st April 2006 was overestimated by
very low percentage of 10%. The rest of the storms were underestimated with lowest at 17% (14th
May 2006). Figure 4-19 shows a plot with error bars whose value is 6.56 mm (standard error of the
derived function).
0
2
4
6
8
10
12
14
16
18
20
1 2 3 4 5
Storms
Tota
l Ra
infa
ll (m
m)
Observed
Estimated
Figure 4-19: Observed and Estimated total rainfall plotted with the error bars
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
55
Furthermore relating the observed and the estimated for these five storms, a relation in the form of:
Observed Rainfall = 0.5777*Estimated Rainfall +2.8847, was obtained.
y = 0.5777x + 2.8847
R2 = 0.3615
0
2
4
6
8
10
12
14
0 2 4 6 8 10 12
Estimated rainfall (mm)
Ob
se
rve
d r
ain
fall (
mm
)
Figure 4-20: Relationship between the observed and the estimated total rainfall
Goodness of fit (R2) of approximately 0.36 was obtained in this relationship. This shows that there is
low correlation between observed total rainfall and that estimated using the derived regression.
However, considering the approach as enumerated above, all the estimates can be said to be nearly the
same as the observed total rainfall.
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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57
5. Discussions of Results
This chapter discusses in detail results of the simple cloud mask and rainfall estimation as obtained in
the previous chapter. Various results of previous studies are compared to some results of the current
study.
5.1. Cloud Mask Results
The simple cloud mask (SCM) algorithm has been checked and validated using the EUMETSAT
cloud mask products. The choice of this algorithm for validation was based on the fact that
EUMETSAT validation procedure of their cloud mask was more realistic since they use database that
is automatically built and is collocated with the MSG satellite data and that of surface observations.
The surface data used are hourly weather observations, coded by observers into the World
Meteorological Organization (WMO) synoptic code (SYNOP). In addition meteorological
information extracted from the French NWP model Action de Recherche Petite Echelle Grande
Echelle (ARPEGE) forecast fields is used. Based on these facts, it can be concluded that EUMETSAT
cloud mask algorithm as developed by Météo-France is more robust as compared to other cloud
algorithms for MSG satellite images. The current study was not able to collect such enormous data for
validation thus made use of EUMETSAT products.
These products are available from EUMETSAT in GRIdded Binary (GRIB2) format. GRIB is a World
Meteorological Organization’s (WMO’s) standard binary format for exchanging gridded data. The
raw data from EUMETSAT was imported into ILWIS after gluing the six segments (provided in the
original EUMETCast data stream) of MSG satellite field of view that are provided. In ILWIS a
procedure to cross check the accuracy of the cloud mask developed in this study is given in the flow
chart (figure 5-1) below.
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Figure 5-1: Flow chart on segmentation and visualization of EUMETSAT CLM and SCM
EUMETSAT CLM raw data received at ITC was processed to check on the accuracy of the developed
simple cloud mask (SCM) in this study. The EUMETSAT CLM of MSG of 26th December 2006 at
15:00 UTC is given in figure 5-2. GRIB2 import routine developed at ITC was used to convert the
data and appropriate classes were assigned manually. These were named as; cloud, clear land, water,
and background. In addition GRIB2 import routine assigns the proper geometric model and therefore
the cloud mask can be directly integrated with other processed results.
EUMETSAT CLM
(GRIB2)
Simple Cloud Mask
(SCM)
Reclassification
Mask clouds only
Sub-map study area Sub-map study area
Segmentation of
cloud mask
Segmentation of
cloud mask
Overlay onto a VIS/NIR false
colour composite image
VISUALIZATION
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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59
Figure 5-2: EUMETSAT cloud mask assigned feature classes for 26th December 2006 at 15:00 UTC
The clouds were selected and a small window (≈ 11°N - 14°S and ≈ 6° - 51°E), covering a part of the
tropics over the African continent considered in this study in developing the SCM, was extracted.
Segmentation was done for both SCM and EUMETSAT CLM. Both were overlaid onto a false colour
composite of the same day and time (e.g. 26th Dec 2006 at 15:00 UTC) and figure 5-3 shows the
results of these overlays.
Figure 5-3: Cloud mask segments of EUMETSAT CLM (yellow lines) and SCM (red lines) for 26th December
2006 at 15:00 UTC, on a false colour composite
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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60
From figure 5-3 it can be seen that the segments of the EUMETSAT CLM and those of SCM match in
some areas and mismatch for other areas. Detection of clouds from the developed algorithm will be
assumed to be accurate when the two lines exactly overlaid on each other in this visualization
procedure. It can be seen that over the Indian Ocean, the SCM did not extract majority of the cloudy
pixels. This could indicate that the climatological SST has to be reviewed either to use daily data
instead of the monthly used in this study.
However, the other technique used to evaluate the accuracy of the SCM was based on creating
confusion (contingency) matrix. This method compares all pixels, within a selected window, to find
out whether the pixels are assigned as cloudy or non-cloudy in both masks.
Here the two cloud masks images were crossed to built a contingency table that indicates the number
of pixels in each category. This will show the ability of the SCM to detect cloudy and non-cloudy
events based on EUMETSAT CLM. In order to get better results of accuracy of the SCM, there is a
need to use a number of images. Since the SCM algorithm was developed based on different solar
illumination conditions, it was appropriate to choose MSG images based on these three conditions.
Here four days images were used for validation and their contingency matrices are as given in the
following tables 5-1 to 5-4 for the specified day and time.
Table 5-1: Contingency table for MSG image of 25th December 2006 at 12:00 UTC
Table 5-2: Contingency table for MSG image of 26th December 2006 at 15:00 UTC
EUMETSAT CLM (Number of pixels)
Cloudy Not cloudy Total Error of
commission (%)
Cloudy 648922 56295 705217 8.0
Not cloudy 73981 415852 489833 15.1
Total 722903 472147 1195050
Sim
ple
cl
ou
d
ma
sk
(SC
M)
(Nu
mb
er
of
pix
els)
Error of
Omission
(%)
10.2 11.9 Overall Accuracy: 89.1 %
EUMETSAT CLM (Number of pixels)
Cloudy Not cloudy Total Error of
commission (%)
Cloudy 587923 97384 685307 14.2
Not cloudy 35360 472169 507529 7.0
Total 623283 569553 1192836
Sim
ple
cl
ou
d
ma
sk
(SC
M)
(Nu
mb
er
of
pix
els)
Error of
Omission
(%)
5.7
17.1
Overall Accuracy: 88.9 %
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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61
Table 5-3: Contingency table for MSG image of 4th January 2007 at 22:00 UTC
Table 5-4: Contingency table for MSG image of 10th January 2007 at 17:00 UTC
From these tables it can be seen that cloud mask of 25th December 2006 at 12:00 UTC, 26th December
2006 at 15:00 UTC and that of 4th January 2007 at 22:00 UTC had the highest accuracies of 89.1%,
88.9% and 88.0% respectively. In the same cloud masks only 10.2%, 5.7% and 7.8% (respectively) of
total number of pixels detected by EUMETSAT, were not detected by the SCM. This can be termed
as the cloud failure score or underestimation of cloudy events. On 10th January 2006 at 17:00 UTC,
there was relatively higher cloud failure of 15.7%. This could be associated with non-detection of low
clouds or thin, semi-transparent broken clouds at night over both the land and the sea. On this day
SCM also depicted a slightly lower overall accuracy of 83.3%.
On 25th December 2006 at 12:00 UTC (day-time), 8.0% of total number of cloudy pixels from the
SCM was not under cloudy conditions as per the EUMETSAT CLM. This is very low as compared to
other days where the total number of pixels assigned cloudy were almost double; 14.2% (26th
December 2006 at 15:00 UTC), 16.7% (4th January 2007 at 22:00 UTC), and 22.5 % (10th January
2006 at 17:00 UTC). This implies that the day-time algorithm was able to differentiate the cloudy and
non-cloudy pixels to a greater accuracy as compared to the night-time and twilight time algorithms.
Besides, only 7.0% and 7.1% of the EUMETSAT cloudy pixels were assigned non-cloudy by the
SCM for 26th December 2006 at 15:00 UTC and 4th January 2007 at 22:00 UTC, respectively. This
was low as compared to the SCM of 25th December 2006 at 12:00 UTC and 10th January 2007 at
17:00 UTC, which assigned EUMETSAT CLM cloudy pixels as non-cloudy at 15.1% and 12.0%,
respectively. This could be associated to the use of NWP models by EUMETSAT which is likely to
model the more variable day atmospheric profile to a greater accuracy. This is not possible with the
simple thresholds used in this study and thus the high difference in assigning cloudy pixels to non-
cloudy despite good results in overall accuracy of 89.1% for the day SCM.
EUMETSAT CLM (Number of pixels)
Cloudy Not cloudy Total Error of
commission (%)
Cloudy 500360 100566 600926 16.7
Not cloudy 42209 549701 591910 7.1
Total 542569 650267 1192836
Sim
ple
cl
ou
d
ma
sk
(SC
M)
(Nu
mb
er
of
pix
els)
Error of
Omission
(%)
7.8
15.5
Overall Accuracy: 88.0 %
EUMETSAT CLM (Number of pixels)
Cloudy Not cloudy Total Error of
commission (%)
Cloudy 419299 121656 540955 22.5
Not cloudy 78193 575902 654095 12.0
Total 497492 697558 1195050
Sim
ple
cl
ou
d
ma
sk
(SC
M)
(Nu
mb
er
of
pix
els)
Error of
Omission
(%)
15.7
17.4
Overall Accuracy: 83.3 %
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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62
Generally underestimation could be occurring over the sea areas since climatological SST used was
monthly mean SST. Use can be made of 5-, 7-, or daily- mean SST which most likely would improve
the accuracies.
During day-time (25th December 2006 at 12:00UTC), SCM overestimated cloudy events by only
11.9%, which is slightly lower than the other times. It is likely that this low failure is due to thresholds
used. Use of NWP model by EUMETSAT to compute some thresholds for twilight time and night-
time seems to improve the accuracies during these times and hence the higher differences from the
SCM. Better results of the day-time cloud mask could be associated with the fact that convective
activities could be present and thus easy to screen the clouds based on the thresholds used in this
study.
Moreover, the period selected here is when the Inter-tropical Convergence Zone (ITCZ) is generally
within the region under consideration and therefore convective activities, with low presence of thin
and semi-transparent cirrus clouds, are common. On 26th December 2006 at 15:00UTC (twilight time),
4th January 2007 at 22:00UTC (night-time), and 10th January 2007 at 17:00UTC (twilight time),
overestimation score was 17.1%, 15.5%, and 17.4%, respectively.
In general terms and considering the four situations, the overall accuracy of the study is 87.3%. This
indicates that SCM performed well and thus the simple thresholds used were able to extract cloudy
pixels as intended. It is worthy noting that the time mentioned here refers to the defined solar
illumination conditions that are occurring over most part of the selected region.
The overall accuracies obtained in this study may depict good performance of the SCM algorithm
developed. However, on superimposing the EUMETSAT CLM on a false colour composite showed
that not all cloudy pixels were correctly screened. Thus a more robust validation method or cloud
mask algorithm may be sought.
5.2. Cloud Height/Type Results
An attempt was made to validate the simple cloud height/type (SCH/T) algorithm using the
EUMETSAT cloud top height (CTH) products also available from EUMETSAT and accessed through
EUMETCast. EUMETSAT CTH products are based on sea surface whereas SCH/T products are
based on the earth surface. Thus digital elevation model (DEM) products, sourced from
ftp://edcftp.cr.usgs.gov/pub/data/gtopo30/global (USGS, 2007) were used to compute the
EUMETSAT CTH products based on the earth surface. Examples of the EUMETSAT cloud top
height and SCH/T images for 25th December 2006 at 11:45 UTC, for a part of African tropical region
and Atlantic Ocean, are given in figure 5-4.
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
63
Figure 5-4: EUMETSAT CTH (a), SCH/T (b), and Difference (between CTH and SCH/T) (c) images for 25th
December 2006 at 11:45 UTC (height is in meters)
The results show significant differences in cloud heights for those pixels assigned cloudy by both
EUMETSAT cloud mask products and by the SCM algorithm. The difference image (figure 5-4 (c)) is
also provided. Various other days EUMETSAT CTH products were investigated and the same high
differences were obtained.
However, based on figure 2-1, it can be seen that the EUMETSAT CTH products might be too high.
Cloud height computed using the SCH/T algorithm appears to provide estimates which may be
realistic based on the same figure 2-1. Thus validation with the EUMETSAT CTH products may not
provide reliable results. Further validation of simple cloud height/type algorithm was not carried.
5.3. Rainfall Estimation Results
Understanding that rainfall estimation from one-dimensional cloud-based model technique lacks a
strong physical basis, it was essential to estimate total amount of rainfall from individual storms. This
method provided an idea of how much rainfall a cloud at a certain height can produce. Although this
approach has limitations given the assumptions used (e.g. wind shear over the station does not
(a) (b)
(c)
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
64
strongly affect the storm), it is possible to establish cloud height- rainfall relations that may be used as
first approximations. Thus the results would be more general than existing methods, so that the
technique would not be tied to one storm or one climatological regime or one synoptic situation.
As given in figure 4-14, rainfall intensities were plotted against cloud height classes and the clusters
indicates that cloud height between 4000m and 5500m produced rainfall of significant intensities.
Total rainfall from storms can also be seen from figure 4-18 to depict the same trend where high
amounts are within cloud heights of above 4000m and below 5500m.
Diurnal trends (see figure 4-13 and 4-17) of cloud heights and rainfall intensity and/or total rainfall
are rather interesting. They showed that rainfall occurred at the station when the cloud height was
higher than 3000m. This is by no means a coincidence of results of the two days selected for
investigating trends which again confirm the results as shown in figures 4-14 and 4-18.
However, despite clear relationship depicted by the direct cloud height – rainfall intensity plots,
estimated rainfall intensities for other storms using the derived function, was way above the observed
intensities at the station as can be seen from figure 4-16.
From statistical analysis, between observed and estimated total rainfall from other five storms,
correlation coefficient of 0.96, root mean square error of 3.72mm, and a skill score index of 0.23 were
obtained. (Laurent et al., 1998) pointed out that the non-dimensional skill score index, as here applied,
is the relative distance between the estimated values and the observed values and it depends on the
standard deviation (error) of the observed data. Skill score is equal to one when the estimates are
perfect and equal to zero when there is best constant estimates. The skill score obtained here indicates
the estimates were not perfect and that is confirmed from the values as can be seen from table 4-2.
Results presented here are for only a few storms and do not depict general results of all storm
situations that may occur over the station. However, they may give a first approximation of rainfall
amount expected from storms that occur at a certain height. As Heinemann, (2003) pointed out, one of
the major difficulties in relating precipitation observed at a ground station and measured satellite
signals is that the amount of precipitation reaching the ground depends very much on the structure of
the atmospheric layer under the precipitating cloud. This can be said to aggravate the error in the
estimates since the atmospheric layers below the precipitating clouds are not modelled in this study to
incorporate them.
It should also be noted that twelve storms were used to derive the regression function. This may not
be enough to derive regression that may be representative of all types of storms that may occur over
this station. There is a need to use more storms in order to derive a representative regression function.
However, given the data available (from February to August 2006) this was not possible. Besides,
there were several days with no precipitation occurrence given that during this period there was only
one rainfall season over this region.
In order to check whether the results of comparing diurnal change of total rainfall and storm height
were mere coincidence, rainfall data from and independent station were investigated. Data from
Ministry of Water and Irrigation, Naivasha were used. Two days, one with long rainfall records and
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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65
0
1000
2000
3000
4000
5000
6000
0:30
3:30
6:30
9:30
12:30
15:30
18:30
21:30
Local time (Hrs)
Clo
ud
heig
ht
(m)
0
0.5
1
1.5
2
2.5
3
3.5
To
tal
rain
fall
(m
m)
Cloud height
Total rainfall
0
500
1000
1500
2000
2500
3000
3500
4000
4500
0:30
3:30
6:30
9:30
12:30
15:30
18:30
21:30
Local time (Hrs)
Clo
ud
heig
ht
(m)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
To
tal
rain
fall (
mm
)
Total rainfall
Cloud height
the other without rainfall were chosen. Here the day with rainfall records was 1st March 2006 and the
day without rainfall was 28th October 2006. Simple cloud height (SCH) algorithm was applied to
compute the cloud heights for the two days. Results to this are presented graphically in figure 5-5.
Figure 5-5: Diurnal height and Total rainfall changes on 1st March 2006 (left) and 28th October 2006 (right) over
Naivasha station
As can be observed, rainfall occurred generally when cloud height was slightly higher than 3000m
especially in the afternoon (the shaded part in the left graph). During this time the likely clouds over
the station are convective type of clouds which predominantly occur in the afternoon over this region.
Early in the morning, no rainfall was recorded even though the cloud height was slightly more than
3000m. These are likely to be cirrus clouds which mainly occur after dissipation of convective clouds.
Thus the convective clouds that produced rainfall in the afternoon and in the night must have
dissipated and cirrus clouds appeared in early morning.
The right graph of 28th October 2006 shows that cloud heights above 3000m occurred in the night and
there was no rainfall recorded on this day. This may be attributed to the fact that the rainfall was
measured at a point and that it may have rained away from the rain gauge.
The situation over CGIS station is slightly different as can be observed that on 5th May 2006 rainfall
occurred in the afternoon whereas on 10th May 2006 it occurred in the morning. This means that
convective clouds (the likely clouds producing this rainfall) over this region may be sustained at
various times during the 24 hours period. Nevertheless, the results of Naivasha station and those of
CGIS station are similar in that rainfall is observed when cloud height was more than 3000m.
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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6. Conclusions and Recommendations
6.1. Conclusions
The main objective is to develop simple cloud mask and height algorithms that can be used for further
studies. As enumerated some of the most important studies include; operational weather forecasting
and energy and water balance studies. Clouds represent the most significant source of error in the
extraction of earth surface energy and water balance parameters out of meteorological satellite data
(Valk et al., 1998). Energy and water balance models are used to estimate fluxes in cloudy conditions.
Thus the focus is to develop a simple cloud mask algorithm in order to be able to accurately develop
other algorithms.
Further to developing simple cloud mask and cloud height algorithms, rainfall estimation is a focus in
this study. Availability of satellite images based on thermal infrared bands is essential and is the first
focal point in this study. In addition to this is the importance of geospatial data on meteorological
parameters that are associated with formation of clouds. Various sources were explored in order to
obtain the long term climatological meteorological data, specifically temperature (minimum,
maximum, and mean).
Firstly, climatological data from the identified sources were used to process input data for the simple
cloud mask algorithm. Secondly, field work campaign was carried for collection of ground rainfall
data that was used for comparison with the processed cloud heights on various satellite images.
During simple cloud mask algorithm development, various thresholds (multi-spectral threshold
technique) were explored in order to optimise on extracting all clouds present on any particular day
and time.
Based on the developed simple cloud mask algorithm, the following comments were drawn:
� Setting thresholds for screening all cloudy pixels in satellite images is the most difficult part
in threshold techniques. The main problem is that the thresholds are functions of many
variables such as; surface type (land, ocean, ice), surface conditions (vegetation, soil
moisture), recent weather (which changes surface temperature and reflectance significantly),
atmospheric conditions (temperature inversions, haze, foggy), season, time of day and even
satellite-earth-sun geometry (hence bidirectional reflectance and sun glint) (Kidder and Haar,
1995).
� An automatic simple cloud mask algorithm has been presented ready for use in other
applications among them those interested in identification of cloudy pixels for the retrieval of
cloud-related parameters (e.g. cloud heights) especially those for clouds which contribute to
rainfall (e.g. cumulonimbus and nimbostratus). Additionally, exclusion of cloudy pixels for
further processing (if required) would be affected by the presence of pixels e.g. for land
surface, ocean colour and aerosol observations. Thus given its aim, a compromise between
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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68
calculation speed and accuracy of the results was necessary and use of only three channels of
MSG satellite aimed at developing the simple algorithms as per the study objective.
� The simplicity in the algorithm and significant accuracies based on EUMETSAT data thirsty
cloud mask algorithm, and the possibility of automation into shareware or freeware such as
ILWIS, may greatly improve cloud detection for specifically weather forecasting in most of
the African National Hydrometeorological Services (NHMS). Thus it was envisioned at the
developmental phase that this algorithm would be simple and physically sound and that the
MSG satellite imageries and the necessary processing tools (software e.g. ILWIS) would be
available in these NHMSs in Africa.
Based on the simple cloud height/type (SCH/T) algorithm developed and consequently comparison
with the observed rain gauge data, the following conclusions were drawn:
� That dew point temperature concept can be used to estimate cloud height which can thus be
used to infer rainfall observed on the earth surface. Despite empirical formulation in obtaining
geospatial dew point temperature and replication from a different region (USA Northern
Great Plains), high correlations when comparing rain gauge observations and processed cloud
heights have been obtained.
� That satellite convective rainfall estimation schemes using thermal infrared data depend on
empirically-derived relations between satellite-observed clouds and rainfall. Worse still,
derived relationship from one specific location or climatological regime is not replicable to
another and thus general low correlations between satellite data and rain gauge observations.
� That deriving a concrete regression function for rainfall estimation may be rather difficult
from simple data inputs such as cloud height or even cloud top temperature. This may require
complex model of high computational strength in order to be able to estimate rainfall to a
reasonable accuracy. Besides, earlier studies have shown that unless for strong convection,
there is low correlation between VIS/IR features and precipitation. This is the same reason as
to why rainfall estimates from the derived regression function in this study were of low
accuracies since not all cases were conclusively discerned as convective activities.
� That there is always need for spatial and temporal averaging of satellite data in order to get
better results while comparing point observations on the earth surface
Nevertheless, the author is aware that the small area considered for the validation of the cloud mask
algorithm may not entirely reflect the overall accuracy of the algorithm. However, this gives an
indication of the expected results for specifically equatorial Africa.
6.2. Recommendations
Regarding the research methods and ability to improve in the simple cloud mask (SCM) and cloud
height/type (SCH/T) algorithms, further research can be considered as follows:
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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� Improving on thresholds tests based on different cloud microphysical processes on formation
of cloud particles.
� Improving on threshold tests based on variables such as surface type, surface conditions,
recent and prevailing weather conditions, and atmospheric conditions.
� Recalibration or deriving a relationship between dew point temperature and readily available
meteorological data e.g. minimum, maximum, and mean temperatures, as suggested by
Hubbard et al., (2003) for any region under consideration.
On the part of rainfall estimation/comparison method, further research can be considered as follows:
� Improving on rainfall estimation scheme by determining the environment of the convection
(in cases where rainfall is assumed to emanate from convective activities) in terms of
temperature, moisture and wind shear.
� Developing a model that is characterised by the significant transience, heterogeneity, and
variability to associate rainfall with the extremely complex and yet imperfectly understood
precipitating processes in order to produce higher quality estimates as suggested by Hong et
al., (2004).
The SCM and SCH/T algorithms seem to work well, but they will benefit a lot from a more thorough
validation method. SYNOP data could be used for this purpose. However, as stated by Casanova et
al., (2004) subjectivity of the meteorological observer, which in most cases depends on expertise as
well as experience, is an issue while using SYNOP as a validation method.
Last but not least, final recommendations for development of cloud mask and cloud height algorithms
would be that; having observed that earth surface features dictates setting of threshold tests, there is a
need to develop cloud mask algorithm that will consider all these features. In addition, it is
recommended that rainfall estimation/comparison method be based on accurately known cloud
formation processes in order to integrate with one-dimensional physical cloud models.
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
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information. International Journal of Remote Sensing, 23(20): 4247-4261.
Stowe, L.L., Davis, P.A. and McClain, E.P., 1999. Scientific basis and initial evaluation of the
CLAVR-1 global clear cloud classification algorithm for the advanced very high resolution
radiometer. Journal of Atmospheric and Oceanic Technology, 16(6): 656-681.
Strahler, A.N., 1965. Introduction to Physical Geography. John Wiley & Sons, Inc., New
York.London.Sydney.65-12699
Turk, F.J., Bidwell, S.W., Smith, E.A. and Mugnai, A., 2003. Investigating Inter-Satellite Calibration
for the GPM ERA, 12th Conference on Satellite Meteorology and Oceanography.
USGS, 2007.GTOPO30 Documentation, ftp://edcftp.cr.usgs.gov/pub/data/gtopo30/global, February
2007
Valk, P.d., Feijt, A.J., Roozekrans, H., Roebeling, H. and Rosema, A., 1998. Operationalisation of an
algorithm for the automatic detection and characterisation of clouds in METEOSAT imagery.
BCRS Report 1998: USP-2: NRSP-2: NUSP, Beleidscommissie Remote Sensing (BCRS).
Wu, X., Menzel, W.P. and Wande, G.S., 1999. Estimation of sea surface temperatures using GOES-
8/9 radiance measurements. Bulletin of the American Meteorological Society, 80(6): 1127-
1138.
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Appendices
Appendix A: ILWIS Script for Simple Cloud Mask and Height Algorithms
// STEP 1: DATA IMPORT //
// Edit batch file to select date and time for thermal bands (Band IR_039, Band IR_108 and Band
//IR_120, imported in Kelvin)
! import_t.bat
! import_tt.bat
! import_ttt.bat
! import_tttt.bat
// Edit batch file to select date and time for visual bands (band VIS006, Band VIS008 and Band
//VIS016, imported as 8 bit)
//Only used for visual inspection of the segments that result from processing cloud heights
! import_v.bat
// Filling the undefined pixels in thermal band 4 (IR_039) by setting minimum brightness
//temperature at 204K
T1_bd39:=iff((T1_band_1<204),204,T1_band_1)
T1_band_1:=ifundef(T1_bd39,204)
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T2_bd39:=iff((T2_band_1<204),204,T2_band_1)
T2_band_1:=ifundef(T2_bd39,204)
T3_bd39:=iff((T3_band_1<204),204,T3_band_1)
T3_band_1:=ifundef(T3_bd39,204)
T4_bd39:=iff((T4_band_1<204),204,T4_band_1)
T4_band_1:=ifundef(T4_bd39,204)
//Assigning domain values of original TIR images for each band to avoid undefines
T1_band1{dom=value;vr=0:1000:0.0001}:=T1_band_1.mpr
T2_band1{dom=value;vr=0:1000:0.0001}:=T2_band_1.mpr
T3_band1{dom=value;vr=0:1000:0.0001}:=T3_band_1.mpr
T4_band1{dom=value;vr=0:1000:0.0001}:=T4_band_1.mpr
T1_band2{dom=value;vr=0:1000:0.0001}:=T1_band_2.mpr
T2_band2{dom=value;vr=0:1000:0.0001}:=T2_band_2.mpr
T3_band2{dom=value;vr=0:1000:0.0001}:=T3_band_2.mpr
T4_band2{dom=value;vr=0:1000:0.0001}:=T4_band_2.mpr
T1_band3{dom=value;vr=0:1000:0.0001}:=T1_band_3.mpr
T2_band3{dom=value;vr=0:1000:0.0001}:=T2_band_3.mpr
T3_band3{dom=value;vr=0:1000:0.0001}:=T3_band_3.mpr
T4_band3{dom=value;vr=0:1000:0.0001}:=T4_band_3.mpr
//Map list with assigned domains is created
crmaplist TR1 T1_band1 T1_band2 T1_band3
crmaplist TR2 T2_band1 T2_band2 T2_band3
crmaplist TR3 T3_band1 T3_band2 T3_band3
crmaplist TR4 T4_band1 T4_band2 T4_band3
// Taking temporal average //
AvgT.mpl = maplistcalculate("(@1+@2 +@3 +@4)/4",0,2,TR1.mpl,TR2.mpl,TR3.mpl,TR4.mpl )
open AvgT.mpl
// GENERATION OF SATELLITE AND SUN ANGLES //
//Calculate satellite, sun zenith angle and sun elevation, for MSG projection
! generateangles.bat
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copy satzen*.* msgzen
copy sunzen*.* solzen
msgzen:=map('msgzen',genras,Convert,378,0,Real,4,SwapBytes)
solzen:=map('solzen',genras,Convert,378,0,Real,4,SwapBytes)
setgrf msgzen.mpr angle
setgrf solzen.mpr angle
msg_zenres.mpr{dom=value.dom;vr=-1000:1000:0.001}:= MapResample(msgzen.mpr,%8,bicubic)
sec_msgzen.mpr{dom=value.dom;vr=0:10:0.001}:=(1/(cos(degrad(msg_zenres))))-1
sol_zenres.mpr{dom=value.dom;vr=-1000:1000:0.001}:= MapResample(solzen.mpr,%8,bicubic)
sec_solzen.mpr{dom=value.dom;vr=0:10:0.001}:=(1/(cos(degrad(sol_zenres))))-1
sun_elev.mpr{dom=value.dom;vr=-1000:1000:0.001}:=90-sol_zenres
// Calculating solar illumination conditions
illum_cond%7.mpr{dom=sol_cond}:=iff(sun_elev<-3,"night",iff(sun_elev<10,"twilight","day"))
// Delete unused files generated in the process of generating angles
!del_file.bat
// Boolean images created to be used in multiplying resulting images in every step
Day:=iff((sun_elev>10),1,0)
Night:=iff((sun_elev<-3),1,0)
Twilight:=iff((sun_elev<10) and (sun_elev>-3),1,0)
//Aggregating (spatial averaging) the retrieved TIR images//
Avg.mpl:=MapListApplic(AvgT, MapAggregateAggFnc(##, 5, nogroup))
open Avg.mpl
closeall
//STEP 2: CLOUD MASKING //
// (1) DAY TIME CLOUD MASK //
// Dew point calculation (temperature layers input in Kelvin)
// Formula according to Hubbard, Mahmood and Carlson (2003): Estimating daily dew point
// temperature for the northern Great Plains using maximum and minimum temperature, Agron J.
//95:323-328 (2003)
// Td=-0.0360 (T-mean) +0.96789(T-min) +0.0072(T-max-T-min) +1.0119 (in degree Celsius)
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Td_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-
273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Night
Td_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-
273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Twilight
Td_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-
273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Day
// Mean T_b of band 10.8 and band 12.0 (input in Kelvin)
b_mean_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Night
b_mean_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Twilight
b_mean_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Day
// Standard deviation of the climatological maps (t_nightall2f, t_dayall2f, and t_meanall2f)
t_std.mpr{dom=VALUE.dom;vr=-10000000.0:10000000.0:0.1}= MapMaplistStatistics(t_clim.mpl,
Std, 0, 2)
// Derive cloud mask according to the MSG algorithm in the Météo-France, Atlantic Sea Surface
//temperature product manual, Version 1.5, Nov 2005 (SAF/OSI/M-F/TEC/MA/121)
Ts_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_D-273.15)+(1.18116*
(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Night
Ts_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_D-273.15)+(1.18116*
(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Twilight
Ts_D.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_D-273.15)+(1.18116*
(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Day
// Cloud mask using fine scale climatology as in SAF/OSI/M-F/TEC/MA/121
deltat_D.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-Ts_D)*Night
deltat_D.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-
Ts_D)*Twilight
deltat_D.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-Ts_D)*Day
// Pixels with small difference (i.e. deltat_D) are masked
Sc_Clim.mpr{dom=BOOL.dom;vr=0:1} = iff((deltat_D>-1)or(deltat_D>0.5)or(deltat_D>2),1,0)
Sc_Clim1.mpr{dom=BOOL.dom;vr=0:1} =Sc_Clim*Night
Sc_Clim1.mpr{dom=BOOL.dom;vr=0:1} =Sc_Clim*Twilight
Sc_Clim1.mpr{dom=BOOL.dom;vr=0:1} =Sc_Clim*Day
CL_1.mpr{dom=BOOL.dom;vr=0:1} = iff((%4_2<293.15) and (Sc_Clim1>0),1,0)
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CL_1_1.mpr{dom=BOOL.dom;vr=0:1} =CL_1*Night
CL_1_1.mpr{dom=BOOL.dom;vr=0:1} =CL_1*Twilight
CL_1_1.mpr{dom=BOOL.dom;vr=0:1} =CL_1*Day
CL_2.mpr{dom=BOOL.dom;vr=0:1} = iff((CL_1>0)and(%4_2<(%2-(t_std/2))),1,0)
CL_2_2.mpr{dom=BOOL.dom;vr=0:1} = CL_2*Night
CL_2_2.mpr{dom=BOOL.dom;vr=0:1} = CL_2*Twilight
CL_2_2.mpr{dom=BOOL.dom;vr=0:1} = CL_2*Day
cloud_mask_D.mpr{dom=BOOL.dom;vr=0:1} = MapFilter(CL_2_2.mpr,MAJORITY.fil)
// (2) NIGHT TIME CLOUD MASK //
// Dew point calculation (temperature layers input in Kelvin)
// Formula according to Hubbard, Mahmood and Carlson (2003): Estimating daily dew point
// temperature for the northern Great Plains using maximum and minimum temperature, Agron J.
//95:323-328 (2003)
// Td=-0.0360 (T-mean)+0.96789(T-min)+0.0072(T-max-T-min)+1.0119 (in degree Celsius)
Td_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-
273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Day
Td_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-
273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Twilight
Td_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-
273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Night
// Mean T_b of band 10.8 and band 12.0 (input in Kelvin)
b_mean_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Day
b_mean_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Twilight
b_mean_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Night
// Standard deviation of the climatological maps (t_nightall2f, t_dayall2f, and t_meanall2f)
t_std.mpr{dom=VALUE.dom;vr=-10000000.0:10000000.0:0.1}= MapMaplistStatistics(t_clim.mpl,
Std, 0, 2)
// Derive cloud mask according to the MSG algorithm in the Météo-France, Atlantic Sea Surface
//temperature product manual, Version 1.5, Nov 2005 (SAF/OSI/M-F/TEC/MA/121)
Ts_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_N-273.15)+(1.18116*
(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Day
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Ts_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_N-273.15)+(1.18116*
(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Twilight
Ts_N.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_N-273.15)+(1.18116*
(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Night
// Cloud mask using fine scale climatology as in SAF/OSI/M-F/TEC/MA/121
deltat_N.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_meanall2f-Ts_N)*Day
deltat_N.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01}=(t_meanall2f-
Ts_N)*Twilight
deltat_N.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_meanall2f-Ts_N)*Night
// Step A: Land Surface cloud mask only, <t_std greater than 1>
// Cloud mask adaptation step A: iff IR_108<283.15 Kelvin (10 degrees Celsius) in combination with
//positive values from the previous step results
// larger threshold for <deltat_N> is used to exclude cooler areas
CL_N1.mpr{dom=BOOL.dom;vr=0:1}:=iff(((%6>1)and((b_mean_N<283.15)and
(deltat_N>10))),1,0)
CL_NN1.mpr{dom=BOOL.dom;vr=0:1}:=CL_N1*Day
CL_NN1.mpr{dom=BOOL.dom;vr=0:1}:=CL_N1*Twilight
CL_NN1.mpr{dom=BOOL.dom;vr=0:1}:=CL_N1*Night
// Step B: temperature threshold sea surface only <t_std less than 1> and difference between
//T-min and actual IR_108 temperature greater than 9 Kelvin
CL_N2.mpr{dom=BOOL.dom;vr=0:1} = iff((%6<1)and((%1-%4_2)>9),1,0)
CL_NN2.mpr{dom=BOOL.dom;vr=0:1} =CL_N2*Day
CL_NN2.mpr{dom=BOOL.dom;vr=0:1} =CL_N2*Twilight
CL_NN2.mpr{dom=BOOL.dom;vr=0:1} =CL_N2*Night
// Step C: Low cloud mask over the sea (Normalizing IR_108 and IR_120 with IR_039 by multiplying
//temperature of the two bands) results in scaled values whose difference with the mean of IR_108 and
//IR_120 indicates presence of low clouds and (dif>2K).
ratall_N.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Day
ratall_N.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Twilight
ratall_N.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Night
difnew_N.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_N-b_mean_N)*Day
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difnew_N.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_N-b_mean_N)*Twilight
difnew_N.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_N-b_mean_N)*Night
CL_N3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=iff((%6<1)and((difnew_N)>2),1,0)
CL_NN3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=CL_N3*Day
CL_NN3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=CL_N3*Twilight
CL_NN3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=CL_N3*Night
// Add all corrected cloud masks from previous steps
CL_N4.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=CL_NN1+CL_NN2+CL_NN3
// Transform to boolean map
CL_N5{dom=bool}:=iff(CL_N4>0,1,0)
// Final cloud mask is being filtered using 3x3 majority filter removing individual pixels and assigning
//1 or 0 (true - false) domain
cloud_mask_N.mpr{dom=BOOL.dom;vr=0:1} = MapFilter(CL_N5.mpr,MAJORITY.fil)
// (3) TWILIGHT TIME CLOUD MASK //
// Dew point calculation (temperature layers input in Kelvin)
// Formula according to Hubbard, Mahmood and Carlson (2003): Estimating daily dew point
// temperature for the northern Great Plains using maximum and minimum temperature, Agron J. //
//95:323-328(2003)
// Td=-0.0360 (T-mean)+0.96789(T-min)+0.0072(T-max-T-min)+1.0119 (in degree Celsius)
Td_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-
273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Night
Td_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-
273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Day
Td_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=(-0.036*(%3-273.15)+0.9679*(%1-
273.15)+(0.0072*((%2-273.15)-(%1-273.15)))+1.0119+273.15)*Twilight
// Mean T_b of band 10.8 and band 12.0 (input in Kelvin)
b_mean_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Night
b_mean_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Day
b_mean_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=((%4_2+%4_3)/2)*Twilight
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// Standard deviation of the climatological maps (t_nightall2f, t_dayall2f, and t_meanall2f)
t_std.mpr{dom=VALUE.dom;vr=-10000000.0:10000000.0:0.1} = MapMaplistStatistics(t_clim.mpl,
Std, 0, 2)
// Derive cloud mask according to the MSG algorithm in the Météo-France, Atlantic Sea Surface
//temperature product manual, Version 1.5, Nov 2005 (SAF/OSI/M-F/TEC/MA/121)
Ts_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_T-273.15)+(1.18116*
(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Night
Ts_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_T-273.15)+(1.18116*
(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Day
Ts_T.mpr{dom=value.dom;vr=0:1000:0.0001}:=(0.98826*(b_mean_T-273.15)+(1.18116*
(sec_msgzen-1)+0.07293*(%1-273.15))*(%4_2-%4_3)+1.10718+0.2+273.15)*Twilight
// Cloud mask using fine scale climatology as in SAF/OSI/M-F/TEC/MA/121
deltat_T.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-Ts_T)*Night
deltat_T.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-Ts_T)*Day
deltat_T.mpr{dom=VALUE.dom;vr=-10000000.00:10000000.00:0.01} = (t_nightall2f-
Ts_T)*Twilight
// Step A: deltat has positive values for clouds and negative values if no clouds
// Land cloud mask only, <t_std greater than 1>
//cloud mask adaptation step A: iff IR_108<283.15 Kelvin (10 degrees Celsius) in combination with
//positive values from the previous step results in clouds
// Medium threshold for <deltat_T> is used to exclude cooler areas
CL_T1.mpr{dom=BOOL.dom;vr=0:1}=iff(((%6>1)and((b_mean_T<283.15)and (deltat_T>5))),1,0)
CL_TT1.mpr{dom=BOOL.dom;vr=0:1} = CL_T1*Night
CL_TT1.mpr{dom=BOOL.dom;vr=0:1} = CL_T1*Day
CL_TT1.mpr{dom=BOOL.dom;vr=0:1} = CL_T1*Twilight
// Step B: temperature threshold sea surface only <t_std less than 1> and difference between
//T-min and actual IR_108 temperature greater than 9 Kelvin
CL_T2.mpr{dom=BOOL.dom;vr=0:1} = iff((%6<1)and((%1-%4_2)>9),1,0)
CL_TT2.mpr{dom=BOOL.dom;vr=0:1} = CL_T2*Night
CL_TT2.mpr{dom=BOOL.dom;vr=0:1} = CL_T2*Day
CL_TT2.mpr{dom=BOOL.dom;vr=0:1} = CL_T2*Twilight
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// Step C: Low cloud mask over the sea (Normalizing IR_108 and IR_120 with IR_039 by multiplying
//temperature of the two bands) results in scaled values whose difference with the mean of IR_108 and
//IR_120 indicates presence of low clouds and (dif<2K).
ratall_T.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Night
ratall_T.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Day
ratall_T.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=((%4_2*%4_3)/%4_1)*Twilight
difnew_T.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_T-b_mean_T)*Night
difnew_T.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_T-b_mean_T)*Day
difnew_T.mpr{dom=value;vr=-1000:1000:0.0001}:=(ratall_T-b_mean_T)*Twilight
CL_T3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=iff((%6<1)and((difnew_T)<2),1,0)
CL_TT3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:= CL_T3*Night
CL_TT3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:= CL_T3*Day
CL_TT3.mpr{dom=value.dom;vr=-1000:1000:0.0001}:= CL_T3*Twilight
// Add all corrected cloud masks from previous steps
CL_T4.mpr{dom=value.dom;vr=-1000:1000:0.0001}:=CL_TT1+CL_TT2+CL_TT3
// Transform to boolean map
CL_T5{dom=bool}:=iff(CL_T4>0,1,0)
// Final cloud mask is being filtered using 3x3 majority filter removing individual pixels and assigning
//1 or 0 (true - false) domain
cloud_mask_T.mpr{dom=BOOL.dom;vr=0:1} = MapFilter(CL_T5.mpr,MAJORITY.fil)
// STEP 3: COMBINING ALL MASKED CLOUDS //
Day_clouds.mpr{dom=BOOL.dom;vr=0:1} = iff(illum_cond%7="day",cloud_mask_D,?)
Night_clouds.mpr{dom=BOOL.dom;vr=0:1} = iff(illum_cond%7="night",cloud_mask_N,?)
Twilight_clouds.mpr{dom=BOOL.dom;vr=0:1}= iff(illum_cond%7="twilight",cloud_mask_T,?)
// Total clouds in the field of view
Final_clouds.mpr{dom=BOOL.dom;vr=0:1}=
iff((Day_clouds>0)or(Night_clouds>0)or(Twilight_clouds>0),1,0)
// Clouds at different times of the day
Final_clouds_D.mpr{dom=BOOL.dom;vr=0:1} = Final_clouds*Day
Final_clouds_N.mpr{dom=BOOL.dom;vr=0:1} = Final_clouds*Night
Final_clouds_T.mpr{dom=BOOL.dom;vr=0:1} = Final_clouds*Twilight
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
82
// STEP 4: CLOUD HEIGHT CALCULATION //
// (1) DAY TIME //
// For the pixels assigned clouds the cloud height is derived:
// using the wet adiabatic lapse rate (0.6 degree per 100 meter): dew point temperature - average
//IR_108 and IR_120 temperature * 0.6 *100
// using dry adiabatic lapse rate (1 degree per 100 meter): T-max - dew point temperature * 100
Height_D.mpr{dom=value.dom;vr=0:30000}:=iff(Final_clouds_D=true,(((Td_D-
b_mean_D)*0.6)*100)+((%2-Td_D)*100),?)
// Calculate cloud height above 50m to avoid false assignment in low levels
Height_Dab50.mpr{dom=VALUE.dom;vr=-10000000:10000000}= iff(Height_D>50,Height_D,?)
// Transform the heights computed into different height classes
Height_CLS_D.mpr{dom=Final_cls}:=iff(Height_Dab50<1500,"Low
clouds",iff(Height_Dab50<3000,"Middle clouds","High clouds"))
// (2) NIGHT TIME //
// For the pixels assigned clouds the cloud height is derived,
// using the wet adiabatic lapse rate (0.6 degree per 100 meter): dew point temperature - average
//IR_108 and IR_120 temperature * 0.6 *100
// using dry adiabatic lapse rate (1 degree per 100 meter): T-max - dew point temperature * 100
Height_N.mpr{dom=value.dom;vr=0:30000}:=iff(Final_clouds_N=true,(((Td_N-
b_mean_N)*0.6)*100)+((%2-Td_N)*100),?)
// Calculate cloud height above 50m to avoid false assignment in low levels
Height_Nab50.mpr {dom=VALUE.dom;vr=-10000000:10000000}= iff(Height_N>50,Height_N,?)
// Transform the heights computed into different height classes
Height_CLS_N.mpr{dom=Final_cls}:=iff(Height_Nab50<1500,"Low
clouds",iff(Height_Nab50<3000,"Middle clouds","High clouds"))
// (3) TWILIGHT TIME //
// For the pixels assigned clouds the cloud height is derived,
// using the wet adiabatic lapse rate (0.6 degree per 100 meter): dew point temperature - average
//IR_108 and IR_120 temperature * 0.6 *100
// using dry adiabatic lapse rate (1 degree per 100 meter): T-max - dew point temperature * 100
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
83
Height_T.mpr{dom=value.dom;vr=0:30000}:=iff(Final_clouds_T=true,(((Td_T-
b_mean_T)*0.6)*100)+((%2-Td_T)*100),?)
// Calculate cloud height above 50m to avoid false assignment in low levels
Height_Tab50.mpr{dom=VALUE.dom;vr=-10000000:10000000}= iff(Height_T>50,Height_T,?)
// Transform the heights computed into different height classes
Height_CLS_T.mpr{dom=Final_cls}:=iff(Height_Tab50<1500,"Low
clouds",iff(Height_Tab50<3000,"Middle clouds","High clouds"))
// STEP 5: GLUING THE HEIGHT IMAGES TOGETHER //
Height_%9_%7.mpr{dom=VALUE.dom;vr=-10000000:10000000}:=
MapGlue(Height_Dab50,Height_Nab50,Height_Tab50)
// STEP 6: GLUING THE DIFFERENT HEIGHT CLASSES TOGETHER//
Height_cls_glued%9_%7.mpr{dom=Final_cls.dom}=MapGlue(Height_CLS_D,Height_CLS_N,
Height_CLS_T)
// STEP 7: SEGMENTATION OF THE GLUED HEIGHT CLASSES //
Final_cloud_seg%9_%7.mps:=SegmentMapFromRasAreaBnd(Height_Cls%9_%7,8,Smooth,unique)
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
84
Appendix B: Samples of Batch Files
! import_t.bat (Batch file for the first image)
Similarly:
! import_tt.bat (Batch file for the second image)
D:\MSGDataRetriever\gdal_translate.exe --config GDAL_CACHEMAX 30 -projwin -2643355.2 3759505.2
4707632.6 -3681494.7 -of ILWIS MSG(\\Pc2133-24002\Rawdata20051101\,200603071515,(4,9,10),N,T,1,1)
D:\MSG_CGIS_Avgt_s\T2
! import_ttt.bat (Batch file for the third image)
D:\MSGDataRetriever\gdal_translate.exe --config GDAL_CACHEMAX 30 -projwin -2643355.2 3759505.2
4707632.6 -3681494.7 -of ILWIS MSG(\\Pc2133-24002\Rawdata20051101\,200603071500,(4,9,10),N,T,1,1)
D:\MSG_CGIS_Avgt_s\T3
! import_tttt.bat (Batch file for the fourth image)
D:\MSGDataRetriever\gdal_translate.exe --config GDAL_CACHEMAX 30 -projwin -2643355.2 3759505.2
4707632.6 -3681494.7 -of ILWIS MSG(\\Pc2133-24002\Rawdata20051101\,200603071445,(4,9,10),N,T,1,1)
D:\MSG_CGIS_Avgt_s\T4
! import_v (Batch file for the visible bands-1, 2, and 3)
D:\MSGDataRetriever\gdal_translate.exe --config GDAL_CACHEMAX 30 -projwin -2643355.2 3759505.2
4707632.6 -3681494.7 -of ILWIS MSG(\\Pc2133-24002\Rawdata20051101\,200603071530,(1,2,3),N,B,1,1)
D:\MSG_CGIS_Avgt_s\V
! generateangles.bat (Batch file for generating satellite and solar angles - works in java
environment)
java -cp .;operation.jar;Jama-1.0.1.jar;numericalMethods.jar AngleMaps 2006 03 07 15.30
! del_file.bat (Batch file for deleting unused files generated by the generate angles batch file)
del sataz*.*
del sunaz*.*
del satzen*.*
del sunzen*.*
del msgzen
del solzen
Projection
window in
the MSG
field of
view
External
drive where
the data is
located
Year-month-
date-time
(yyyymmdd
hhmm)
Bands 4,
9, and 10
Folder to which
the data is to be
stored and the
name of data
file
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-SITU OBSERVATIONS
85
Appendix C: Sample of CGIS Weather Station Data
TIM
ES
TA
MP
(Lo
cal
Tim
e)
(H
rs)
RE
CO
RD
RN
TA
_A
vg
deg
C
Av
g
RH
_A
vg
%
Av
g
VP
_A
vg
kP
a
Av
g
RIS
_A
vg
W/m
2
Av
g
PA
R_
T_
Av
g
my
mo
l/(m
2*
s)
Av
g
PA
R_
D_
Av
g
my
mo
l/(m
2*
s)
Av
g
Su
n_
stat
e_A
vg
Fra
ctio
n
Av
g
WS
_M
ax
m/s
Max
WS
_M
in
m/s
Min
WS
_W
Vc(
1)
m/s
WV
c
WS
_
WV
c(
2)
m/s
WV
c
P_
To
t
mm
To
t
Bat
t_v
olt
_M
in
Min
PT
emp
Sm
p
2/27/2006
17:30 96 21.35 60.57 1.539 108.9 240.1 219.8 0 0 0 0 0 0 13.8 24.1
2/27/2006
18:00 97 21.02 61.23 1.524 42.34 100.6 94.9 0 0 0 0 0 0 13.14 22.7
2/27/2006
18:30 98 20.64 63.02 1.532 5.221 23.79 20.57 0 0 0 0 0 0 12.99 21.15
2/27/2006
19:00 99 19.96 68.36 1.594 0.013 12.23 12.81 0 0 0 0 0 0.4 12.95 19.84
2/27/2006
19:30 100 19.54 70.03 1.59 0.04 12.8 14.03 0 0 0 0 0 0 12.94 18.92
2/27/2006
20:00 101 19.11 72.5 1.603 0.067 12.44 12.24 0 0 0 0 0 0 12.92 18.12
2/27/2006
20:30 102 18.84 72.78 1.583 -0.027 15.33 14.49 0 0 0 0 0 0 12.91 17.54
2/27/2006
21:00 103 18.88 72.66 1.584 0.027 14.78 14.59 0 0 0 0 0 0 12.9 17.21
2/27/2006
21:30 104 18.57 77.07 1.648 0 14.26 13.99 0 0 0 0 0 0 12.89 16.91
2/27/2006
22:00 105 17.79 82 1.67 0.054 14.19 13.73 0 0 0 0 0 0 12.88 16.59
2/27/2006
22:30 106 17.67 82.6 1.669 0.027 15.19 14.97 0 0 0 0 0 0 12.87 16.29
2/27/2006
23:00 107 17.81 81 1.651 0.107 13.32 14.43 0 0 0 0 0 0 12.86 16.09
2/27/2006
23:30 108 18.01 78.32 1.616 0.013 14.75 15.04 0 0 0 0 0 0 12.86 15.97
2/28/2006
0:00 109 17.79 79.7 1.622 -0.013 14.95 14.01 0 0 0 0 0 0 12.85 16
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-SITU OBSERVATIONS
86
2/28/2006
0:30 110 18.2 76.65 1.601 -0.067 15.16 14.18 0 0 0 0 0 0 12.85 16.29
2/28/2006
1:00 111 17.86 79.39 1.623 0.013 14.33 14.5 0 0 0 0 0 0 12.85 16.46
2/28/2006
1:30 112 17.4 82.1 1.631 0.121 14.47 14.45 0 0 0 0 0 0 12.85 16.39
2/28/2006
2:00 113 17.36 81.6 1.618 0.08 14.46 14.29 0 0 0 0 0 0 12.84 16.32
2/28/2006
2:30 114 17.47 80.1 1.597 -0.067 13.86 14.08 0 0 0 0 0 0 12.83 16.24
2/28/2006
3:00 115 17.09 83.9 1.634 0.027 14.62 14.37 0 0 0 0 0 0 12.83 16.19
2/28/2006
3:30 116 16.87 85.1 1.634 0.013 14.53 14.54 0 0 0 0 0 0 12.83 15.97
2/28/2006
4:00 117 16.62 86.1 1.626 0.121 13.95 14.48 0 0 0 0 0 0 12.83 15.65
2/28/2006
4:30 118 16.93 82.8 1.596 0.054 14.36 14.02 0 0 0 0 0 0 12.83 15.24
2/28/2006
5:00 119 16.31 87.6 1.623 0.054 14.5 14.42 0 0 0 0 0 0 12.82 15.04
2/28/2006
5:30 120 16.35 86.6 1.609 0 14.38 13.97 0 0 0 0 0 0 12.82 14.9
2/28/2006
6:00 121 16.1 88.4 1.616 0.201 14.47 14.45 0 0 0 0 0 0 12.82 14.78
2/28/2006
6:30 122 15.84 88.2 1.585 13.28 31.1 29.36 0 0 0 0 0 0 12.81 14.44
2/28/2006
7:00 123 16.5 83.9 1.573 51 114.3 97.9 0 0 0 0 0 0 12.83 14.66
2/28/2006
7:30 124 16.84 83.8 1.606 114 237 226.6 0 0 0 0 0 0 13.07 15.65
2/28/2006
8:00 125 18.12 80.9 1.682 149.1 317.3 314.4 0 0 0 0 0 0 13.61 17.14
2/28/2006
8:30 126 19.57 73.67 1.675 261.1 534.9 492.8 0 0 0 0 0 0 13.92 19.42
2/28/2006
9:00 127 20.9 67.47 1.667 453.9 892 703.1 0 0 0 0 0 0 13.84 22.87
2/28/2006 128 21.03 64.53 1.607 264.1 562.3 530.3 0 0 0 0 0 0 13.82 23.93
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-SITU OBSERVATIONS
87
9:30
2/28/2006
10:00 129 21.44 61.33 1.566 399 810 737 0 0 0 0 0 0 13.79 24.92
2/28/2006
10:30 130 21.37 66.43 1.689 280 587 569.9 0 0 0 0 0 0 13.79 25.22
2/28/2006
11:00 131 20.24 75.69 1.791 180.1 385.9 382.4 0 0 0 0 0 0 13.79 24.3
2/28/2006
11:30 132 19.2 81.9 1.823 194.1 419.4 412.3 0 0 0 0 0 0 13.82 23.24
2/28/2006
12:00 133 20.09 78.51 1.845 281.3 593.3 581.4 0 0 0 0 0 0 13.84 23.24
2/28/2006
12:30 134 21.64 64.22 1.658 527.5 1080 1052 0 0 0 0 0 0 13.79 25.28
2/28/2006
13:00 135 22.9 59 1.646 748.5 1518 1133 0 0 0 0 0 0 13.74 27.59
2/28/2006
13:30 136 23.95 54.99 1.635 697 1389 1163 0 0 0 0 0 0 13.69 29.92
2/28/2006
14:00 137 23.11 58.73 1.66 251.5 519.8 509.6 0 0 0 0 0 0 13.68 29.22
2/28/2006
14:30 138 21.9 63.13 1.655 73.24 179.5 175.2 0 0 0 0 0 0 13.7 26.47
2/28/2006
15:00 139 19.27 82.4 1.833 19.57 66.67 63.91 0 0 0 0 0 1.2 13.03 23.21
2/28/2006
15:30 140 15.67 95 1.69 9.81 42.47 40.45 0 0 0 0 0 4.8 12.98 19.97
2/28/2006
16:00 141 15.12 95.5 1.64 16.3 56.4 54.61 0 0 0 0 0 2 12.97 17.99
2/28/2006
16:30 142 15.49 92.8 1.631 39.47 101.4 99.7 0 0 0 0 0 0.2 13.01 17.01
2/28/2006
17:00 143 16.14 88.5 1.623 40.14 94.9 93.8 0 0 0 0 0 0.2 13.16 16.54
2/28/2006
17:30 144 16.58 85 1.603 34.44 79.12 78.91 0 0 0 0 0 0 13.12 16.24
2/28/2006
18:00 145 16.94 84.4 1.628 18.81 45.97 43.65 0 0 0 0 0 0 12.94 16.17
2/28/2006
18:30 146 17.04 85.8 1.666 1.863 14.62 15.17 0 0 0 0 0 0 12.9 16.12
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-SITU OBSERVATIONS
88
2/28/2006
19:00 147 17.02 87.9 1.703 0.027 14.99 14.01 0 0 0 0 0 0 12.88 16.04
2/28/2006
19:30 148 17.27 86.4 1.7 0.147 14.02 14.64 0 0 0 0 0 0 12.88 15.97
2/28/2006
20:00 149 17.79 82.5 1.681 0.04 14.75 14.37 0 0 0 0 0 0 12.87 15.97
2/28/2006
20:30 150 17.73 81.3 1.648 -0.054 15 16.19 0 0 0 0 0 0 12.86 15.92
2/28/2006
21:00 151 17.9 76.9 1.577 0.067 13.3 14.58 0 0 0 0 0 0 12.86 15.75
2/28/2006
21:30 152 17.79 76.48 1.557 0.04 14.52 14.2 0 0 0 0 0 0 12.85 15.75
2/28/2006
22:00 153 17.79 76.28 1.552 -0.013 14.52 14.29 0 0 0 0 0 0 12.85 15.82
2/28/2006
22:30 154 18 73.94 1.525 0.013 13.71 14.33 0 0 0 0 0 0 12.84 15.9
2/28/2006
23:00 155 18.14 71.57 1.49 0.04 14.14 14.66 0 0 0 0 0 0 12.84 16.04
2/28/2006
23:30 156 18.05 70.54 1.46 0.094 14.41 14.51 0 0 0 0 0 0 12.83 16.02
3/1/2006
0:00 157 17.93 71.16 1.461 -0.027 14.62 14.21 0 0 0 0 0 0 12.83 16.09
3/1/2006
0:30 158 18.13 70.02 1.456 0 14.73 14.22 0 0 0 0 0 0 12.83 16.32
3/1/2006
1:00 159 17.56 75.27 1.509 0.08 13.62 14.11 0 0 0 0 0 0 12.83 16.17
3/1/2006
1:30 160 16.64 81.1 1.535 -0.013 14.66 14.55 0 0 0 0 0 0 12.82 15.75
3/1/2006
2:00 161 16.21 86.1 1.585 0.054 14.65 13.93 0 0 0 0 0 0 12.82 15.41
3/1/2006
2:30 162 15.62 92.6 1.642 0 13.96 14.47 0 0 0 0 0 0 12.82 15.02
3/1/2006
3:00 163 15.27 95.2 1.65 0.147 14.23 13.97 0 0 0 0 0 0 12.81 14.68
3/1/2006
3:30 164 15.36 94.6 1.649 0.013 14.53 13.94 0 0 0 0 0 0 12.81 14.52
3/1/2006 165 15.56 93.8 1.656 0.013 15.27 15.3 0 0 0 0 0 0 12.8 14.47
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-SITU OBSERVATIONS
89
4:00
3/1/2006
4:30 166 15.49 93.4 1.643 -0.08 13.57 13.34 0 0 0 0 0 0 12.8 14.47
3/1/2006
5:00 167 15.49 93.3 1.641 0.054 14.36 14.74 0 0 0 0 0 0 12.8 14.52
3/1/2006
5:30 168 15.43 94.1 1.648 0.08 14.46 14.42 0 0 0 0 0 0 12.79 14.56
3/1/2006
6:00 169 15.38 94.7 1.654 0.121 14.6 13.49 0 0 0 0 0 0 12.79 14.56
3/1/2006
6:30 170 15.27 94.8 1.644 5.817 19.05 18.71 0 0 0 0 0 0 12.79 14.44
3/1/2006
7:00 171 15.38 94.7 1.655 26.39 54.58 52.65 0 0 0 0 0 0 12.8 14.49
3/1/2006
7:30 172 15.79 93.7 1.68 60.65 118.5 116.2 0 0 0 0 0 0 12.93 14.88
3/1/2006
8:00 173 16.53 91 1.71 135.4 264.5 262.8 0 0 0 0 0 0 13.32 15.8
3/1/2006
8:30 174 16.94 88.8 1.714 190 398.3 390.8 0 0 0 0 0 0 13.86 17.01
3/1/2006
9:00 175 17.29 86.3 1.702 247.5 515 508.2 0 0 0 0 0 0 13.95 18.33
3/1/2006
9:30 176 17.65 87.9 1.773 318.5 655.5 651.1 0 0 0 0 0 0 13.92 19.76
3/1/2006
10:00 177 18.01 85.6 1.768 291.7 606.1 601.2 0 0 0 0 0 0 13.9 20.88
3/1/2006
10:30 178 18.49 82 1.743 454.8 919 881 0 0 0 0 0 0 13.87 22.22
3/1/2006
11:00 179 20.36 70.09 1.673 789.5 1567 1090 0 0 0 0 0 0 13.8 25.34
3/1/2006
11:30 180 20.86 67.35 1.66 700.8 1396 1027 0 0 0 0 0 0 13.74 27.69
3/1/2006
12:00 181 20.83 65.85 1.62 483.4 966 949 0 0 0 0 0 0 13.73 27.82
3/1/2006
12:30 182 21.21 64.31 1.62 422.9 845 841 0 0 0 0 0 0 13.74 27.53
3/1/2006
13:00 183 21.57 66.74 1.718 602.2 1224 1047 0 0 0 0 0 0 13.74 28.01
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-SITU OBSERVATIONS
90
3/1/2006
13:30 184 22.75 61.11 1.689 923 1855 1223 0 0 0 0 0 0 13.69 30.05
3/1/2006
14:00 185 23.47 58.13 1.68 633.3 1285 1182 0 0 0 0 0 0 13.66 30.95
3/1/2006
14:30 186 23.7 55.84 1.636 603.3 1272 1091 0 0 0 0 0 0 13.65 31.51
3/1/2006
15:00 187 23.74 55.36 1.626 560.7 1183 1019 0 0 0 0 0 0 13.65 31.62
3/1/2006
15:30 188 22.81 65.4 1.816 314.9 660.1 560.1 0 0 0 0 0 0 13.65 30.46
3/1/2006
16:00 189 22.24 66.99 1.796 111.4 242.3 235.6 0 0 0 0 0 0 13.69 28.01
3/1/2006
16:30 190 20.77 73.3 1.792 18.06 46.11 44.32 0 0 0 0 0 1.6 13.01 25.22
3/1/2006
17:00 191 17.1 81.6 1.591 13.25 42.1 40.68 0 0 0 0 0 0.2 12.98 21.26
3/1/2006
17:30 192 16.91 89.1 1.716 20.23 58.35 55.64 0 0 0 0 0 0 12.97 19.36
3/1/2006
18:00 193 17.29 85.7 1.689 11.29 40.74 38.16 0 0 0 0 0 0 12.93 18.33
3/1/2006
18:30 194 17.63 78.76 1.587 2.384 15.68 16.8 0 0 0 0 0 0 12.91 17.59
3/1/2006
19:00 195 17.22 83.1 1.63 0.067 16.11 18.32 0 0 0 0 0 0 12.9 16.94
3/1/2006
19:30 196 17.06 83.5 1.623 0.04 13.28 14.89 0 0 0 0 0 0 12.88 16.51
3/1/2006
20:00 197 17.34 80.3 1.589 0.121 14.81 13.92 0 0 0 0 0 0 12.87 16.36
3/1/2006
20:30 198 17.56 78.97 1.584 0.054 14.37 14.47 0 0 0 0 0 0 12.86 16.24
3/1/2006
21:00 199 17.27 81.6 1.608 -0.04 14.08 14.99 0 0 0 0 0 0 12.85 16
3/1/2006
21:30 200 16.92 84.4 1.625 0.027 14.32 14.58 0 0 0 0 0 0 12.85 15.7
3/1/2006
22:00 201 17.14 83 1.621 -0.067 14.29 14.63 0 0 0 0 0 0 12.84 15.48
3/1/2006 202 16.54 85.6 1.61 0.067 13.88 13.61 0 0 0 0 0 0 12.83 15.07
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-SITU OBSERVATIONS
91
22:30
3/1/2006
23:00 203 15.97 89 1.614 0.04 14.38 14.45 0 0 0 0 0 0 12.83 14.68
3/1/2006
23:30 204 16.35 85.6 1.59 0.013 14.37 13.99 0 0 0 0 0 0 12.82 14.4
3/2/2006
0:00 205 16.64 84.1 1.592 0.013 14.18 15.23 0 0 0 0 0 0 12.82 14.3
3/2/2006
0:30 206 16.34 86.2 1.601 -0.04 14.18 12.82 0 0 0 0 0 0 12.81 14.13
3/2/2006
1:00 207 16.45 84.7 1.585 0.107 14.6 14.58 0 0 0 0 0 0 12.81 14.01
3/2/2006
1:30 208 16 87.8 1.595 0.067 14.21 14.58 0 0 0 0 0 0 12.8 13.9
3/2/2006
2:00 209 15.93 87.5 1.582 0.08 14.38 14.05 0 0 0 0 0 0 12.79 13.82
3/2/2006
2:30 210 15.35 91.2 1.589 0.054 14.25 14.21 0 0 0 0 0 0 12.79 13.66
3/2/2006
3:00 211 14.97 92.7 1.577 0.08 14.46 14.48 0 0 0 0 0 0 12.79 13.42
3/2/2006
3:30 212 14.5 94.5 1.56 0.067 13.67 13.93 0 0 0 0 0 0 12.79 13.19
3/2/2006
4:00 213 14.83 92.9 1.566 0.013 15 14.55 0 0 0 0 0 0 12.78 13.05
3/2/2006
4:30 214 14.49 94.9 1.565 0.054 14.06 14.12 0 0 0 0 0 0 12.78 12.98
3/2/2006
5:00 215 14.47 94.2 1.551 0.08 14.55 14.66 0 0 0 0 0 0 12.78 12.98
3/2/2006
5:30 216 14.44 93.2 1.531 -0.027 13.94 13.38 0 0 0 0 0 0 12.78 12.77
3/2/2006
6:00 217 14.1 94.9 1.526 0.255 14.42 14.09 0 0 0 0 0 0 12.77 12.56
3/2/2006
6:30 218 13.87 94.9 1.504 15.79 35.93 33.38 0 0 0 0 0 0 12.77 12.42
3/2/2006
7:00 219 14.64 89.4 1.487 124.7 180.3 148.2 0 0 0 0 0 0 12.85 13
3/2/2006
7:30 220 16.91 80.5 1.55 267.4 467.5 311 0 0 0 0 0 0 13.6 15.46
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
92
Appendix D: Storms over CGIS Weather Station used for Developing Cloud
height – Rainfall Intensity Regression Function
Storm 1 (28/02/2006: 1330-1600UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
5217.5 5000-5500 5250 6.0
5219.3 5000-5500 5250 6.8
5022.2 5000-5500 5250 2.2
4665.5 4500-5000 4750 0.4
4350.8 4000-4500 4250 0.2
Storm 2 (07/03/2006: 1530-1930UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
3962.9 3500-4000 3750 0.8
5204.6 5000-5500 5250 2.4
5815.7 >5500 5750 4.0
5951.3 >5500 5750 4.2
5910.5 >5500 5750 12.4
5523.7 >5500 5750 11.0
4853.8 4500-5000 4750 0.8
4272.8 4000-4500 4250 0.4
Storm 3 (07/03/2006: 2230 - 08/03/2006:0200UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
5842.1 >5500 5750 0.2
5751.2 >5500 5750 1.4
5585.6 >5500 5750 2.6
5390.3 5000-5500 5250 2.6
5123.9 5000-5500 5250 3.0
4838.9 4500-5000 4750 3.0
4689.2 4500-5000 4750 2.8
4656.5 4500-5000 4750 1.4
Storm 4 (01/04/2006: 2100 – 02/04/2006: 0500UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
2882.0 2500-3000 2750 0.4
3455.6 3000-3500 3250 1.0
3672.8 3500-4000 3750 6.2
3974.3 3500-4000 3750 6.4
4235.3 4000-4500 4250 1.8
4724.9 4500-5000 4750 2.4
4935.5 4500-5000 4750 3.8
4822.1 4500-5000 4750 6.0
4565.9 4500-5000 4750 5.8
4241.3 4000-4500 4250 4.4
4102.1 4000-4500 4250 5.6
3899.6 3500-4000 3750 6.8
3616.1 3500-4000 3750 9.6
3491.6 3000-3500 3250 11.4
3212.6 3000-3500 3250 7.8
2849.9 2500-3000 2750 3.6
2540.3 2500-3000 2750 1.0
Storm 5 (20/04/2006:1200 – 1230UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
2931.7 2500-3000 2750 4.0
4022.0 4000-4500 4250 5.8
Storm 6 (05/05/2006: 1730 - 2000UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
4061.0 4000-4500 4250 2.8
4306.4 4000-4500 4250 6.4
4894.7 4500-5000 4750 10.6
5154.5 5000-5500 5250 23.8
Storm 7 (05/05/2006: 2030 - 2230UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
5045.0 5000-5500 5250 38.4
4767.5 4500-5000 4750 51.0
4499.9 4000-4500 4250 38.2
4337.3 4000-4500 4250 10.8
4208.9 4000-4500 4250 3.2
Storm 8 (10/05/2006:0200 – 0600UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
4453.1 4000-4500 4250 1.0
4501.1 4500-5000 4750 3.6
4407.8 4000-4500 4250 23.0
4398.5 4000-4500 4250 45.6
4682.3 4500-5000 4750 44.6
4988.6 4500-5000 4750 25.8
4888.1 4500-5000 4750 12.0
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
93
4704.5 4500-5000 4750 10.4
4854.8 4500-5000 4750 9.6
Storm 9 (10/05/2006:0630 – 0930UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
4904.9 4500-5000 4750 14.2
4824.1 4500-5000 4750 21.0
4920.4 4500-5000 4750 33.8
4820.8 4500-5000 4750 16.2
4773.5 4500-5000 4750 5.2
4674.8 4500-5000 4750 1.4
4615.4 4500-5000 4750 0.2
Storm 10 (12/05/2006:1400 – 1900UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
5708.0 >5500 5750 2.6
5774.0 >5500 5750 18.6
5754.7 >5500 5750 26.8
5392.4 5000-5500 5250 12.8
4957.1 4500-5000 4750 3.8
4954.1 4500-5000 4750 3.4
5003.9 5000-5500 5250 3.6
4752.8 4500-5000 4750 2.8
4561.1 4500-5000 4750 1.2
4469.6 4000-4500 4250 0.6
4281.5 4000-4500 4250 0.2
Storm 11 (14/05/2006:1900 – 2130UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
5779.3 >5500 5750 0.8
5417.3 5000-5500 5250 8.8
5571.1 >5500 5750 9.4
5302.6 5000-5500 5250 3.6
5365.9 5000-5500 5250 2.8
Storm 12 (16/05/2006:1400 – 1500UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
2522.9 2500-3000 2750 0.2
2653.1 2500-3000 2750 2.6
3014.6 3000-3500 3250 2.6
5680.0 >5500 5750 0.6
Storm 13 (22/07/2006:0430 – 0630UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
2513.6 2500-3000 2750 0.4
2513.9 2500-3000 2750 1.0
2407.1 0.8
2387.0 0.4
2512.7 2500-3000 2750 0.2
Storm 14 (05/08/2006:1400 – 1630UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
6151.7 >5500 5750 0.4
6176.3 >5500 5750 7.6
5906.3 >5500 5750 10.8
5588.9 >5500 5750 3.4
5325.2 5000-5500 5250 1.2
5150.6 5000-5500 5250 0.4
Storm 15 (06/08/2006:0100 – 0430UTC)
Height Height Class R/ Intensity
(m) Class centre (mm/hr)
2430.1 0.8
2494.6 6.2
2630.8 2500-3000 2750 6.6
2766.7 2500-3000 2750 2.4
2799.1 2500-3000 2750 2.6
2758.6 2500-3000 2750 2.4
2631.4 2500-3000 2750 1.2
2538.1 2500-3000 2750 0.2
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
94
Appendix E: Storms over CGIS Weather Station used for Developing Cloud
height – Total Rainfall Regression Function
Storm 1 (07/03/06: 1530-1930UTC)
Height Rainfall
(m) (mm)
3962.9 0.8
5204.6 1.6
5815.7 2.4
5951.3 1.8
5910.5 10.6
5523.7 0.4
4853.8 0.4
Avg H.: 5317.5
Total Rainfall: 18.0
Storm 2 (07/03/06: 2230 - 08/03/06:0200UTC)
Height Rainfall
(m) (mm)
5842.1 0.2
5751.2 1.4
5585.6 1.2
5390.3 1.4
5123.9 1.6
4838.9 1.4
4689.2 1.4
Avg H.: 5317.3
Total Rainfall: 8.6
Storm 3 (18/03/06: 1230 - 1530UTC)
Height Rainfall
(m) (mm)
2436.8 0.2
2453.5 1.4
2841.5 0.6
3416.0 0.2
3669.5 0.2
3628.7 0.2
Avg H.: 3074.3
Total Rainfall: 2.8
Storm 4 (27/03/06: 1700 - 2130UTC)
Height Rainfall
(m) (mm)
4495.1 0.8
3950.9 1.8
3165.2 1.0
2697.8 2.6
2766.2 2.6
2895.2 2.2
3194.3 0.4
3526.7 0.6
3562.7 0.2
Avg H.: 3361.6
Total Rainfall: 12.2
Storm 5 (01/04/06: 2100 – 02/04/06: 0500UTC)
Height Rainfall
(m) (mm)
2882.0 0.4
3455.6 0.6
3672.8 5.6
3974.3 0.8
4235.3 1.0
4724.9 1.4
4935.5 2.4
4822.1 3.6
4565.9 2.2
4241.3 2.2
4102.1 3.4
3899.6 3.4
3616.1 6.2
3491.6 5.2
3212.6 2.6
2849.9 1.0
Avg H.: 3917.6
Total Rainfall: 42.0
Storm 6 (14/04/06: 1530 – 1800UTC)
Height Rainfall
(m) (mm)
1774.7 0.2
2143.7 3.4
2791.1 0.6
3140.0 0.8
3221.9 0.2
Avg H.: 2614.3
Total Rainfall: 5.2
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
95
Storm 7 (05/05/06: 2030 - 2230UTC)
Height Rainfall
(m) (mm)
5045.0 21.6
4767.5 29.4
4499.9 8.8
4337.3 2.0
4208.9 1.2
Avg H.: 4571.7
Total Rainfall: 63.0
Storm 8 (10/05/06:0630 – 0930UTC)
Height Rainfall
(m) (mm)
4904.9 9.4
4824.1 11.6
4920.4 12.2
4820.8 4.0
4773.5 1.2
4674.8 0.2
Avg H.: 4819.8
Total Rainfall: 38.6
Storm 9 (12/05/06:1400 – 1900UTC)
Height Rainfall
(m) (mm)
5708.0 2.6
5774.0 16.0
5754.7 10.8
5392.4 2.0
4957.1 1.8
4954.1 1.6
5003.9 2.0
4752.8 0.8
4561.1 0.4
4469.6 0.2
Avg H.: 5132.8
Total Rainfall: 38.2
Storm 10 (20/07/06:2200 – 21/07/06:0130UTC)
Height Rainfall
(m) (mm)
2423.3 0.2
2415.5 0.2
2411.6 0.4
2429.9 0.6
2469.5 0.4
2481.5 0.2
2507.0 0.2
Avg H.: 2448.3
Total Rainfall: 2.2
Storm 11 (22/07/06:0430 – 0630UTC)
Height Rainfall
(m) (mm)
2513.6 0.4
2513.9 0.6
2407.1 0.2
2387.0 0.2
Avg H.: 2455.4
Total Rainfall: 1.4
Storm 12 (05/08/06:1400 – 1630UTC)
Height Rainfall
(m) (mm)
6151.7 0.4
6176.3 7.2
5906.3 2.6
5588.9 0.8
5325.2 0.4
Avg H.: 5829.7
Total Rainfall: 11.4
METEOSAT SECOND GENERATION (MSG) CLOUD MASK, CLOUD PROPERTY DETERMINATION AND RAINFALL COMPARISON WITH IN-
SITU OBSERVATIONS
96
Appendix F: Sample of Rain gauge (tipping bucket) Rainfall Data (Nairobi-
Dagoretti Meteorological Station)
10/22/06 17:46:22.5 11.684
10/22/06 17:47:30.0 11.938
10/22/06 17:49:28.0 12.192
10/22/06 17:51:35.5 12.446 10/22/06 17:53:20.5 12.7
10/22/06 17:56:16.5 12.954
10/22/06 18:09:55.5 13.208
10/22/06 18:19:16.5 13.462
10/22/06 18:20:42.0 13.716
10/22/06 18:20:42.5 13.97
10/22/06 18:21:39.0 14.224
10/22/06 18:22:12.0 14.478
10/22/06 18:22:45.0 14.732
10/22/06 18:23:53.0 14.986
10/22/06 18:27:24.0 15.24
10/22/06 18:27:24.5 15.494
10/22/06 18:28:02.0 15.748
10/22/06 18:28:02.5 16.002
10/22/06 18:30:54.5 16.256
10/22/06 18:30:55.0 16.51
10/22/06 18:31:31.0 16.764
10/22/06 18:32:28.5 17.018
10/22/06 18:32:51.0 17.272
10/22/06 18:33:08.0 17.526
10/22/06 18:33:28.5 17.78
10/22/06 18:33:46.5 18.034
10/22/06 18:34:00.5 18.288
10/22/06 18:34:17.0 18.542
10/22/06 18:34:37.5 18.796
10/22/06 18:34:38.0 19.05
10/22/06 18:34:56.0 19.304
10/22/06 18:34:56.5 19.558
10/22/06 18:35:17.0 19.812
10/22/06 18:35:17.5 20.066
10/22/06 18:35:36.0 20.32
10/22/06 18:35:36.5 20.574
10/22/06 18:36:02.0 20.828
10/22/06 18:36:18.5 21.082
10/22/06 18:36:52.5 21.336
10/23/06 13:29:57.0 21.59
Date/Time (Local) (Hrs) Accumulative Rainfall (mm)
10/13/06 11:28:47.0 0
10/13/06 23:41:31.5 0.254
10/13/06 23:43:13.0 0.508
10/13/06 23:44:13.0 0.762
10/13/06 23:49:10.0 1.016
10/13/06 23:56:54.5 1.27
10/13/06 23:58:27.5 1.524
10/14/06 00:00:57.5 1.778
10/14/06 00:02:32.5 2.032
10/14/06 00:03:43.0 2.286
10/14/06 00:07:18.0 2.54
10/14/06 00:14:23.5 2.794
10/14/06 00:14:24.0 3.048
10/14/06 00:37:49.0 3.302
10/14/06 00:42:19.0 3.556
10/14/06 00:42:19.5 3.81
10/14/06 00:44:26.0 4.064
10/14/06 00:47:29.5 4.318
10/14/06 00:49:02.0 4.572
10/14/06 00:50:46.5 4.826
10/17/06 02:41:33.0 5.08
10/17/06 02:41:33.5 5.334
10/17/06 02:42:09.0 5.588
10/17/06 09:00:02.5 5.842
10/17/06 09:01:56.0 6.096
10/17/06 09:01:56.5 6.35
10/17/06 09:03:04.0 6.604
10/17/06 09:03:04.5 6.858
10/17/06 09:10:43.0 7.112
10/17/06 09:11:13.5 7.366
10/17/06 09:12:23.5 7.62
10/17/06 09:14:05.5 7.874
10/17/06 09:22:19.5 8.128
10/17/06 09:22:20.0 8.382
10/17/06 09:25:48.0 8.636
10/21/06 06:54:16.0 8.89
10/22/06 16:43:23.5 9.144
10/22/06 16:43:24.0 9.398
10/22/06 16:47:09.5 9.652
10/22/06 17:36:14.0 9.906
10/22/06 17:37:00.0 10.16
10/22/06 17:37:00.5 10.414
10/22/06 17:38:34.0 10.668
10/22/06 17:43:36.0 10.922
10/22/06 17:45:09.5 11.176
10/22/06 17:45:49.0 11.43