Satellite remote sensing applications in Meteorology 2 nd Ewiem Nimdie International Summer School 2...

Satellite remote sensing applications in Meteorology

22ndnd EEwiem Nimdie International Summer Schoolwiem Nimdie International Summer School

Gizaw Mengistu, Dept. of Physics, Addis Ababa University, Ethiopia

1. Introduction Meteorological satellites Instrumentation

2. Retrieval of meteorological parameters

Measurement of sea and land surface temperature

Retrieval of vertical profiles of temperature and humidity

3. Measurement of rainfall Visible & Infra-red (IR)

Outline

4. Measurement of winds Cloud motion vectors (CMV)

5. Satellite image/signalo Satellite image/signal interpretation

Outline

1. Introduction

Meteorological satellites

Satellites instrumentation

Classification of Satellites

Satellite System

Sensor System

First application of satellite remote First application of satellite remote sensingsensing

Began with TIROS1, launched in April Began with TIROS1, launched in April 19601960

Simple TV system on board to map Simple TV system on board to map cloudsclouds

Satellites are now a vital an integral Satellites are now a vital an integral part of our weather forecasting part of our weather forecasting system.system.

Satellite remote sensingSatellite remote sensing

Now both polar orbiting and Now both polar orbiting and geostationary satellites are usedgeostationary satellites are used

Polar orbiters operate in a similar Polar orbiters operate in a similar way to other remote sensing way to other remote sensing satellites (Landsat, SPOT etc.)satellites (Landsat, SPOT etc.)

Geostationary satellites continually Geostationary satellites continually view the same portion of the Earth.view the same portion of the Earth.

Geostationary Satellites Geostationary Satellites

Orbit above the equator at 35,800 Km and complete Orbit above the equator at 35,800 Km and complete

one orbit every 24hrs.one orbit every 24hrs.

Remain over the same point on the surface of the Remain over the same point on the surface of the

Earth.Earth.

Continually view the same portion of the Earth.Continually view the same portion of the Earth.

A network provides coverage of the entire globeA network provides coverage of the entire globe


Major ApplicationsMajor Applications

Solar radiation exposureSolar radiation exposure

– Uses a model based on an advanced estimate of cloud Uses a model based on an advanced estimate of cloud

covercover

Cloud and Water Vapour Motion vectors Cloud and Water Vapour Motion vectors

– Tracks identifiable cloud featuresTracks identifiable cloud features

Entered into weather forecasting modelsEntered into weather forecasting models


Instrument Observing Characteristics

Observations depend on telescope characteristics (resolving

power, diffraction) detector characteristics (signal to noise) communications bandwidth (bit depth) spectral intervals (window, absorption

band) time of day (daylight visible) atmospheric state (T, Q, clouds) earth surface (Ts, vegetation cover)

Instrument Observing Characteristics

2. Retrieval of meteorological parameters

Measurement of sea and land surface temperature;

Retrieval of vertical profiles of temperature and humidity.

Radiance is the amount of energy/per unit time/per area of a detector/per spectral interval/per solid angle

Definition

Surface Temperature and Emissivity Estimation


The radiance at the sensor is given byLS j =[εjLj

BB(T)+(1-εj)Ljsky]*t+Lj

atm

Where εj is surface emissivity ,Lj

BB is spectral radiance of a blackbody at the surface at temperature T,Lj

sky is spectral radiance incident upon the surface from the Atmosphere, calculated using radiative transfer equation (e.g. MODTRAN etc),Lj

atm is spectral radiance emitted by the atmosphere, again from Model,t is spectral atmospheric transmission andLS j is spectral radiance observed by the sensor.

After getting all the necessary data from RT model as stated on the previous slide, radiance from the Surface, Lj, is:


Emission characteristics of different objects

Water is a Water is a good good approximatioapproximation of a black n of a black body (grey body (grey body)body)

Quartz is not Quartz is not a good a good approximatapproximation of a ion of a black body black body (selective (selective radiator)radiator)

Emission characteristics of different objects

Snow, vegetation, rock: spectra of mixed pixels

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4

wavelength, um

refl

ecta

nce

snow

vegetation

rock

equal snow-veg-rock

0.8 snow, 0.1 veg, rock

0.2 snow, 0.5 veg, 0.3 rock


Relative Emissivity (to the average of all channels, say 5 channels in this example) is given by

From laboratory measurement, relationship between minimum emissivity, max.-min. relative emissivity difference can be constructed i.e εmin=f(βmax- βmin). Therefore, the revised emissivity can be computed from

εj = βj(εmin/ βmin)


“Split-window” methods—atmospheric correction for surface temperature measurement

Water-vapor absorption in 10-12 m window is greater than in 3-5 m window Greater difference between TB (3.8 m) and

TB (11 m) implies more water vapor Enables estimate of atmospheric contribution

(and thereby correction) Best developed for sea-surface temperatures

Known emissivity Close coupling between atmospheric and

surface temperatures Liquid water is opaque in thermal IR, hence

instruments cannot see through clouds


Spectral radiance (LSj) data are acquired as the 8 or more bit gray-scale imagery in Level 1b products for most surface observing satellite. So, 8 or more bits digital number (DN) should be converted to radiance in order to apply TES algorithm outlined earlier. The equation and constants for converting the 8 bits digital number of the image data into the spectral radiance is as follows:

LSj=Gain*DN+Bias=DN*(Lmax-Lmin)/255+Bias

Picture elements of multispectral image

0

127

255

Brightness value range

(typically 8 bit)Associated gray-scale

10 15 17 20

15 16 18 21

17 18

20

22

18

20

22 24

1

2

3

4

1 5432Columns ( j)

Bands (k )

1

2

3

4

X axis Picture element (pixel) at location Line 4, Column 4, in Band 1 has a Brightness Value of 24, i.e., BV4,4,1 = 24 .

black

gray

white21

23

22

25

Lines or rows (i)

0

127

255

Brightness value range

(typically 8 bit)Associated gray-scale

10 15 17 20

15 16 18 21

17 18

20

22

18

20

22 24

1

2

3

4

1 5432Columns ( j)

Bands (k )

1

2

3

4

X axis Picture element (pixel) at location Line 4, Column 4, in Band 1 has a Brightness Value of 24, i.e., BV4,4,1 = 24 .

black

gray

white21

23

22

25

Lines or rows (i)

Image from different bands

.


Weighting function (http://goes.gsfc.nasa.gov/)

Weighting function

Retrieval of vertical profiles of temperature :demonstration

The preceding RT equation is commonly known as Fredholm integral equation of the first kind whose solution is difficult to find.

Consider the following example:

where the kernel is a simple exponential function


1. Assume the function f(x) is given by f(x)=x+4x(x-1/2)^2, we can compute g(k) for ki=(0 10) interval using

2. Write the integral equation in summation form:

Let , and compute g(k)


and compare with step 13. Use direct linear inversion method ((AT.A)^-1AT.g) to recover f(x)

4. If result in (3) is not good, use constrained inversion ((AT.A+gamma.H)^-1AT.g) where H is constraining matrix (smoothing matrix)

Stratospheric chemistry & Dynamics

Mengistu et. al., doi:10.1029/2004JD004856, 2004, doi:10.1029/2004JD005322, 2005

Mengistu et. al., doi:10.1029/2004JD004856, 2004, doi:10.1029/2004JD005322, 2005

3. Measurement of rainfall

Visible & Infra-red (IR)

Introduction

Geostationary satellites (e.g. GOES, GMS, Meteosat) typically carry infrared (IR) and visible (VIS) imagers with surface resolutions ranging from 1-4 km. NOAA polar orbiters carry VIS/IR imagers with 1 km resolution

The choice of polar-orbiting vs geostationary platforms for precipitation estimation entails several tradeoffs with regard to temporal and spatial sampling and geographical coverage: a geostationary satellite positioned over the equator can provide high frequency (hourly or better) images of a portion of the tropics and middle latitudes, while a polar orbiter provides roughly twice-daily coverage of the entire globe. Polar orbiters also fly in a low Earth orbit which is more suitable for the deployment of microwave imagers on account of the latter's coarse angular resolution.

Introduction

Spectral bands

The choice of spectral band for observing precipitation also involves tradeoffs. Historically, infrared (IR) and visible (VIS) imagery have been widely available for the longest period of time, with high quality microwave (MW) imagery becoming widely available only after the launch of the SSM/I in 1987.

Advantages of the VIS and IR bands include high spatial resolution as well as the possibility of frequent temporal sampling from geostationary platforms. A major disadvantage is the indirectness of the relationship between cloud top albedo or temperature and surface precipitation rate.

The evidence to date suggests that VIS/IR methods produce highly smoothed depictions of instantaneous rainfall fields which become useful only when averaged over larger space and/or time scales, and then only when carefully calibrated for the region and season in question.

Spectral bands

VIS and/or IR algorithms

Almost all IR techniques are based on variations of the premise that precipitation is most likely to be associated with deep clouds and thus with cold cloud tops, as observed by an infrared imager. Visible cloud albedos are generally used, if at all, as supplemental information to discriminate cold clouds which are optically thin and presumably non-precipitating from those which are optically thick and therefore possibly precipitating. Of course, visible imagery is only usable during the time that the sun is high above the horizon.

IR-only methods are often preferred for the simple reason that their performance is less likely to be a strong function of the time of day and therefore less likely to introduce spurious day-night biases in estimated precipitation.

Because rainfall usually occupies only a small fraction of the cold or bright cloud area visible from space, VIS/IR algorithms tend to overestimate significantly actual rain area. To avoid systematic overestimates in temporal or spatial averages, this tendency is usually accounted for by assigning very low rain rates (empirically derived) to the area identified as precipitating in the instantaneous images.

The GOES Precipitation Index (GPI) is one of the simplest and most widely used IR indices of precipitation in the tropics and subtropics. The GPI is computed by simply taking the fraction of pixels within a region whose IR brightness temperatures are less than some threshold, T0, and multiplying that fraction by a constant rain rate, RQ. Most commonly, T0 is taken to be 235°K and RQ is taken to be 3.0 mm/hr. However, other values for these parameters may be more appropriate in some cases, depending on location, season, spatial averaging scale and other factors.


The Negri-Adler-Wetzel Technique (NAWT) technique

NAWT assigns rain rates to "cloudy pixels" based on a threshold brightness temperature, T0, which was originally taken to be 253 °K but has been modified in more recent versions of the algorithm. Of the area defined as cloud, the coldest 10% is assigned a rain rate R10 and the next coldest 40% is assigned a lower rain rate R40. Values for R10 and R40 were originally specified as 8 and 2 mm/hr, respectively, but have been adjusted slightly in more recent applications.


RAINSAT is a supervised classification algorithm which is trained to identify areas of precipitation from a combination of VIS and IR imagery. At night, visible imagery is unavailable and RAINSAT reverts to a pure IR technique. RAINSAT and its relatives are among the few VIS/IR algorithms that are used operationally in middle latitudes.

Recently, a similar approach by using a multivariate classification scheme and raingauge data to estimate daily mean areal precipitation is proposed.


TAMSAT algorithm over Africao TAMSAT (Tropical Application of Meteorology

using Satellite and other data)o A regular series of thermal infrared (TIR) images

of an area is received, pixels with apparent temperatures lower than some predetermined threshold are classified as “cold cloud” and their charastristics accumulated over some period.

The procedures adopted and the form of the algorithms are regarded as a statistical model, which is calibrated through comparisons between observed cold cloud characteristics and sets of conventional raingauge data.

TAMSAT algorithm over AfricaThe factors to be considered in comparing methods include the

following:- the type of regression model employed (linear, non-linear?

multivariate)- the inteval between images (slots); the time averaging period- the space averaging scale; the threshold temperature

adopted- data treatment (e.g. linear or temperature weighted

accumulation) additional data incorporated (e.g. water vapour Channel, visible Channel or contemporary surface

raingauge measurements)- localization of calibration (time or space varying TIR features,

variation with geographic location, time of year, character of season, topography and local storm climatology.

TAMSAT algorithm over Africa The technique is simple. Local seasonally varying

temperature thresholds which best discriminate between precipitating and non-precipitating clouds of convective origin are determined.

The CCD is defined to be the duration of a cloud, with top temperature below a predetermined threshold, over a given area. Therefore, the relation between CCD and rainfall (RR) is given as:

TAMSAT algorithm over Ethiopia It is noted that instead of relating rainfall to CCD, the

regression is performed between midpoints of CCD classes and the median of the rainfall in the CCD class in order to overcome the skewness of the rainfall frequency distribution.

In Ethiopia, the original TAMSAT model is modified to account for spatial inhomogeneity due to complex topography since 1993:

1. Homogeneous zones are delineated2. Selection of a best temperature threshold which

reasonably discriminates between rain giving and inactive (non-rain giving) clouds (archieved CCD at TAMSAT:-40, -50, -60 0C are used)

TAMSAT algorithm over Ethiopia

Comparison of actual and estimated rainfall at different rainfall ranges, July 1995 for the whole country

Comparison of observed and estimated over western Ethiopia for the period June to September 1994.


Comparison of observed and estimated over northeastern Ethiopia for the period June to September 1994.


METEOSAT Channels (bands)

The IR information is separated into three classes, based on temperature thresholds, for improving quantitative rainfall estimation for cold convective clouds, middle layer clouds and warm coastal clouds.

The METEOSAT spin scan radiometer operates in three spectral bands: 0.5 - 0.9 μm (visible band - VIS) 5.7 - 7.1 μm (infra-red water vapour absorption band - WV) 10.5 - 12.5 μm (thermal infra-red band - IR)

The amount of radiation absorbed by water vapour is dependent on the amount of moisture in the radiation's path and the wavelength of the radiation. Increased amounts of moisture, or water content, in the radiation’s path lead to more absorption of the radiation emitted from lower layers. Therefore, if the air temperature decreases with height, higher moisture content result in colder brightness temperature. On a 6.7µm image the coldest temperatures correspond to high cloud tops, whilst the warmest are observed over lower altitude areas when the air is very dry through a deep layer in the atmosphere. For the 6.7µm water vapour channel, the radiation values may also be converted to brightness temperatures. A difference exists between WV (6.7µm) brightness temperature and that of the standard IR (11µm) channel. This is attributed to the absorption and re-radiation by water vapour above the earth's surface or clouds. It is this difference that allows a distinction to be drawn between cirrus and moist updraft regions.

METEOSAT Channels (bands)

4. Measurement of winds

Cloud motion vectors (CMV)

Satellite Derived Motion Fields

Clouds are “passive” tracers of Clouds are “passive” tracers of winds at a single levelwinds at a single level use infrared and visible radiancesuse infrared and visible radiances

Water vapor features (ie., Water vapor features (ie., moisture gradients are “passive” moisture gradients are “passive” tracers of winds)tracers of winds) both in clear air and cloudy conditionsboth in clear air and cloudy conditions use water vapor infrared radiancesuse water vapor infrared radiances

We can properly assign height of We can properly assign height of tracertracer

Satellite Derived Motion Fields:GOES Visible, IR, WV Channels

ImagerImager Water vapor channel (6.7µm) Band 3Water vapor channel (6.7µm) Band 3 Longwave IR window chan. (10.7µm) Longwave IR window chan. (10.7µm)

Band 4Band 4 Visible Channel (0.65µm) Band 1Visible Channel (0.65µm) Band 1

SounderSounder Water vapor channel (7.3µm) Band 10Water vapor channel (7.3µm) Band 10 Water vapor channel (7.0µm) Band 11Water vapor channel (7.0µm) Band 11

Satellite Derived Motion Fields:BASIC METHODOLOGY Image acquisitionImage acquisition

Automated registration of imageryAutomated registration of imagery

Target selection processTarget selection process

Height assignment of targetsHeight assignment of targets

Target tracking Target tracking

Quality control (Autoeditor)Quality control (Autoeditor)

Satellite Derived Motion Fields: Image Acquisition

Select 3 consecutive images in Select 3 consecutive images in timetime

Which channels are selected is a Which channels are selected is a function of which wind product function of which wind product (cloud-drift, water vapor, visible) (cloud-drift, water vapor, visible) is to be generatedis to be generated

Satellite Derived Motion Fields:Auto-registration of Imagery

Registration is a measure of Registration is a measure of consistencyconsistency of navigation of navigation between successive imagesbetween successive images

Landmark features (ie., Landmark features (ie., coastlines) coastlines) mustmust remain remain stationary from image to imagestationary from image to image

Satellite-derived winds are much Satellite-derived winds are much more sensitive to more sensitive to changes in changes in registrationregistration than to errors in than to errors in navigationnavigation

Satellite Derived Motion Fields:Auto-registration (Cont’d)

Manual registration corrections applied Manual registration corrections applied operationally to imagery 5% of the operationally to imagery 5% of the timetime

New automatedNew automated registration registration: : hundreds of landmarks usedhundreds of landmarks used each landmark is sought in all imageseach landmark is sought in all images middle image in loop is assumed to have middle image in loop is assumed to have

“perfect” navigation“perfect” navigation mean line and element correction is mean line and element correction is

computed and computed and possibly appliedpossibly applied for the 1st for the 1st and 3rd imageand 3rd image

Satellite Derived Motion Fields:TARGET SELECTION PROCESS

Consider small sub-areas (target Consider small sub-areas (target

area) of an image in successionarea) of an image in succession

Perform a spatial coherence Perform a spatial coherence

analysis of all targets. analysis of all targets. Filter outFilter out

targets where:targets where: multi-deck cloud signatures are evidentmulti-deck cloud signatures are evident

Satellite Derived Motion Fields:TARGET SELECTION PROCESS (Cont’d)

Locate maxima in brightnessLocate maxima in brightness

Select target/feature associated Select target/feature associated

with with strongest gradientstrongest gradient

Target density is controlled by Target density is controlled by

size of target selector areasize of target selector area

Satellite Derived Motion Fields:Height Assignment of Targets

Infrared window techniqueInfrared window technique oldest method of assigning heights to oldest method of assigning heights to

cloud-motion windscloud-motion winds

not suitablenot suitable for assigning heights of for assigning heights of semi-transparent cloud (ie., thin cirrus)semi-transparent cloud (ie., thin cirrus)

still provides a still provides a suitable fallbacksuitable fallback to other to other methodsmethods

Satellite Derived Motion Fields:Target Height Assignment (Cont’d)

COCO22 Slicing Technique Slicing Technique

most accuratemost accurate means of assigning means of assigning

heights to semi-transparent tracersheights to semi-transparent tracers

utilizes IR window and COutilizes IR window and CO22 (13µm) (13µm)

absorption channels viewing the same absorption channels viewing the same

FOV FOV

Satellite Derived Motion Fields:Satellite Derived Motion Fields:Target Height Assignment (Cont’d)Target Height Assignment (Cont’d)

HH22O Intercept MethodO Intercept Method Utilizes Water Vapour channel (6.7µm) Utilizes Water Vapour channel (6.7µm) Band 3 Band 3 and and

longwave IR window chan. (10.7µm) longwave IR window chan. (10.7µm) Band 4Band 4

Algorithm: these two sets of radiances from a Algorithm: these two sets of radiances from a single-single-

level cloud decklevel cloud deck vary linearly with cloud amount vary linearly with cloud amount

Adequate replacementAdequate replacement of CO of CO22 slicing method slicing method

Satellite Derived Motion Fields:Satellite Derived Motion Fields:TARGET TRACKING ALGORITHMTARGET TRACKING ALGORITHM

Define tracking area centered over Define tracking area centered over each targeteach target

Search area in second image which Search area in second image which best matches radiances in tracking best matches radiances in tracking areaarea

Confine search to “search” area Confine search to “search” area centered around guess centered around guess displacement of targetdisplacement of target

Two vectors per target: 1 for image Two vectors per target: 1 for image 1&2; 1 for image 2&31&2; 1 for image 2&3

Satellite Derived Motion Fields:Quality Control (Autoeditor)

FunctionsFunctions Target height reassignmentTarget height reassignment Wind quality estimation flagWind quality estimation flag

Method (4 Steps)Method (4 Steps) 1)1) 3-dimensional objective analysis 3-dimensional objective analysis

of model forecast wind field on of model forecast wind field on 1st pass1st pass

2)2) Incorporate sat winds into Incorporate sat winds into analysis on analysis on 2nd pass. 2nd pass. Remove those differing Remove those differing significantlysignificantly from analysis from analysis

Satellite Derived Motion Fields:Quality Control (Cont’d)

Method (Cont’d)Method (Cont’d) 3)3) Target heights readjusted by Target heights readjusted by

minimizing minimizing a penalty a penalty function which seeks function which seeks

the the optimum “fit”optimum “fit” of the of the vector to vector to the analysisthe analysis

4)4) Perform another 3-dimensionalPerform another 3-dimensionalobjective analysis (objective analysis (at at

reassigned reassigned pressurespressures) and ) and assign quality assign quality flagflag

GOES High Density Water Vapor Winds

100mb - 250mb

250mb - 400mb

400mb - 700mb

GOES High Density Cloud Drift Winds

100mb - 400mb

400mb - 700mb

Below 700mb

GOES High Density Winds(Cloud Drift, Imager H2O, Sounder H2O)

GOES High Density Visible Winds

Satellite Derived Motion Fields:Satellite Derived Motion Fields:Sources of ErrorsSources of Errors

Assumption that clouds and water Assumption that clouds and water

vapor features are passive tracers vapor features are passive tracers

of the wind fieldof the wind field

Image registration errorsImage registration errors

Target identification and tracking Target identification and tracking

errorserrors

Inaccurate height assignment of Inaccurate height assignment of

targettarget

5. Satellite image/signal

Satellite image/signal interpretation

Interpretation: Contamination

Interpretation: Attenuation

Clouds in Satellite Image1) High Clouds − composed of small ice crystals.

a) Cirrus − thin hooks, strands, and filaments or dense tufts and sproutings.

i) Visible imagery − thin cirrus is difficult to detect due to visual contamination. Dense cirrus shows as patches, streaks, and bands, casting shadows on lower clouds or terrain.

(1) Brightness − normally a darker or translucent appearance, often obscuring definitions of lower features. A light gray compared to thicker clouds.

(2) Texture − fibrous with banding perpendicular to winds.

ii) IR imagery(1) Brightness − usually dense patches are very bright but thin

cirrus is subject to considerable contamination and appears much warmer (darker gray) than the actual temperature.

(2) Texture − subject to variation due to contamination.b) Cirrostratus − High/thin to dense continuous veil of stable ice

crystals covering an extensive area. Commonly found on equatorial side of jet streaks.

i) Visible imagery − generally appears white, thick, smooth, and organized when associated with cyclones. Casts shadows on surfaces below.

ii) IR imagery − appears as uniformly cold (white), often the coldest, cloud layer (except when cumulonimbus clouds are present) with small variations in gray shades. Thin

cirrostratus has considerable contamination problems.

c) Anvil Cirrus (detached from cumulonimbus clouds) − dense remains of thunderstorms, usually irregularly shaped, aligned parallel to the upper level winds. Vary in shape and

especially in size from 5 to 500 km. Tends to become thin and dissipate rapidly.

Clouds in Satellite Image

i) Visible imagery − bright white but diffuse. Thick anvils may cast shadows on lowersurfaces whereas thin anvils are often translucent to lower features.ii) IR imagery − bright white patches, usually coldest (whitest) cloud, except whenactive thunderstorms are present.d) Cirrocumulus − cumuliform ice crystal clouds formed by upward vertical motions in theupper troposphere. May precede rapidly developing cyclone.i) Visible imagery − thin patches of clouds, gray to white, usually in advance of acyclone. Individual elements often below the resolution of geostationary sensors.ii) IR imagery − similar to cirrostratus, white to gray clouds subject to contamination.


2) Middle Clouds − composed of supercooled water droplets and graupel (soft hail).

a) Altocumulus − indicates vertical motion and moisture in the mid-troposphere. Usually accompanies large, organized synoptic scale cyclones, minor upper tropospheric waves, and tropical waves. For well-developed systems, sometimes masked by extensive cirrus.

i) Visible imagery − Bright white, textured, or lumpy, and very difficult to distinguish from stratocumulus.

(1) Wave clouds appear as parallel bands.(2) Altocumulus castellanus (ACCAS) appear as a diffuse, ragged band

of small blobs. In summer ACCAS may be found near air mass boundaries preceding thunderstorm development.

ii) IR imagery − Colder (lighter gray) than stratocumulus but warmer (darker gray) than high clouds. Must be compared to other clouds in the area.


(1)Wave clouds frequently appear warmer and lower (darker gray) than actual due to contamination. Individual waves may be below resolution of geostationary sensors.

(2) ACCAS often appear with frontal systems. Rather large temperature variations may be observed.

b) Altostratus/Nimbostratus − stratiform cloud in mid levels. Normally found in extensive sheets with cyclones.

i) Visible imagery − Bright white, extensive sheet. May be difficult to distinguish from low or high stratiform clouds. Often textured, unlike cirrostratus, but uniform. May cast shadows, unlike stratus.

ii) IR imagery − nearly uniform gray shade indicating the middle temperature ranges. Usually distinguishable by comparison with other cloud layers, warmer (grayer) than cirrus, colder (brighter) than stratus.


3) Low Clouds − composed of water droplets. Wintertime conditions and vertical growth may allow glaciation.

a) Cumulus − similar to detached cauliflower-like clouds with sharp outlines. Often, aregion of unorganized cumulus (“popcorn”) forms over landmasses during fair weather. Cumulus clusters whose edges are clearly visible are referred to as “open cell” cumuli.

i) Visible imagery − scattered individual elements are often below the resolution ofgeostationary sensors and appear as gray areas due to contamination. Large individual elements and groups of broken cumulus appear as bright white blobs of clouds.

ii) IR imagery − only large areas show due to contamination, appearing as dark grayblobs.

b) Towering Cumulus − cumulus of moderate or strong vertical extent.i) Visible imagery − similar to cumulus but elements are larger, so are more likely to be distinguishable as bright white blobs.ii) IR imagery − similar to cumulus, but appearing as lighter gray blobs.


c) Cumulonimbus − cumulus of strong vertical development with or without cirrus anvils. Vary greatly in size and shape depending on storm intensity and environment. If upper level winds are weak, mature thunderstorms are circular cirrus clouds often with cirrus plumes (filaments) streaming out nearly symmetrically in all directions with occasionally lumpy, penetrating tops (indicated by shadows on visible imagery). Stronger winds aloft blow the cirrus anvil downstream and create a diffuse downwind boundary with a sharp, smooth upwind boundary. In region of vigorous thunderstorms, cirrus anvils may merge into cirrus canopies. The active cells are indicated on visible imagery by their lumpy penetrating tops. Much of the cirrus in the ITCZ is actually decaying cirrus anvils.

i) Visible imagery − bright white cellular shape covered with diffuse thin cirrus and often a lumpy penetrating top.

ii) IR imagery − bright white, smooth cellular shape. Enhancement techniques help identify the maximum cloud tops by relating cloud top temperatures to height.


d) Stratocumulus − formed by the spreading of cumulus or convective development of stratus.

Large regions are found over cold ocean currents such as the California current off the West Coast (convective development of coastal fog and stratus) and in the lee of cold fronts (spreading of cumulus). Stratocumulus clouds form along the low level flow. Widely scattered and smaller patches of stratocumulus (trade wind cumulus) are found throughout the tropics. These scattered patches look like polygonal plates and range in diameter from 100−500 km and have limited vertical development.

i) Visible imagery − light gray to white, appearing in cloud lines or sheets composed of parallel rolls. Textures are noticeable.

ii) IR imagery − Dark gray, often difficult to distinguish from the surface due to contamination. Cellular or textured nature often not observed.


e) Stratus and Fog − caused by various means.

Large areas of stratus are found over cold ocean currents, as warm subsiding air underneath anticyclones meets the cold water below.

i) Visible imagery − white to gray, uniform, smooth sheet, except when terrain features penetrate above the stratus tops. Coastal and valley stratus often outlines the surrounding terrain.

ii) IR imagery − nearly invisible due to lack of contrast between the surface and cloud top temperatures. Occasionally, stratus forming beneath a radiation inversion will appear warmer (darker) than the surface, and is called “black” stratus.


a) Snow and Ice

i) Visible imagery − the ability to discern snow cover on visible imagery depends on the type of terrain, the vegetation cover, the snow depth and age, sun angle, the amount of cloud cover, and wind.

(1) Terrain − mountains permit easy snow cover identification, appearing as a white, dendritic pattern against a darker background. Rivers and lakes may be snow covered and may help to distinguish snow from a stratus or stratiform deck. Snow covered plains tend to have a smooth appearance, whereas clouds normally have texture. Snow swaths caused by passing lows tend to be long and narrow

with smooth texture and sharp edges.

Weather Related Satellite Image

(2) Vegetation − snow-covered forest regions appear gray and mottled, rather than white. Snow-covered short grass regions appear white and smooth. The brightness of snow covered grass decreases with increasing height of the grass and decreasing snow depth.

(3) Snow Depth and Age − generally, the deeper and newer the snow, the whiter it appears. Rain on snow makes it appear grayer.

(4) Sun Angle − brightness of snow decreases rapidly when the sun angle drops below 45°.


(5) Cloud cover − since snow does not move, looping imagery may assist in distinguishing snow cover from clouds.

(6) Fracture Lines − ice can sometimes be distinguished by its location (water bodies) and the presence of dark fracture lines. Ice may also look chunky.

(7) Wind may blow snow around, and the peaks and depressions may add texture to an otherwise smooth sheet, helping to distinguish snow from fog and snow from ice.

ii) IR imagery − detection of ice, snow cover, and low clouds is very difficult without simultaneous visible imagery. Snow may appear as a patch colder (lighter) than bare ground.


b) Haze and Smog − suspended fine droplets and particulates in still, stable conditions.

i) Visible imagery − dull, filmy blob. Smog may be related to locations of major urban centers. Varying gray shades due to differential light scattering and absorption effects from the various constituents of the haze or smog.

ii) IR imagery − contamination makes detection of haze or smog very difficult without simultaneous visible imagery, although smog may be inferred over urban centers.


c) Dust or Sand Plumes and Storms − suspended surface particles carried aloft by strong surface winds and carried downwind long distances.

i) Visible imagery − dull, filmy plume often striated with a defined shape. Varying gray shades due to differential light scattering and absorption effects from the various constituents of the dust. Downwind of major deserts (e.g. Sahara, Outback) and dried-up river basins are likely locations for dust/sand plumes.

ii) IR imagery − contamination makes detection of dust or sand very difficult without simultaneous visible imagery.


a) Smoke and Ash − suspended fine carbon and mineral particles from fires, industry, ships, and volcanic activity.

i) Visible imagery − depends on level of activity, atmospheric stability, and windiness, ranging from a dull, filmy area to a bright, well-defined plume streaming downwind from a point. Varying gray shades due to differential light scattering and absorption effects from the various constituents and intensity of fires (etc.). Ships may leave long trails resembling aircraft contrails but thicker and grayer.

ii) IR imagery − red-hot fires and explosive volcanic eruptions appear as black dots in IR and bright dots in visible. Lava flows may appear as small, narrow, winding black bands. Thin plumes are subject to contamination but high, thick plumes may be cold(bright) enough to be clearly discernable.

Non-Weather Related Satellite Image

b) Surface Variation

i) Visible imagery − differences in reflectivity among land cover types (e.g. grass and forest) may be apparent as variations in shades of gray. Some highly reflective sandyareas (e.g. White Sands, NM) may be seen as white to gray unmoving blobs that could be confused for snow. Shadowing in mountainous areas may give the landscape texture.

ii) IR imagery − differences in land cover may produce differences in surface heating that may be apparent as variations in shades of gray. Typically, these are large areas (e.g. California’s Central Valley, the Great Salt Lake) that are significantly warmer or colder than their surroundings.


c) Sun Glint − can be seen when the sun is directly above the viewing scene and sunlight is reflected off a highly reflective surface such as water or sand. Sun glint patterns appear regularly in visible imagery of both polar orbiting and geostationary satellites. The patterns vary in shape, size, and brightness depending on the solar sub point, sea state, and low-level distribution of aerosols and moisture. Simultaneous comparison to IR can help distinguish sun glint from a dust plume or similar filmy area.

i) Geostationary imagery − appears as a large, diffuse, circular bright region located between the satellite sub-point and the solar subpoint, and thus would be found in the tropics near the equator. If the water surface is very smooth, the sun glint area is small and intensely brilliant.

ii) Polar orbiting imagery − a large, diffuse, semi-bright area that typically stretches from the bottom to the top of a picture. Very dark areas cutting through diffuse sun glint indicate the presence of calm seas, and may signify the presence of surface ridges.


Various cloud forms1) Open Cell Cumulus − cumulus clusters whose edges are

clearly visible.a) Cloud Street − orography or heating contrasts due to

topography or vegetation may cause alignment of open cell cumuli in lines parallel to the low-level flow or the low to midlevel wind shear. Cloud streets can provide an excellent representation of the low level flow around anticyclones.

b) Typically appear brighter than closed cell stratocumulus in IR imagery.

2) Closed Cell Stratocumulus − individual cloud elements or clusters of elements without clearly defined edges, instead forming smooth to slightly lumpy lines of merged elements.

a) Cloud Sheet − may form from semi-merged lines of closed cell stratocumulus and will have a lumpy striated texture.

b) Typically appear grayer than open cell cumulus in IR imagery.

3) Enhanced Cumulus − area of cumulus congestus, towering cumulus, or cumulonimbus clouds. Associated with fronts, PVA, or orography, and appear as very bright dots in a field of otherwise uniform open cell cumulus.

4) Sea Breeze/Land Breeze − a nearly continuous band of cumulus clouds that tend to parallel the coastline. Sea breezes are most often found inland during the late afternoon, and land breezes offshore during the early morning. A cloud-free region along and off the coastline indicates the subsidence portion of a sea breeze cell.

5) Ship Tracks − Long, narrow, stratocumulus cloud plumes that form in the wake of ships when the winds are light, and there is a subsidence inversion capping the rising air.

Various cloud forms

Satellite remote sensing applications in Meteorology 2 nd Ewiem Nimdie International Summer School 2...

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