Workshop: Dados e Produtos Meteosat e MetOp em Tempo Quase Real via EUMETCast para Aplicações Meteorológicas e Ambientais. Stephen English CPTEC-INPE

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1 Workshop: Dados e Produtos Meteosat e MetOp em Tempo Quase Real via EUMETCast para Aplicações Meteorológicas e Ambientais Applications in Atmospheric Data Assimilation for Numerical Weather Prediction Stephen English CPTEC-INPE

2 Contents 1) Os produtos EUMETCast - Relevância para assimilicao 2) Uso de dados por assimilicao - CPTEC e o resto do mundo 3) Que dados queremos via EUMETCast? 4) Conclusões

3 Objective The objective of this workshop is to provide for the academic and scientific communities the distribution of near-real time data and weather satellite-derived products from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) ś EUMETCast system and their applications to the atmospheric convection, atmospherebiosphere interaction, agriculture and assimilation into meteorological models.

4 EUMETCast for DA for NWP EUMETCast-Americas = 90 products (48 weather) EUMETCast-Europe = 177 products (102 weather) EUMETCast-Americas + Level 1 SEVIRI channel radiances + Level 2 products: atmospheric temperature and humidity ATOVS + Level 2 products: Surface fields (SST, Sea ice, fire, vegetation) + Level 2 products: ASCAT winds + Level 2 products: Clouds, stability, preciptation + Level 2 products: FY-2E (AMVs, clouds) EUMETCast-Europe + EUMETCast-Americas + Level 1 raw data: ATOVS, IASI, GRAS, GOME, MODIS, ASCAT + Level 1 Clear sky radiances + Level 2 Polar AMVs + Level 2 IASI products (T, q, greenhouse gases, emissivity)

5 Can we get the data another way? ATOVS Dados globais so esta disponivel no CLASS server (para FTP) Dados de RARS no GTS (NOAA-18, 19) Dados de RARS e globais de MetOp nao esta disponivel no GTS em Brazil IASI So no CLASS server (NOAA) para FTP IASI nao esta disponivel ainda no GTS em Brazil Entao nao existe um metodo operacionais para todos esses dados para America de Sul nesse momento...

6 AIRS ~4,500 AMVs ~9,000 IASI ~3,000 GPSRO ~800 SSMIS ~4,500 ATOVS ~36,000 Scat ~16,500 Crown copyright Met Office SSM/I ~4,500

7 Observacoes agora e 12 anos atras > 12 years ago Mass 2 HIRS, 2 MSU Wind Some Geo AMVs Humidity 2 HIRS Cloud and rain, snow 2 AVHRR 2 SSM/I Surface 1 ERS Scat, (sea ice, SST, Surface 2 AVHRR wind, snow, vegetation) Now or soon (METOP, POESS, DMSP, Research) 3 HIRS 6 AMSU-A 2 SSMIS 2 AIRS/IASI 9 GPSRO 5 Geo AMVs 6 AMVs 3 HIRS 6 AMSU-B + MHS 5 SSMIS + SSM/I + AMSR-E + TMI 2 AIRS/IASI Many Ground based GPS 5 SSMIS + SSM/I + AMSR-E + TMI 4 AVHRR 1 SEVIRI plus other Geo imagers 4 Scat-like (QuikScat, ERS, ASCAT, WindSat) 5 SSMIS + SSM/I + AMSR-E + TMI 4 AVHRR 1 SEVIRI plus other Geo imagers 1 L-band SMOS (9/9/09) (plus other L-band missions). MetOp-B ATMS/CrIS GIFTS/STORM MTG-IRS Crown copyright Met Office

8 WMO OSE Workshop, Geneva 2008

9 Assimilation of observations IR (IASI, AIRS, HIRS, SEVIRI) MWS (AMSU, MHS) MWI (SSMIS, AMSR) AAPP or similar 1D-var AMVs (Geo + polar) Conventional GPSRO+WV Scatterometer Bias correction y* = y + c LETKF interface Calcular W(x fi ) e H(x fi ) Subset of radiances y + ancillary (nuvens T*, Є) NWP analysis ensemble x ai 4D-LETKF x ai =x fi + W(x fi )(y*-h(x fi )) Forecast model x fi = M(x ai ) NWP short range forecast ensemble x fi

10 Weight to background vs observations New Obs Background (old obs)

11 1000s of channels, use 150 Red Used (Sea/Land, Clear/MWcloud) Yellow Used (Sea/Clear only) Blue Used (1D-Var preprocessor only) Cyan Rejected Green / Lime Rejected water vapour channels

12 Complex Radiative Transfer (2) Algumas vezes os Jacobians estao muito complicado e dificil usar... Fiona Hilton

13 Clouds: 1D-var analysis to select channels CTP, CF CTP CF Analisar nuvens em 1D- Var Escolhar canais que nao tem muito sensitividade de nuvens LETKF ou 4D-Var Usar radiancias em 4Dvar ou LETKF Esse sistema usar um modelo bem simples dos nuvens, mas e possivel fazer esse modelo mais complicado. From Ed Pavelin, Met Office

14 Adjoint-sensitivity: que canais estao mais importante? Broadband IR (HIRS) Best -1.1 J/kg Microwave (AMSU-A) Best -0.5 J/kg Hyperspectral IR (IASI) Best -1.7 J/kg Adjoint sensitivity is a measure of the ability of each observation to reduce forecast error for a specified metric. From Richard Marriot, Met Office

15 Dados para mandar no EUMTCast Que e oa canais certo para um centro tem dependencia que eles querem fazer, detalhes de sistema, que outros dados eles estao usando. Entao mesmo que centros vao usar talvez menos de 300 canais melhor mandar tudo Mas tudo para todos satelites e demais... Mandar Principal Components E um medo que PCs vai perder algumas informacoes, e.g. por GHGs

16 Erros e importante Se nos nao sabemos os erros de background e observacoes e possivel fazer um analise que e pior que background. => Ensemble Kalman Filter para erro de => Ensemble Kalman Filter para erro de background => Queremos ter erros dos observacoes tambem

17 Sensitivity to errors in description of error Analysis error Danger zone Analysis gains information from the observations Using fixed B = 1 Using true B Analysis error exceeds prior knowledge (despite good observations!) D a n g Suboptimal Suboptimal Optimal True Background Error Based on Hilton and Eyre 2010

18 Que nos precisamos no EUMETCast por assimilicao no modelos? Observacoes Hiperspectral sondas com qualidade de IASI Radiancias brutos Informacao em nuvens e superficie, GHGs Os erros dos observacoes Sondas microndas com qualidade de AMSU e MHS Radiancias brutos Informacao em precipitacao, superficie The 1b radiances are a product and we want the best quality, best characterised 1b radiance product possible. Near real time Rapid dissemination: goal timeliness 30 minutes and send data to NWP centre partners as soon as available to Sat Agencies so cal/val is partnership between NWP centres and Sat Agencies.

19 Conclusions EUMETCast-Americas currently supports SEVIRI and some derived products Use of SEVIRI in dat assimilation is limited at present Use is mostly for imagery, products or regional model assimilation Other EUMETCast products are of little value to assimilation Assimilation interest is for raw radiances for sounders (ATOVS, IASI) It is possible to acquire some of these data via GTS or FTP But EUMETCast may be more complete, robust, faster and there is a desire to have a single method for distribution of satellite data and products.