PowerPoint Presentation

EUMETSATs Network of Satellite Application Facilities

Lothar Schller

EARSel Workshop Bern February 2014CryoClim Workshop Oslo 7 November 2013OutlineEUMETSATSAF Programmatic frameworkSAF concept and establishmentSAF structure and processesSAF product characteristicsCurrent SAF NetworkCommitted SAF snow productsSAF interaction with usersSAF phasingResearch and/to Operations

Slide: #EARSel Workshop February 2014 Bern

METOP A-B(LOW-EARTH, SUN SYNCHRONOUS ORBIT)

EUMETSAT POLAR SYSTEM/INITIAL JOINT POLAR SYSTEM

JASON-2(LOW-EARTH, 63 INCL. NON SYNCHRONOUS ORBIT)

OCEAN SURFACE TOPOGRAPHY MISSION

METEOSAT 8-9-10 (2nd GENERATION)(GEOSTATIONARY ORBIT)

TWO-SATELLITE SYSTEM: - METEOSAT-9: FULL DISK IMAGERY MISSION AT 0 (15 MN)

- METEOSAT-8: RAPID SCAN SERVICE OVER EUROPE AT 9.5E (5 MN) METEOSAT 7 (1st GENERATION) (GEOSTATIONARY ORBIT)

INDIAN OCEAN DATA COVERAGE MISSION AT 575 E

Current EUMETSAT satellites

3EUMETSAT ground segment overview

EUMETSAT geostationary systemsMeteosat satellitesJason-2Optional programme for ocean altimetryEUMETSAT Polar SystemMetop satellitesData acquisitionand controlPre-processingEUMETSAT HQData CentreEUMETSAT HQ DarmstadtMeteorological product extractionEUMETSAT HQ DarmstadtSatellite Application Facilitieswithin the Member StatesThird-party data sourcesData disseminationvia EUMETCastApplicationsGroundSegmentCentralised processingand generation of productsDecentralised processingand generation of productsUsers

Flight OperationsNOAA satellitesOther satellitesSlide: #EARSel Workshop February 2014 Bern4What is a SAF?SAF = Satellite Application Facilitypart of the EUMETSAT application ground segmentcomplement production of standard meteorological products at EUMETSAT central facilityproviding products and services to usersspecialised on topics and themeslocated at Weather Services in EUMETSAT Member and Co-operating Statesdeveloped and operated by consortium of partners Satellite ApplicationFacilities SAFsSlide: #EARSel Workshop February 2014 BernWhat is a SAF?SAF is developed and operated in a consortiumlead by a Leading Entity (a National Meteorological Service)involving partners in EUMETSAT Member and Co-operating statesNational Meteorological ServicesOther operational organisationsUniversities and Research InstitutesSAFLeading EntityPartnerInstitutePartnerInstitutePartnerInstitutePartnerInstitutePartnerInstituteSlide: #EARSel Workshop February 2014 BernUser siteSAF

For each pixel do derive datadoneendHow do SAFs work? Providing data productsUserSome SAFs provide data productsSlide: #EARSel Workshop February 2014 BernHow do SAFs work? Providing Software products User siteUserSAF

For each pixel do derive datadoneendFor each pixel do derive datadoneendSome SAFs provide softwareproductsSlide: #EARSel Workshop February 2014 Bern8Nature of SAF productsThe objective of SAFs is to provide operational products.

Operational means:

Continuity of product provision Continuity of product improvementsContinuous quality monitoringCommitted user servicesValidation and review before official releaseComplete Documentation of Products, Algorithms, Validation Results...Slide: #EARSel Workshop February 2014 BernEUMETSATs 8 Satellite Application Facilities

Support to Nowcasting and Very Short Range Forecasting

Ocean and Sea Ice

Climate Monitoring

Numerical Weather Prediction

Land Surface Analysis

Ozone and Atmospheric Chemistry Monitoring

Radio Occultation Meteorology

Support to Operational Hydrology and Water Management

SAF Consortium Member67854321Slide: #EARSel Workshop February 2014 BernHydrology SAFSAF on Support to Operational Hydrology and Water ManagementLeading Entity: Italian Meteorological Service (USAM)SAF products focuses onprecipitationsoil moisturesnow parametersutilisation of these parameters in hydrological models and NWP

Slide: #EARSel Workshop February 2014 BernCurrently available snow products from H SAF

operational: Snow detection (snow mask) by VIS/IR radiometry Snow water equivalent by MW radiometry

pre-operational: Effective snow cover by VIS/IR radiometry Snow status (dry/wet) by MW radiometrySlide: #EARSel Workshop February 2014 BernSnow detection by VIS/IR radiometry

Binary map of snow / no-snow situation. Using Meteosat/SEVIRI observationDifferent methods used for flat/forested and mountainous regionsDaylight product, output result every 24h Probability Of Detection (POD): Flat / Forested areas: 85 % Mountainous areas: 70% False Alarm Rate (FAR): Flat / Forested areas: 15 %, Mountainous areas: 20%

Slide: #EARSel Workshop February 2014 BernSnow status (dry/wet) by MW radiometry

status of the snow mantle, whether it is wet or dry and, in time series, thawing or freezing. Multi-channel MW observations SSMIS on DMSP (middle frequencies), and the algorithm is based on thresholding. Requirements: Hit Rate (HR): 80 % False Alarm Rate (FAR): 10 % Slide: #EARSel Workshop February 2014 BernEffective snow cover by VIS/IR radiometry

Multichannel (VIS, NIR, IR) analysis AVHRR on NOAA and Metop satellites Requirement: 65% overall accuracy Validation against snow observing stations

Slide: #EARSel Workshop February 2014 BernSnow water equivalent by MW radiometry

Maps of snow water equivalent derived from MW measurements SSMIS on DMSP sensitive to snow thickness and density. Algorithm based on assimilating MW brightness temperatures of several channels at frequencies with different penetration in snow, into a first-guess field built by the (sparse) network of stations measuring snow depth. Requirements: Flat / Forested areas: 20mm Mountainous areas: 25mm

Slide: #EARSel Workshop February 2014 BernInteraction with Users: Services provided to usersUser

Near-Real-Time Products

EUMETCastInternet(FTP, WEB)Software and off-line productsEUMETSATData CentreSAFFor each pixel do derive datadoneend

DocumentsATBDProduct User ManualValidation ReportSlide: #EARSel Workshop February 2014 BernInteraction with Users:Feedback provided to SAFsUser

UserHelpdesk

WorkshopsBeta userprogramme

SAFFor each pixel do derive datadoneend

Slide: #EARSel Workshop February 2014 BernSAF Network PhasingSAFNWC SAFOSI SAFO3M SAFNWP SAFCM SAFROM SAFLSA SAFH-SAF1997 2007 2012 2017 2022Developments and Initial Operations CDOP CDOP-2 CDOP3

MSGEPSMTGEPS-SGSlide: #EARSel Workshop February 2014 BernSynergy: Research -> OperationsSAFs embedded programmatically in the EUMETSAT mandatory programmes, currently secured until 2022. Current proposal for a EPS Second Generation Programme foresees funding for SAF development and operations until 2032.The purpose and commitment of SAFs is to:deliver operational products addressing requirements of the EUMETSAT member statesdevelop new products implementing mature retrievals validated by Research (transition from Research to Operations)Next SAF development and operations phase (CDOP-3 for 2017-2022): proposals expected end of 2015.

Slide: #EARSel Workshop February 2014 BernSAF implementing mature research algorithmOperational DataproductsOwn Algorithm DevelopmentImplementation in processing chain

Slide: #EARSel Workshop February 2014 BernSAF implementing mature research algorithmOperational DataproductsOwn Algorithm DevelopmentImplementation in processing chainExternal Algorithm DevelopmentOperational Dataproducts

Slide: #EARSel Workshop February 2014 BernSummarySAF = Satellite Application Facilityproviding products and services to users on an operational basis with a long-term perspectivespecialised on topics and themes located at Weather Services in EUMETSAT Member and Co-operating Statesdeveloped and operated by consortium of partners part of the EUMETSAT application ground segmentcomplement production of standard meteorological products at EUMETSAT central facility in DarmstadtData and software are usable free of charge

Slide: #EARSel Workshop February 2014 BernBACKUP SLIDESSlide: #EARSel Workshop February 2014 BernWhy SAFs ? Facts and ObjectivesFacts:New generation of Meteorological Satellites (MSG and Metop as well as MTG and EPS-SG) have much wider areas of applicationspecific expertise available in EUMETSAT's Member and Cooperating States

Objectives:

SAF concept encourage the utilisation of existing skills and infrastructure in Member- and Cooperating States for developing geophysical data products and servicesFacilitating cost-effective exploitation by ensuring services are distributed in the most appropriate way SAFs improve the ability of EUMETSAT Member States to exploit satellite data Fostering development of cooperation with non-Member States and other organisationsSlide: #EARSel Workshop February 2014 BernNowcasting SAFSupport to Nowcasting and Very Short Range Forecasting (NWC SAF) established to utilise the new data from Geostationary satellites (Meteosat and others) and the polar platforms (Metop, NOAA, Soumi NPP) for enhancing NowcastingDevelopment of Software packages for the operational extraction of products relevant to Nowcasting and for local installationLeading Entity is the Spanish Meteorological Agency AEMET in MadridRegular updated software packages since October 2004

Rapidly developing Thunderstorm Product

Slide: #EARSel Workshop February 2014 BernOcean and Sea Ice SAFOcean and Sea Ice (OSI) SAF routinely produces and disseminates products characterising the ocean surface and the energy fluxes across the sea surfaceOperationally produces information on the sea ice characteristics (extend, concentration, )Leading Entity is Mto-France in LannionOSI SAF distributes near real-time products based on NOAA, MSG, Metop, Oceansat, DMSP and GOES data

Sea surface temperatureSea ice concentration

Slide: #EARSel Workshop February 2014 BernOzone SAFSAF on Ozone and Atmospheric Chemistry Monitoring (O3M SAF)developed for the processing of data on ozone, other trace gases, aerosols and ultraviolet radiationEmphasis on the Global Ozone Monitoring Experiment (GOME-2) and IASI on EPS (Metop)Leading Entity is the Finnish Meteorological Institute FMI, HelsinkiFirst release of products in Summer 2007

Slide: #EARSel Workshop February 2014 Bern

Climate Monitoring SAFSAF on Climate Monitoringgenerates and archives high-quality data-set for specific climate application areasCurrently concentrates on:cloud parametersradiation budget parametersatmospheric humidityLeading Entity is the German Weather Service DWD, OffenbachNOAA-AVHRR based data operationally produced since November 2004, MSG based data from October 2005, Metop data used since 2009.Climate Data Records: 20 years of SSM/I Water Vapour information released in 2009.NOAA-AVHRR based 20 years of homogeneous data record (clouds, surface radiation) released 2012Upcoming Climate Data Records based on SEVIRI, ATOVS, SSM/I

Slide: #EARSel Workshop February 2014 BernNWP SAFSAF on Numerical Weather Prediction (NWP SAF)aims at increasing the benefits to Met.-Services from Numerical Weather Prediction (NWP)develops advanced techniques for the effective use of satellite datadevelopment and maintenance of RTTOV radiative transfer model, ATOVS and AVHRR Pre-processing Package (AAPP) Leading Entity is the UK MetOffice, Exeter

Slide: #EARSel Workshop February 2014 Bern

ROM SAFSAF on Radio Occultation MeteorologyGRAS: Global Positioning System (GPS) Receiver for Atmospheric Sounding flown on EPS/Metop satellitesnear real-time and offline: sounding data (temperature, pressure, humidity) corresponding validation products, andassimilation softwareThe Leading Entity is the Danish Meteorological Institute DMI, CopenhagenSoftware packages released since 2007, first NRT product dissemination in October 2008

Slide: #EARSel Workshop February 2014 BernLand Surface Analysis SAFSAF on Land Surface Analysis (LSA SAF)established to increase the benefit from MSG and EPS data related to land, land-atmosphere interaction and biospheric applicationsGenerates operationally data services related to Surface Radiation, Vegetation and wild fireLeading entity is the Portuguese Institute for Meteorology IM, Lisbon

Slide: #EARSel Workshop February 2014 BernHydrology SAFSAF on Support to Operational Hydrology and Water ManagementLeading Entity: Italian Meteorological Service (USAM)SAF products focuses onprecipitationsoil moisturesnow parametersutilisation of these parameters in hydrological models and NWP

Slide: #EARSel Workshop February 2014 BernSAF Development and Service ModelBeta-UserTasksSAF Researchand DevelopmentSAF OperationsInitial ObjectivesInitial and new User NeedsNeeds for Changes and ImprovementsDelivered SAF ServicesValidated ProductsUser Provided Operational FeedbackSlide: #EARSel Workshop February 2014 Bern CDOP review logicDevelopment Life Cycle of new operational chainsProductsDistributionUser SupportProvisionOperationsReportingORRPrevious Phase ORNew/enhancedProductsDefinitionPCRProductsImprovementImplementationCommitted operations baselineupdated baselineupdated baselineUser Workshops and ConsultationProduct Consolidation ReviewsOperational Readiness ReviewsOperation ReviewsRRRequirementsReviewsSAF Development and Service ModelSlide: #EARSel Workshop February 2014 BernCDOP-2: Second slice of the Continuous Development and Operations PhaseSAF Network after 2012:

funding for 5 years of CDOP-2 and 5 more years for a CDOP-3 foreseen within the MTG budgetCDOP-2 started March 2012Operational Continuation of existing productsDevelopment of new products and services (list of committed SAF products soon to be available).Preparation activities of Meteosat Third Generation (MTG) based productsExploitation of synergies within the Application Ground Segment (SAF Network + Central Facilities)Reprocessing and homogeneous data set generation (e.g. for climate applications)Integration and interfacing with other initiatives: GMES, WMO SCOPE-CM, EUMETCal, GHRSST, GODAE, ESA CCI, etc.Slide: #EARSel Workshop February 2014 BernExamples for new Developments in CDOP-2Long-term satellite data records:CM SAF: Meteosat since 1982O3M SAF: GOME-2 since 2006OSI SAF: Sea Ice since 1978, Sea Surface Temperature since 2002ROM SAF: Reprocessing of all GNSS-RO missions (since 1995)LSA SAF: reprocessing of SEVIRI products (since 2002)

Slide: #EARSel Workshop February 2014 Bern38

Application: PV GIS

Country-scale and Europe / Africa MapsSlide: #EARSel Workshop February 2014 Bern38PV GIS also provides spatial maps for each European country of the solar energy potential (here for optimally-inclined PV modules for Germany) and the PV potential for Europe. CM SAF data is used to generate these freely available maps. 39

Application: SMA Solar Checker

www.sma.de Products Plant Planning

Slide: #EARSel Workshop February 2014 Bern39The freely available Solar Checker iPhone app by the company SMA uses the PV GIS data base to derive the solar energy potential: Put the iPhone on your roof; using GPS, compass and balance, the App determines the orientation of the roof and calculates the estimated energy yield and the estimate rate of return for a potential PV module. Examples for new Developments in CDOP-2Bringing external developments into operations:O3M SAF: IASI products developed at LATMOSLSA SAF: NDVI (vegetation index from VITO, Belgium)NWC SAF: Precipitation based on cloud microphysics (KNMI)

Slide: #EARSel Workshop February 2014 BernV2013, GEO-CRRv4.0 outlook41CTMP Function: Based on the one developed by Roebeling and Holleman*

Cloud Top Microphysical Properties used by this algorithm:Phase (Ph)Effective radius (Reff)Cloud water path (CWP)

Two steps:1.- Delimitation of the precipitation area2.- Assignment of rain rates

(*) Roebeling, R. A. and I. Holleman, 2009: SEVIRI rainfall retrieval and validation using weather radar observations. J. Geophys. Res., VOL. 114, D21202.

Liquid water path (LWP)Ice water path (IWP)Slide: #EARSel Workshop February 2014 Bern

PREPARATIONS FOR V2013 DAY-TIME ALGORITHMExample over Spain4242

CRR (3D Matrices)

CRR (CTMP function)Radar (PPI)22th August 2008 at 14:00 UTCSlide: #EARSel Workshop February 2014 BernPREPARATIONS FOR V2013 DAY-TIME ALGORITHM Comparison: 3D Matrices vs CTMP function43

43Dataset:Spain: 46 days, May-September 2008, 10:00 14:00 UTC every 30 min

RMS error decreases with CTMP function because precipitation maxima are better located and precipitation pattern is more accurate.

False alarms increases 1% while probability of detection increases 24% for this dataset.Slide: #EARSel Workshop February 2014 BernExamples for new Developments in CDOP-2New satellite sensors:OSI SAF: OSCAT based wind productsH SAF: GPM dataNWC SAF: Soumi NPP VIIRS, additional GEO imagerROM SAF: cosmicPreparation for MTG and EPS-SG

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Development of PR-OBS-2Initial VersionOperationalH02Neural Network algorithm trained with a global CRD: not optimized for Europe/Mediterranean area; using different CRM than H01Ver. 1Under verification

Operational Dec. 2012H02Correction of corrupted channels [i.e., MetOp-A, AMSU-A Channel 7 (54 GHz)]Generation of a new version of the neural network algorithm, trained by the same CDRD as H01: Representativeness for Europe/Mediterranean basinHarmonize products from cross-track and conical MW scannersImprovement of the screening procedures (under particularly unfavourable backgrounds (e.g., snow cover, coastlines)Quality flag and indication of phase (under verification)Ver. 1H02AH-SAF AreaVer. 1H02BExtension to Full Disk Area (exp. op. 2014) Ver. 2H18AH-SAF area: introduction of SSI index. (exp. op. 2017)Ver. 2H18BFull Disk Area (exp. op. 2017)CDOP-1CDOP-2Precipitation rate at ground by MW cross-track scannersRome, Italy Flash Flood20 Oct 20118:20 UTCMetOp-AAMSU/MHSNN trained with Cloud Dynamics and Radiation Database CDRD

New VersionNN trained with global Cloud Radiation DatabaseCRDSlide: #EARSel Workshop February 2014 BernConcept of the Hydrological Validation

Slide: #EARSel Workshop February 2014 BernH-SAF workshop, 29.11 - 02.12 2011, Roma46Test catchments of the Hydrological Program

Slide: #EARSel Workshop February 2014 BernH-SAF workshop, 29.11 - 02.12 2011, Roma47Examples for new Developments in CDOP-2New product developments:NWC SAF: Convection Initiation, Extrapolated Imagery, precipitation approachO3M SAF: trace gases (Formaldehyde, BrO, ..) and aerosol properties from GOME-2LSA / CM SAF: long-wave surface radiation from SEVIRI

Slide: #EARSel Workshop February 2014 BernGrfico10.620.590.130.090.910.72.312.06

3D MatricesCTMP FunctionAccuracy Statistics

RFR_CRR_2008MAYSEP_INTMat_3D_3d3D MatricesCTMP FunctionN226,865250,228MEAN0.620.59ME0.130.09MAE0.910.7RMSE2.312.06FAR22.1723.36POD54.5878.85CSI47.2463.57PC67.6875.88

RFR_CRR_2008MAYSEP_INTMat_3D_3d00000000

3D MatricesCTMP FunctionAccuracy Statistics

00000000

3D MatricesCTMP FunctionCategorical Statistics

Grfico222.1723.3654.5878.8547.2463.5767.6875.88

3D MatricesCTMP Function(%)Categorical Statistics

RFR_CRR_2008MAYSEP_INTMat_3D_3d3D MatricesCTMP FunctionN226,865250,228MEAN0.620.59ME0.130.09MAE0.910.7RMSE2.312.06FAR22.1723.36POD54.5878.85CSI47.2463.57PC67.6875.88

RFR_CRR_2008MAYSEP_INTMat_3D_3d00000000

3D MatricesCTMP FunctionAccuracy Statistics

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3D MatricesCTMP FunctionCategorical Statistics