1.3 THE EUMETSAT GEOSTATIONARY AND POLAR SATELLITE SYSTEMS AND PRODUCTS

Kenneth Holmlund and Marianne KönigEUMETSAT

1. INTRODUCTION

Satellite data are the main source of information inNumerical Weather Prediction (Szyndel (2003)), with99% of the available data for global numerical weatherprediction (NWP) being satellite based. It should also benoted that satellites are one of the few observingsystems that can provide data at sufficiently hightemporal and spatial frequency for regional andmesoscale models. The derivation of quantitativemeteorological parameters from the Meteosat satellitesstarted already with Meteosat-1 in 1979. Majorimprovements in the algorithms during the 1980’sestablished the derived products as a major source ofinformation for global NWP, with initially AtmosphericMotion Vectors (AMV) and later also Clear SkyRadiances being the key products for the assimilation.Lately, AMVs are also used both in regional andmesoscale NWP models (e.g. Bonavita and Torrisi(2004)), proving the importance of this data. TheMeteosat First Generation satellites were the first toprovide observations in the water vapour band fromgeostationary orbit with a 5 km resolution every 30minutes. The water vapour data combined with theinfrared window measurements have proven to be apowerful combination enabling the derivation of highquality cloud height measurements and other products.However, the addition of new channels on the MeteosatSecond Generation (MSG) imager SEVIRI (SpinningEnhanced Visible and InfraRed Imager) will furtherimprove the capabilities to determine cloud heights.Additionally, information on cloud properties, aerosoland total ozone amongst other potential applicationscan be provided with the new instrument. The first MSGsatellite MSG-1 was successfully launched in August2002. The commissioning ended in December 2003 andthe satellite entered operations as Meteosat-8 inJanuary 2004. The new imager is performing well (fordetails see e.g. Hanson et al. (2004), Müller et al.(2004)) and with improved image and product quality.The following sections will give an overview and statusof the EUMETSAT Meteorological Product ExtractionFacility (MPEF) and the derived products. The lastsections will shortly introduce the current status of theEUMETSAT system of polar orbiting satellites andEUMETSAT's contribution to the JASON-2 programme.

Corresponding author address: Kenneth Holmlund andMarianne König, EUMETSAT, Am Kavalleriesand 31,64295 Darmstadt, Germany;e-mail: holmlund@eumetsat.de, koenig@eumetsat.de

2. THE GEOSTATIONARY SATELLITE SYSTEM

The first generation Meteosat satellites, which arecurrently controlled under the Meteosat TransitionProgramme (MTP), have as payload a three-channelimager with broadband channels in the visible, infraredand water vapour region of the spectrum. The resolutionat the sub satellite point is 2.5 km for the visible and 5km for the infrared and water vapour channels. TheMeteosat satellites are spin stabilised rotating at 100rpm providing a full field of view imagery every 30minutes. Currently, three Meteosat satellites are usedoperationally. Meteosat-7 provides the primary missionat 0°, and Meteosat-5 supports the Indian Ocean DataCoverage (IODC) mission at 63°E. The current hot-standby satellite for the primary mission, Meteosat-6,located at 10°E, is used for the rapid scan serviceproviding 10 minute imagery over Europe. It is currentlyforeseen that the IODC service will be continued until2005, whereas the primary mission will be taken over bythe new MSG satellites.

The first spacecraft MSG-1 was successfullylaunched in August 2002 and has become operationalin early 2004, when it was renamed to Meteosat-8. TheMSG satellites are also spin-stabilised rotating at 100rpm. The SEVIRI instrument is a 12 channel imagerscanning the Earth in each spectral band with threedetectors (nine for High Resolution Visible, HRV)instead of one as is done with the imager on Meteosat.This enables a 3 km sampling distance betweenindividual measurements (1 km for HRV) and a full fieldof view coverage of 15 minutes instead of 30 minutesas provided by the first generation satellites. Theabsolute pointing accuracy is within 3 km RMS for asingle image and 1.2 km for image to imageregistration. The relative accuracy for image sub-areasis even higher with 1.2 km for 500 pixels and 0.75 kmover 16 km. The co-registration of the channels is forthe HRV, 0.6, 0.8 and 1.6µm 0.6 km and for the otherchannels 0.75 km. The image data from all satellites isdisseminated in near-real-time.

The successful Meteosat era is now beingcomplemented and continued by MSG. The currentoperational products derived from MSG imagery datawill be enhanced with new products and algorithms. Theimproved capability of the MSG image data ascompared to Meteosat enables more accurate clouddetection and classification. Especially the highernumber of channels in the infrared and near-infraredand also the three channels in the visible band providenew data that will improve the detection andclassification of clouds and other atmospheric features.The SEVIRI instrument will also have the capability todetect the cloud phase and provides the potential toderive completely new products like Total Ozone and

instability information. The higher sampling rate in timeand space provides further improvements that willdirectly increase the quality of several of the existingkey products like Clear Sky Radiance and theAtmospheric Motion Vector product. The multitude ofchannels will also enable the estimation of cloud topheights with higher accuracy than before.

The SEVIRI instrument provides observations in12 spectral bands, three visible channels (HRV, 0.6,0.8µm) a near-infrared channel (1.6µm) and 8 infraredchannels (3.9, 6.2, 7.3, 8.7, 9.7, 10.8, 12.0, 13.4µm).Figure 1 presents the spectral response functions of the8 infrared channels together with the atmospherictransmission for a mid-latitude summer standardatmosphere (nadir-view) and the correspondingweighting functions. Table 1 summarises the channelinformation.

Figure 1: The SEVIRI spectral response functions of the 8infrared channels together with the atmospherictransmission for a mid-latitude summer standardatmosphere at nadir (top) and the corresponding weightingfunctions (bottom).

ChannelNumber and

Name

minimum, central,and maximum

wavelength (µm)Identification

01 VIS0.6 0.56 0.635 0.71 solar02 VIS0.8 0.74 0.81 0.88 solar03 NIR1.6 1.50 1.64 1.78 solar04 IR3.9 3.48 3.90 4.36. window(solar/thermal)05 WV6.2 5.35 6.25 7.15 water vapour06 WV7.3 6.85 7.35 7.85 water vapour07 IR8.7 8.30 8.70 9.10 window08 IR9.7 9.38 9.60 9.94 ozone09 IR10.8 9.80 10.8 11.8 window10 IR12.0 11.0 12.0 13.0 window11 IR13.4 12.4 13.4 14.4 carbon dioxide12 HRV broadband 0.4 –1.1 solar

Table 1: Overview over the spectral bands of the SEVIRIinstrument

3. STATUS OF THE MSG MPEF PRODUCTS

3.1 Overview

The EUMETSAT MSG Meteorological ProductExtraction Facility (MPEF) is a near-real time system(Holmlund et al. (2003)). The fully automated productgeneration is controlled by a schedule that starts all theactivities from data acceptance, product generation,quality control, product distribution to productverification. Currently, the input data are rectified (i.e.geo-located) and calibrated Level 1.5 image data. Inaddition to the image data the MPEF uses ECMWF(European Centre for Medium Range WeatherForecasts) model data and meteorological observationslike radiosonde data. All activities are configurable withsetup parameters, and the processes are continuouslymonitored. The system also produces automatedstatistical reports for various purposes like monitoring,validation, verification, and reporting. The generatedproducts are distributed to the users via the GlobalTelecommunication System (GTS), via satellite(EUMETCast), or are available via the U-MARF (UnifiedMeteosat Archiving and Retrieval Facility) atEUMETSAT. The new system very quickly reached ahigh level of reliability. The required availability of 98.5%is generally reached. In April 2004 99.8% and in May99.8% of all scheduled products were generated anddisseminated. A high quality of the products is of coursealso required. The following sections will give anoverview of the status of the various products. It isexpected that most products will experience furtherimprovements during the early operations throughtuning of the setup parameters and algorithms.Additional implementation of new algorithms will alsotake place as well as the introduction of new products.

3.2 The MPEF Baseline Products

One of the main objectives of MSG is to providecontinuity to the already well established productsderived with current first generation Meteosat data. TheSEVIRI data enables the derivation of new andimproved products. At EUMETSAT the SEVIRIcapabilities are used to improve the quality of thederived products like the Atmospheric Motion Vectors,cloud analysis and clear sky radiance products. Newproducts like mid-tropospheric humidity, GlobalInstability Index, Total Ozone and Clear SkyReflectance Map are also produced. Table 2 presentsthe current baseline products and distribution means.

SEVIRI instrument has an on-board blackbodycalibration for the infrared channels. This calibration ismonitored by the vicarious calibration in the MPEF(Holmlund et al. (2003)). This vicarious calibrationscheme is similar to that used with the Meteosat firstgeneration satellites, further improvements, however,have been introduced for MSG. In addition tomonitoring, it also supports the selection and tuning ofthe blackbody models. The vicarious calibration can beexecuted both with observational data like radiosondedata or with NWP data. The advantage of theobservational data is that it is a truly independentapproach. However, shortcomings in observableparameters ( e.g. ozone) and quality (e.g. upper tropo-

Products Acronym UMARF GTS EUMETCastAtmospheric Motion Vectors AMV Yes Yes YesCloud Analysis CLA Yes Yes YesCloud Analysis Image CLAI Yes YesCloud Mask CLM Yes YesCloud Top Height CTH Yes YesClear Sky Radiance CSR Yes YesClimate Data Set CDS YesHigh Resolution Precipitation Index HPI YesISCCP Data Set AC, B1 & B2 IDS YesTropospheric Humidity TH Yes Yes YesTotal Ozone TOZ Yes Yes YesSea Surface Temperature (1) SSTScenes Analysis (1) SCERadiative Transfer Model (1) RTMCalibration Support CAL YesGlobal Instability GII Yes YesClear-sky Reflectance Map (2) CRM Yes Yes

Table 2: The status of the meteorological products extracted centrally with Meteosat-8.1) Internal products only, 2) Not fully operational

3.3 The MPEF Radiative Transfer Model

The MSG MPEF Radiative Transfer Model (RTM) is acoarse resolution line-by-line model that performsmonochromatic calculations at about 1 cm-1 wave-number resolution (Tjemkes and Schmetz (1997)). Itcurrently uses HITRAN 92 with CDK 2.0 lineparameters as static input data as well as static surfaceemissivity data. The dynamic atmospheric data areprovided by ECMWF forecasts at 31 levels and radio-sonde data. The accuracy of the model has beenverified already before launch against LBL-RTM andamounts to 1 – 2 %. The model output is used for thesubsequent scenes and cloud analysis, heightassignment, atmospheric correction tables, tropospherichumidity and calibration monitoring.

3.4 Calibration Monitoring

One of the most important aspects of satellite datais the availability of high quality calibration. On MSG the

spheric / lower stratospheric humidity) together withavailability (e.g. radiosonde stations are limited, andsondes are generally launched only twice daily) limit theuse of the data. The use of model data on the otherhand has the scope of good availability for all variables,locations and levels, however, the quality is dependenton the model performance and is also not always fullyindependent through the use of the satellite data in themodels themselves. However, both data types providecurrently a consistent view of the MSG blackbodycalibration. In general the blackbody and vicariouscalibration agree to within 1 K for most IR channels. Forvisible channels only a vicarious calibration approach isused (Govaerts (2003)). The expected accuracy is ofthe order of 5%.

3.5 Scenes and Cloud Analysis

The algorithm used for the scenes and cloudanalysis in the MSG MPEF is a pixel-basedthresholding technique based on the work by Saunders

and Kriebel (1988). Their approach has been furtherrefined, enhanced, and adapted to MSG data by Lutz(2003). The scenes and cloud analysis is a pre-requisitefor further processing and contributes crucially to thederivation of Clear Sky Radiances and AMVs as well asmost other products. The current performance of thescenes and cloud analysis is demonstrated in Figure 2.In the cloud mask the clouds are identified in white, andthe clear sky areas are coded in different shadesaccording to information from the static scene type file.For the cloud analysis different cloud types areidentified in different shades of grey, the surfaceinformation is again derived from the surface scenetype.

Figure 2: The cloud mask derived by the MSG MPEF (top) andthe corresponding cloud analysis (bottom) on 29 June 200411:45 UTC. Cloud heights are represented by different greyshades

3.6 The Clear Sky Radiance Product

The derivation of the clear sky radiance (CSR)product is performed in all 8 infrared channels. Thebasic derivation is on pixel basis, and the final productis the average CSR on a segment size of 16*16 pixels.

The main processing factors are the need for a propercloud clearance and good calibration. As an example,Figure 3 presents the clear sky radiances derived fortwo MSG IR channels; 3.9 and 6.2µm on 02 July 2004at 0545 UTC. It should be specifically noted that the useof the radiances of the 3.9µm channel is difficult duringdaytime. This is well illustrated by the high observedradiances in the eastern part of the 3.9µm CSR, wherereflected solar radiance significantly enhances theobserved radiance. The water vapour CSR productsfrom geostationary satellites are operationally used inglobal NWP, while the use of radiances from the ozonechannel at 9.7µm and the infrared window channel stillpose a challenge in data assimilation.

Figure 3: The CSR product for the IR channels at 3.9µm (top)and at 6.2µm (bottom), 02 July 2004, 0545 UTC.

3.7 The Tropospheric Humidity Product

The relationship between observed radiances inthe 6.2 and 7.3µm channels and the corresponding mid-and upper-tropospheric humidity is based on theregression approach by Soden and Bretherton (1993).Figure 4 presents an example of the upper tropospherichumidity product.

Figure 4: The Upper Tropospheric Humidity (UTH) field asderived within the MSG MPEF (top) and the correspondingIR window channel image showing the cloud distribution.

3.8 The Total Ozone Product

The total ozone product is currently derived with astatistical/physical regression model. However, thecurrent operational product shows some deficiencies(noisy retrieval, no retrieval over high level clouds).Hence a new method based on an optimal estimationtechnique has been developed and is expected tobecome operational during 2004. Figure 5 presents anexample of the derived ozone fields derived with the twomethods.

3.9 The Atmospheric Motion Vectors Product

The derivation of Atmospheric Motion Vectors(AMV) is based on the approach already used with thecurrent geostationary satellites. One of the maindifferences to the current operational approach is thedynamic target selection scheme, which enables notonly a better selection of features for tracking, but alsothe flexilbility to change the resolutino of the productaccording to user requirements (Holmlund (2000)).

Figure 5: The operational Total Ozone product based on alinear regression model (top), compared to thecorresponding result based on an optimal estimationtechnique (bottom)

An important feature of the new AMV scheme isthe multitude of height assignment methods that can beused in parallel. Figure 6 shows an overview of theperformance of the various height assignment methodstogether with a detailed comparison for the so-calledCO2 height assignment with colocated radiosondes. Inthese examples the performance of the heightassignment is rather satisfactory, which is not alwaysthe case, and the operational height assignment needsfurther tuning. Currently, one of the biggest concernswith the AMVs is the horizontally correlated error asobserved by Bormann et. al. (2003). These errors maybe related to the correlated errors in the NWPtemperature fields, and hence a forecast independentapproach for height assignment would be highlydesirable. The existence of two water vapour channelsenables a derivation of cloud heights that is completelyindependent of forecast data and is a potential futureimprovement.

The new capability of Meteosat-8 provides alreadynow 5 times more wind vectors than the first generationsatellites. Monitoring of the data has shown that alreadyat this early stage of operations the quality of the

Figure 6: Comparison of various height assignment methods(top) and colocated wind speeds (Meteosat-8 / radiosondes,bottom)

Meteosat-8 winds are comparable to Meteosat-7, andthat with appropriate filtering or data selection thequality is even higher. This is illustrated in Figure 7where the quality of the derived vectors from the twosystems is defined by the normalised RMS (NRMS)difference to colocated radiosonde wind measurementsfor different quality categories as determined by theAutomatic Quality Control. The normalisation isperformed by the mean wind speed of the colocatedobservations. The figure also presents the number ofwinds for the different quality classes. It can be seenthat for a specific quality class the NRMS is higher forMeteosat-8, but if a higher quality class is selected, theNRMS is equal or better and that at the same time thenumber of vectors is still higher. The selection ofdifferent quality classes for different satellites is a typicalbehaviour of the used Automatic Quality Control (AQC)scheme. the increased positive impact of Meteosat-8AMV data for regional NWP has been demonstrated byBonavita and Torrisi (2004).

The high density of the Meteosat-8 AMV field isillustrated in Figure 8 for two examples, which bothshow areas of high curvature and high spatial variation.Figure 9 presents an example of high level windsderived in a jet stream area using infrared window,water vapour, and visible image data.

Figure 7: Meteosat-8 (MSG) and Meteosat-7 (MTP) IR highlevel AMV quality as determined by the NRMS difference(solid line) derived against colocated radiondes. Additionallythe number of winds (dshed line) for all categories is shown.(squares = Meteosat-8, triangles = Meteosat-7).

Figure 8: High level Atmospheric Motion Vectors (AMV)derived from cloudy targets with the WV channel overcentral Europe (top) and low level AMVs derived with theVIS channel over the South Atlantic (bottom).

MSG vs. MTP R/S Collocations, IR, High level

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0 10 20 30 40 50 60 70 80 90

Quality

NR

MS

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f Col

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Figure 9: A high level jet-stream area depicted by AtmosphericMotion derived from cloudy targets with the IR channel (red),the water vapour channel (blue), and the visible channel(green)

3.10 The Global Instability Index Product

The Global Instability Index (GII) product isoperationally derived with a statistical regression modelfor which the parameters have been derived with aneural network approach. The current algorithm workswell for the Total Precipitable Water (TPW) index asverified with comparisons to a physical retrieval methodand to radiosonde observations. However, the reliabilityof the other parameters (K index, KO index, LiftedIndex, Maximum Buoyancy) with the current algorithm issignificantly lower. Therefore, during 2004/2005, thecurrent statistical method will be replaced by a physicalretrieval, based on an optimal estimation method,(König (2002)). Figure 10 gives an example of the TotalPrecipitable Water as one of the derived airmassparameters.

3.11 The Clear Sky Reflectance Map

The Clear-Sky Reflectance Map (CRM) is basedon the average clear sky reflectance derived in cloud-free areas using three images around a central timeslot. The observed daily clear sky reflectances areaveraged over a 10-day period. Figure 11 gives anexample of a CRM product derived with data from the0.6 and 1.6µm channels.

Figure 11: The Clear-Sky Reflectance Map derived with the0.6µm (top) and 1.6µm (bottom) channel from cloudfree dataaveraged over a 10-day period in June 2004.

Figure 10: The Total Precipitable Water derived on aglobal scale as one of the GII products (8September 2003, 1200 UTC)

4. THE EUMETSAT POLAR SYSTEM (EPS)

The EUMETSAT Polar System will complement theUS provided system and together with the lattercontinue the present NOAA polar orbiting satellitesystem in the frame of the Initial Joint Polar System(IJPS) (Figure 12).

The future EUMETSAT satellites of this new polarsystem are the METOP (METeorological OPerationalSatellite) satellites, jointly developed with ESA. They willprovide high-resolution sounding and also high-resolution imagery in global coverage. The first two ofthe METOP satellites will be operated in the frameworkof the Initial Joint Polar System (IJPS) together with thepresent NOAA system of the United States. TheseMETOP-1 and METOP-2 spacecraft are foreseen for asun synchronous orbit for the 9:30 AM equator crossing(descending node). The first METOP satellite iscurrently scheduled for launch towards the end of 2005.With the third spacecraft the converged military andcivilian US-Systems NPOESS will form together withMETOP-3 the Joint Polar System (JPS). METOP-3 isplanned to be a recurrent copy of METOP-1 and 2,however no HIRS/4 will be flown on METOP-3. Payloadoptions for the imager and the microwave soundinginstruments are VIRI-M and ATMS in case AVHRR andAMSU are not available and are currently beinginvestigated. The EPS programme is planned to cover14 years of operation.

4.1 The EPS Products

The main mission objectives for the system areOperational Meteorology and Climate Monitoring. Inaddition the Search and Rescue Instruments and alsothe Space Environment Monitor are on-board thespacecraft. To achieve the mission objectives theMETOP satellites contain a wide variety of instruments,including a number of sounding instruments. The

HIRS/4 (High Resolution Infrared Radiation Sounder)instrument, the AMSU-A (Advanced MicrowaveSounding Unit -A) and the MHS (Microwave HumiditySounder) instrument asuccessor instrument of AMSU-B, will provide the continuity to the current soundingcapabilities onboard the NOAA-15, -16 and -17spacecraft. Highly improved sounding capability will beprovided by the IASI (Infrared Atmospheric SoundingInterferometer) instrument, both in accuracy and also invertical and horizontal resolution. The sounding payloadis complemented by the AVHRR/3 (Advanced VeryHigh-Resolution Radiometer) high-resolution imager.The EPS products comprise centrally processed Level 1products from all instruments, Level 2 soundingproducts from ATOVS and IASI, and a large number ofLevel 2 and higher products from the distributedEuropean Satellite Application Facility (SAF) network.

Figures 13 and 14 present examples of ozonederived with ERS-2 and scatterometer wind dataderived with QuickScat. Similar data will also beobtained by EPS. Ozone data will be provided byGOME-2, which, in addition to ozone profiling, can beused for other trace gas retrievals. The wind vector fieldnear the surface over the oceans will be provided withan Advanced Scatterometer (ASCAT).

Other EPS instruments are the GRAS (GPSRadio-Occultation Atmospheric Sounder) soundingsystem. GRAS uses the information on the atmosphereand ionosphere contained in the radio-occultation of theGPS navigation satellite signals. For further informationand examples on the capabilities of the EPSinstrumentation see the EUMETSAT web pageswww.eumetsat.de.

NOAA N & N’ p.m.

METOP 1 & 2 a.m.

Global datadump

Global datadump

NOAA GS

EPS GS

NOAA global dataMETOP blind orbits

METOP global dataNOAA blind orbits

Blind orbitsdata dump

CNESIASI TEC SAF

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EUMETSAT POLAR SYSTEM

Figure 12: EPS as part of the initial Joint Polar System

Figure 13: : GOME/ERS-2 30 November 1999. Global ozonetotal column concentration (courtesy of DLR)

Figure 14: Surface wind field derived with QuickScat data(courtesy KNMI)

5. OPTIONAL PROGRAMMES

The Optional Jason-2 programme on altimetryentered into force on 27 June 2003, after a sufficientnumber of EUMETSAT Member States have subscribedto the programme. The Jason-2 altimetry programme isthe first EUMETSAT optional programme to implementEUMETSAT’s amended Convention from November2000. This Convention expands the mandate given toEUMETSAT by its Member States in operational climatemonitoring and the detection of global climatic changes.EUMETSAT will contribute to the operations of theoverall system and to the generation of the data stream,using a European Earth Terminal and a real time

processing chain. The programme is EUMETSAT’scontribution to a joint undertaking with the FrenchCentre National d’Etudes Spatiales (CNES), the USNational Oceanic and Atmospheric Administration(NOAA) and the US National Aeronautics and SpaceAdministration (NASA), known as the Ocean SurfaceTopography Mission (OSTM). It is an important elementin the overall altimetry data system and will bring highprecision altimetry to a full operational status, as aresult of a balanced co-operation between Europe andthe USA.

6. CONCLUSIONS

During commissioning of MSG-1 the mainobjective of the product validation has been to verify theimplementation of the scientific algorithms and tovalidate the baseline products. Many of the capabilitiesof the SEVIRI instrument are not yet fully exploited.Further improvements in cloud height assignment aswell as in the resolution of products is expected. Alsothe use of new and better algorithms, e.g. a physicalretrieval of the Global Instability Index and the use ofoptimal estimation for the derivation of Total Ozone arecurrently being explored. The current scenes and cloudanalysis scheme does also not fully utilise the multi-spectral information and could be further enhanced toderive cloud properties like optical depth. Thisinformation may turn out to be crucial for severalproducts e.g. for the AMVs in order to properly establishthe level at which the clouds travel. Many improvedalgorithms and products are developed and distributedby the EUMETSAT Satellite Application Facilities(SAFs). For some further details on the products andalgorithms planned within the SAF context consult theEUMETSAT WEB pages www.eumetsat.de.

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Bonavita, M. and L. Torrisi, 2004: Use Of Satellite WindVectors In The Italian Weather Service NWP SystemCurrent Status And Perspectives. Proc. SeventhInternational Winds Workshop, Helsinki, Finland,EUMETSAT, in press

Govaerts, Y., 2003: Vicarious Calibration ofMSG/SEVIRI Channels. Proc. The 2003 EUMETSATMeteorological Satellite Conference, Weimar,Germany, EUMETSAT, EUM P39

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