The EUMETSAT Satellite Application Facility on Land ... · The EUMETSAT Satellite Application...

The EUMETSAT Satellite Application Facility

on Land Surface Analysis (Land SAF):

Proposed Products

by:

Alirio Arboleda

Based on material provided by Carla Barroso (IPMA)

OBJECTIVE

• Introduce the EUMETSAT’s Satellite ApplicationFacility (SAF) on Land Surface Analysis (LandSAF) and present the products developed, theircharacteristics and some validation results

• Invite you to use the Land SAF products in yourprojects and applications and to participate to theimprovement of these products by given feedbackto products developers

Satellite Application Facility (SAF) is an European scientific network designed by EUMETSAT.

Objectives

• Improve EUMETSAT’s Member States exploitation of satellite data

• Encourage the utilisation of existing skills and infrastructure in Member States and Cooperating States

• Cost-effective exploitation: Services are distributed in the most appropriate way

• Foster development of cooperation with non-Member States and other organisations

EUMETSAT SAF NETWORK

• Develop techniques that allow an effective use of remote sensing data (MSG, EPS) data related to:– LAND– LAND-ATMOSPHERE

Interactions– BIOSPHERIC Applications

• Timely provide:– Products– User support

Objectives

Organisation principles– Algorithms developed at one of the

participating Institutes– Algorithms handed over to IM for

integration and production

LSA-SAF consortium• Instituto de Meteorologia (IM),

Portugal• Meteo-France (MF), France• Royal Meteorological Institute

(RMI), Belgium• [Finnish Meteorological Institute

(FMI), Finland]• IMK, University of Karlsruhe• IDL, University of Lisbon• UV, University of ValenciaCDOP-2 new members:• KCL, UK• VITO, Belgium

SAF on Land Surface Analysis (LSA)

Dev. Phase:

Sep 1999

Initial

Operations

Phase:

Feb 2005

Continuous

Development &

Operations Phase:

Mar 2007

Continuous

Development &

Operations Phase 2:

Mar 2012

02 03 04 05 06 07 08 10 11 12 13 14 15 16 17 18 19 20010099

Meteosat-8 (MSG-1)

Meteosat-9 (MSG-2)

MetOp-A

Meteosat-10 (MSG-3)

MetOp-BMeteosat-11 (MSG-4) MTG-1

MetOp-C

21 22

MTG-S

LSA-SAF chronogram of activities

Surface Radiation

↓↓↓↓LongWave Flux

↓↓↓↓ShortWave Flux

Surface Water Balance

Snow Cover

Vegetation

Fraction Veg Cover

LAI

FAPAR

Wild fires

LSA-SAF MSG Products

Increased level of maturity

Development Pre. Operat. Operational

Fire Radiative Power

LST

EvapotranspirationAlbedo

Fire Detection & Monitoring

Fire Risk Mapping(Europe)

NDVI from Metop

Variable time resolution-15 min to 10 days

SEVIRI resolution

EPS products generation for a subset of variables(LST, DSLF, NDVI)

All products have a quality flag and/or error bar associatedAll products have detailed documentation (Product User Manual,

Validation Report, Algorithm theoretical Basis Document)

Euro

NAfr

SAme

SAfr

Euro

NAfr

SAme

SAfr

4 production areas for MSG

- Europe

- Northern Africa

- Southern Africa

- Southern America

Common Products Characteristics

↓↓↓↓LongWave Flux (DSLF)

↓↓↓↓ShortWave Flux (DSSF)



LST

Albedo

Surface Radiation


=rad

F

↑↓−

SWSWFF

FSW + FLW

↑↓−

LWLWFF↑↓

−SWSW

FF

DSSF - ALBEDO××××DSSF + DSLF - DSLF ××××(1-ε)-εσεσεσεσLST4=rad

F

Long-wave Radiation budget at the surface

Short-wave Radiation budget at the surface

Radiation Products

The surface radiation budget is the balance between the incoming energy from

the sun and the outgoing energy of Earth.

Surface Radiation Budget

Radiation Products - DSSF

LSA SAF method (Geiger et al., 2008)Input data:•0.6, 0.8 and 1.6 µm SEVIRI channels• TCWV from ECMWF•Cloud mask from NWCSAF

Downwelling Surface Shortwave FluxRadiative energy flux in the wavelength interval [0.3µm, 4.0µm] reaching the Earth's surface per time and surface unit.

atmsurf

atmeffective AA1

1TT

−=

Simplified physical description of the radiation transfer in the cloud-atmosphere-surface system

The effective transmittance of the atmosphere is a funtion of atmospheric constituents


MethodologyeffectivesunTtvSDSSF θcos)(0≈

Solar Constant

Distance sun-earth

(jday)

Solar zenith angle

Effective transmittance

cloudsurfcloudbelowatm

cloudatmeffective AAT

TTT

__1−=

Atmosphere transmittance

Cloud albedoSurface albedo

Cloud transmittance

ProductProductProductProduct HorizontHorizontHorizontHorizontal al al al

ResolutiResolutiResolutiResolution & on & on & on &

CoveragCoveragCoveragCoverageeee

Temporal Temporal Temporal Temporal ResolutionResolutionResolutionResolution

GeneraGeneraGeneraGeneration tion tion tion

FrequeFrequeFrequeFrequencyncyncyncy

Target Target Target Target AccuraAccuraAccuraAccura

cycycycy

DSSFDSSFDSSFDSSF MSG MSG MSG MSG diskdiskdiskdisk

InstantaneoInstantaneoInstantaneoInstantaneous & Dailyus & Dailyus & Dailyus & Daily

30 min 30 min 30 min 30 min & Daily& Daily& Daily& Daily

5555----10 %10 %10 %10 %

=> completed with METOP/AVHRR to improve high latitudes coverage


Characteristics

Instantaneous (30 min) DSSF

Monthly variation of daily accumulated Solar Radiation

15 Jan 2009 15 March 2009 15 May 2009

15 July 2009 15 Sep 2009 15 Nov 2009


Validation at measurement sites


Validation

2004 to 2006

Clear sky:biais : 5 W/m2 stdev : 40 W/m2

Cloudy sky:biais : 5 W/m2 stdev : 115 W/m2


Validation

Snow

Snow

Albedo Seasonal Cycle:

Primarily driven by Vegetation & Snow

High Albedo Values over

Deserts

Low Albedo Values

Vegetated Areas

Radiation Products - Albedo

Albedo (AL): Fraction of incident

radiation reflected by the surface

• 0.6µm, 0.8µm, 1.6µm SEVIRI channels• Solar and View Angles• Land/Sea Mask• Cloud Mask (SAF-NWC software)• Total Column Water Vapour, and

Pressure (ECMWF)• Ozone Content (Climatology)• Aerosol Optical Thickness (Climatology)

1.6µm

Methodology (Input data)


Model Parameters 0.6µm, 0.8µm, 1.6µm

BRDF ModelInversion

Geometry Previous ModelEstimate

Spectral Albedo 0.6µm, 0.8µm, 1.6µm

Angular Integration

Broadband Albedo [0.3µm,4µm], [0.4µm,0.7µm], [0.7µm,4µm]

TOA-Radiances 0.6µm, 0.8µm, 1.6µm

TOC-Reflectances 0.6µm, 0.8µm, 1.6µm

Atmospheric Correction(SMAC)

GeometryAtmosphericConstituents

Cloud Mask

Spectral Integration

for

each

day

for

each

slo

t

Lucht and Roujean (2000), Geiger et al. (2008)


Methodology

Modis SW-BH albedo

ground measurementsMSG albedo

boreal forest

mixed shrub/tree


Validation

� Temporal evolution of the albedo estimate is influenced by rainfall.

� ‘Spurious’ fluctuations appear to be caused by aerosol effects.


Validation

MSG albedo

Modis albedoAerosol optical thickness

ground measurements

Good correlation between LSA SAF and MODIS albedo

Comparison in Central Africa (Savannah)


Validation

LST

�Generation Frequency - 15 min

�clear sky pixels ...

� over land ...

� where estimated errors < 4K

� Available since 2005

Radiation Products - LST

Land Surface Temperature (LST) is the radiative skin temperature over land.

C2

TT)

ε

∆εB

ε

ε1B(B

2

TT)

ε

∆εA

ε

ε1A(ASTL 12.010.8

232112.010.8

2321 +−+−++++−+=

Generalised Split-Windows (Wan and Dozier 1996, adapted to SEVIRI: Trigo et al., 2008a):

TOA Clear sky brightness temperature at 10.8µm and12.0µm (SEVIRI)

εεεε = average

∆ε∆ε∆ε∆ε = difference of channel emissivities

GSW parameters depend on:1. total column of water vapour2. satellite viewing angle

ECMWF fc


Methodology

ε = εveg FVC+ εground (1-FVC)

LSA SAF Product

Pixel MSG

Methodology


Channel Emissivity → From fraction of Vegetation Cover

Emissivity is estimated as a weighted average of that of bare ground and vegetation elements within the pixel

(ºC) BIAS RMSD

SEVIRI +1.9 2.2

MODIS -1.8 2.6

Day time

(ºC) BIAS RMSD

SEVIRI -1.7 2.1

MODIS -2.6 2.7

Trigo, I. F., I. T. Monteiro, F. Olesen, and E. Kabsch, (2008) in J. Geophys. Res., 113

Night time

Evora


Validation at Evora (Pt)

Freitas, S. C., I. F. Trigo, J. M. Bioucas-Dias, F.M. Goettche, 2010 in IEEE TGRS

0 20 40 60LST SEVIRI/MSG (ºC)

Bias: -0.31ºCRMS: 1.16ºC

May 200860

40

20

0

Obs

In S

itu (

ºC)

Bias: -0.91ºCRMS: 1.67ºC

Jul 200860

40

20

0

Obs

In S

itu (

ºC)


60

40

20

0

Obs

In S

itu (

ºC)

Nov 2008


Bias: +0.24ºCRMS: 1. 44ºC


Validation at Gobabe (NA)

�Overall SEVIRI LST is warmer than MODIS LST, with night-time systematic differences ranging between 0.5 -1ºC.

Directional Character of remotely sensed LST

Daytime – Differences depend on viewing angle


Validation: Comparison between MSG-LST and MODIS LST (Central Africa Savannah)

O SEVIRI LST* MODIS LST

Radiation Products - DSLF

Downwelling Surface Long-wave Flux (DSLF):Is the thermal irradiance reaching the surface in the thermal infrared

spectrum interval [4µm,100µm].

Long-wave Radiation reaching the Surface:

Essentially emitted by the lowest 100 m of the atmosphere

� It is controlled by

� concentration of absorbing gases

CO2, CH4, H2O, ...

� presence of clouds, clouds phase

� temperature profile & temperature of cloud base

....

Total irradiance at the surface within the [4 – 100 µµµµm] interval:

clearcloudyskysky DSLFnnDSLFTDSLF )1(4 −+== σε

+−

+−=m

sky

ww

10exp

1011 βαε ( )δγ +∆+= 22 TdTTsky

Cloud

Fraction of Cloud

Total column water vapour

2m air temperature

2m dew point depression

Bulk Parameterization based on the Stefan-Boltzman Law:

Trigo et al. (2010)


Methodology

Example of instantaneous DSLF product and associated quality flag for 14/03/2014 at 12:00 UTC


15 Jan 2011 15 Apr 2011

15 Jul 2011 15 Oct 2011


CERES SEVIRI DSLFPeriodJan 2006 - Apr 2007

StationsCambourne, UKLerwick, UKToravere, Estonia

CERES SEVIRI DSLF

Bias RMSE Bias RMSE

Clear Sky -0.0 12.7 -2.2 22.3

All Sky -2.1 23.5 3.1 25.3

Problems:

- Cloud identification at the edge

of Meteosat disk

- DSLF model for ice clouds

- Temperature inversions

○ clear sky▪ cloudy sky


Validation: Northern Europe station

CERES SEVIRI DSLF

Bias RMSE Bias RMSE

Clear Sky -0.9 13.4 0.8 14.5

All Sky -1.4 22.7 1.6 22.5

Problems:

- temperature inversions

CERES


SEVIRI DSLFPeriodJan 2006 - Apr 2007

StationsPalaiseau, FrancePayerne, CHCarpentras, France


Validation: Central Europe station

PeriodJan 2006 - Apr 2007

StationsTamanrasset, AlgeriaSde Boqer, IsraelNiamey, Niger

CERES SEVIRI DSLF

Bias RMSE Bias RMSE

Clear Sky 13.0 26.8 -4.4 14.1

All Sky 17.5 29.6 -5.4 16.9

Problems:

- Impact of high aerosol loads

CERES


SEVIRI DSLF


Validation: Semi-arid and desert stations

Surface Water Balance

Evapotranspiration



Snow Cover


0 5 (mm)

0 0.7 (mm/h)

Water balance productsEvapotranspiration (ET)

ET: Flux of water vapour between ground surface and the atmosphere.

Daily cumulated ET (DMET)Instantaneous ET, one image every 30 minutes

MSG PixelTuiles:• % Bare soil• % forests• % crops• % grass• …

�A simplified version of ECMWF TESSEL SVAT module, forced with

LSA SAF radiation products (DSSF, DSLF & AL) & ECMWF

meteorology.

Methodology


fro

m s

atel

lite

MS

G-I

I

FVC -Fractional Vegetation Cover-

LST -Land Surface Temperature-

LAI -Leaf Area Index-

AL -Surface ALbedo-DSSF-Shortwave flux at surface

DSLF-Longwave flux at surface

Ti -Tiles in pixel-

FVi -Fraction of tiles-

Rsi -Tile minimum stomatal resistance

LAI i -Tile Leaf Area Index -

FVCi -Tile fractional vegetation cover

ALM -Monthly pixel AlbedoFro

m d

atab

ase

(EC

OC

LIM

AP

)F

rom

NW

P

(EC

MW

F)

Ta -Air temperature-

U -Wind speed-

Pa-Air pressure -

Td -Dew-point temperature-

ST -Soil Temperature-

SM -Soil Moisture-

MODELFORMULATION

MET

QF

(1)

1 included in last version2 future versions

(2)


Methodology

� Comparison to observations in different climatic and environmental conditions

�Comparison to output from other models

�Good agreement between simulations and observations ; the best agreement is observed in areas dominated by grasslands and mixed forests

Validation


A thresholding technique is applied to distinguish surfaces

covered with snow or ice from clouds and snow-free

pixels.

Snow

Non proc

Snow-free

Unclass

Water

Different signatures of snow, ice, and clouds on 0.6, 0.8 & 1.6 µm channels

reflectances.

Water balance productsSnow Cover (SC)

Fraction Veg Cover

LAI

FAPAR




Fire Detection & Monitoring

Fire Risk Mapping(Europe)

NDVI from Metop

Wild FiresVegetation


Fractional Vegetation Cover Leaf Area Index

Fraction of Absorbed

Photosynthetically Active Radiation

Vegetation Products

Fraction of vegetation on a

flat background.

Accounts for the surface of

leaves contained in a vertical

column normalized by its cross-

sectioanl area.

Indicator of the health

(productivity) of vegetation.

FVC LAI

fAPAR

Algorithms

BRDF parameters (contain specific spectral directional signatures of vegetation

reflectances).

FVC is estimated through the application of a spectral mixture analysis

methodology (0.6, 0.8 & 1.6 µm channel refletances), developed taking into

account the spectral variability of vegetation in different ecosystems (e.g.

Bateson et al. 2000; García-Haro et al 2005);

LAI is estimated from FVC (Roujean and Lacaze (2002));

FAPAR is based on simulations of surface reflectances in optimal angular

geometries (Roujean and Bréon, 1995).

Validation results

Example of validation exercises carried out in SAfrica:

Peak of biomass

Minimum biomass

Peak of biomass

Minimum biomass

Rapid green-up

(Zambia)

Near-global, 10-daily composite images (synthesized from the "best available"observations registered in the course of every "dekad" by the orbiting earth observationsystem Metop-AVHRR)

http://www.metops10.vito.be

NDVI from Metop

FIRE RADIATIVE POWER (FRP) - The Fire Radiative Power (FRP) is the amount ofradiant energy emitted per unit time during a vegetation fire. is related to the rate atwhich fuel is being consumed


The FRP product is derived using a Fire Thermal Anomaly (FTA) algorithm. Ii works mainly on statistics derived from the 3.9 µm and 11.0 µm brightness temperatures, and their differences


By integrating FRP during the lifetime of a vegetation fire we get the total combusted biomass(Kg)

�Detect an active vegetation fire


FRP product allows to:

�Compute the radiant energy per unit time for the detected fire

�Estimate trace gas emissions from the fire

Temporal Frequency & Availability

Availability

>2006

>2008

5-Day

30-Day

Daily

10-day

Daily/15 min. Daily/15 min.

Vegetation

Products

FRP

NDVI 10-Day 10-Day >March

2013 (…)

http://landsaf.meteo.pt

Publications

Download Data

Documentation (User Manuals, Validation Reports)

Helpdesk

•How to access products files ?


1. register

2. login


• Free softwares, eg :HDFview (http://www.hdfgroup.org)

• Commercial softwares (matlab, idl,…)

•How to read/visualize the files ?

Data stored in Hierarchical Data Format 5 (HDF5), a file format designed to store and organize large amounts of numerical data. It is supported by the non-profit HDF Group

The files are compressed with bzip2 (http://www.bzip.org).

When downloaded, you should uncompress with bunzip2

(http://www.hdfgroup.org)

This software allows:- Visualize

images- Visualize

values- Histograms- Save images in

other formats

• (.jpg, bmp, gif,….)

Evapotranspiration values (array)

Quality flag (array)

•Visualize data with HDFview

•Attribute of dataset

•Vizualization with different color palettes

•Visualize data with HDFview

•Datasets attributes

•Read files with matlab

•3 datasets for the hdf5 file•Daily evapotranspiration

•Information on the data: attributes

•Read the HDF5 file (dataset ET) with hdf5read

•Step 1: use « hdf5read », specifying the dataset to read

•Step 2: T is an array of integers. You should divide by the scaling factor.

•Select a point based on its geographical coordinates

•Chosen point corresponds•distance minimum

•Step 1 : choice of the point (Ex.: 15.05°N, 10°W)

•Step 2 : read file lat-long

•Step 3 : divide by scale factor

•Step 4 : compute the distance between point and pixels Meteosat

•ET in mm/hr

•Quality flag

•Extraction of a time series

•Clear sky day

•Clouds passing over

•According to the table in the product user manual (PUM)

•Nominal quality

•Below nominal quality

•Poor quality

•Daily values

Questions/Comments?

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