Data Assimilation: Observing System
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1 Data Assimilation: Observing System Dirceu Luís Herdies CPTEC/INPE Intensive Course on Data Assimilation Buenos Aires - 03/11/2008
2 Satellites Numerical Weather Polar Orbit Operacional Forecast Very simple models of the atmosphere Atmosphere Atmosphere Surface First numerical weather forecast with ENIAC - Univ. of Princepton Geoestacionar y Orbit Série TIRUS Polar Órbit For research Used Operacionally Beginning of assimilation of Satellite data in NWP Série NIMBUS Série ATS Série DMSP ECMWF Série GOES Atmosphere Surface Ocean and ocean ice Série NOAA Série ERBS Atmosphere Surface Ocean and ocean ice Aerosol Carbon CPTEC TRMM (1997) METOP 2006 Terra 1999 Atmosphere Surface Ocean and ocean ice Aerosol Carbon Vegetation Dynamic Atmospheric Chemistry NPOES 2011 Quikscat 1999 NPP 2009 MSG Aqua MTG ( ) Evolution of numerical Modeling components Envisat 2002 Série GOES R
3 ECMWF Data Assimilation: Observing System
4 Observational data There is a wide variety of observations, with different characteristics, irregularly distributed in space and time. Data Assimilation: Observing System ECMWF-2008 [email protected]
5 Conventional data at CPTEC/INPE Data Assimilation: Observing System
6 Operational Webpage Data Assimilation: Observing System
7 GMAO/NASA Total Observation Types Main Observing Systems Used in the Processed: 4,061,237 GEOS-5 Analysis on 00 UTC 11 Nov 2007 Assimilated: 1,669,057 Aircraft Ozone 8320 Scatterometer AIRS ATOVS Profilers Radiosonde SSM/I SYNOP/Ships Buoys SatWinds TMI
8 Operational Models at CPTEC Global Model AGCM 63 Km 63 Km Regional Model ETA 20 Km 20 Km Data Assimilation: Observing System
9 Data Assimilation: The bridge between modelling and observations xa = xb + K [yo - H(xb)] Observation Operator (H) provides the link between the model variables and the observations Data Assimilation: Observing System [email protected]
10 Data Assimilation: Observing System
11 Conventional Data Coverage 15 June UTC 15 June UTC Data Assimilation: Observing System
12 Satellite Data Coverage 15June UTC 15 June UTC Data Assimilation: Observing System
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14 Regional Model RPSAS Eta 40 km
15 Regional Model RPSAS Eta 40 km
16 Data currently used - ECMWF Conventional SYNOP/SHIP/METAR Ps, Wind-10m, RH-2m AIREP Wind, Temp AMVs (GEO and POLAR) Wind DRIBU Ps, Wind-10m TEMP/DropSONDE Wind, Temp, Spec Humidity PILOT Wind profiles Profilers: Amer./Eu./Japan Wind profiles Satellite ATOVS and AIRS HIRS, AIRS and AMSU radiances SSM/I Microwave radiances (clear-sky) TCWV in rain and clouds Meteosat/MSG/GOES Water Vapour IR-channel QuikSCAT and ERS-2 Ambiguous winds-10m GOME/SBUV Ozone retrievals GPS-RO/ COSMIC Bending angle SSMIS TMI AMSR-E ASCAT IASI In preparation: ADM-Aeolus, GPS ground-based
17 Data currently used CPTEC/INPE Conventional SYNOP/SHIP/METAR Ps AIREP Wind, Temp AMVs (GEO) Wind DRIBU Ps, Wind-10m TEMP Wind, Temp, Spec Humidity PILOT Wind profiles Satellite ATOVS and AIRS HIRS, AIRS and AMSU retrievlas QuikSCAT and ERS-2 Ambiguous winds-10m SSMIS TMI AMSR-E In preparation: GPS ground-based Meteosat/MSG/GOES - Water Vapour IR-channel GPS-RO/ COSMIC Bending angle ASCAT IASI retrievals AIRS radiances ( LETKF)
18 Satellite data sources in 2006 Number of satellite sources used at ECMWF No. of sources JASON GOES rad METEOSAT rad GMS winds GOES winds METEOSAT winds AQUA TERRA QSCAT ENVISAT ERS DMSP (A)TOVS Year Peter Bauer - ECMWF
19 Satellite data sources in Number of satellite sources used at ECMWF No. of sources AEOLUS SMOS TRMM CHAMP/GRACE COSMIC METOP MTSAT rad MTSAT winds JASON GOES rad METEOSAT rad GMS winds GOES winds METEOSAT winds AQUA TERRA QSCAT ENVISAT ERS DMSP NOAA Year Peter Bauer - ECMWF
20 Satellite data volume in 2006 quantity of satellite data used per day at ECMWF num ber of data used per day (m illions) CONV+SAT WINDS TOTAL Year Peter Bauer ECMWF
21 Satellite data volume in quantity of satellite data used per day at ECMWF num ber of data used per day (m illions) CONV+SAT WINDS TOTAL Year Peter Bauer - ECMWF
22 Large increase in number of data assimilated A scientific and technical challenge
23 Observing System Experiments CPTEC Model (CPTEC - Andreolli et al. 2007)
24 Observing System Experiments ECMWF Model 500 hpa, S.Hem, 89 cases 500 hpa, N.Hem, 89 cases NoSAT= no satellite radiances or winds Control= like operations NoUpper=no radiosondes, no pilot winds, no wind profilers (ECMWF - G. Kelly et al.)
25 What happens if we lose all satellites? ~3 days at day 5 S.H. ~2/3 to 3/4 of a day at day 5 N.H. Peter Bauer - ECMWF
26 Increasing Skill of Numerical Weather Forecasts ECMWF Anomaly Correlation of 500mb Height Forecasts ECMWF implements 4DVAR Forecast skill has improved steadily due to increased computing, better models and assimilation increased satellite data usage!
27 Forecast performance, ERA-40, ERA-Interim and Operations
28 Observation data count for one 12h 4D-Var cycle UTC 3 March ECMWF Screened Assimilated Synop: 450, % Aircraft: 434, % Dribu: 24, % Temp: 153, % Pilot: 86, % AMV s: 2,535, % Radiance data: 150,663, % Scat: 835, % GPS radio occult. 271, % TOTAL: 155,448, % Synop: 64, % Aircraft: 215, % Dribu: 7, % Temp: 76, % Pilot: 39, % AMV s: 125, % Radiance data: 8,207, % Scat: 149, % GPS radio occult. 137, % TOTAL: 9,018, % 99% of screened data is from satellites 96% of assimilated data is from satellites
29 Surface data 1958 March 1998 March
30 Assimilação de Dados no CPTEC/INPE Case Studies Data Assimilation: Observing System
31 Geopotential from AIRS - AQUA Data Assimilation: Observing System [email protected]
32 CPTEC/INPE AIRS Testing weather forecast impact - more than 12 h Data Assimilation: Observing System Andreoli et al [email protected]
33 Impact of AIRS retrievals over the 48 h forecast Andreoli et al [email protected]
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35 QuickScat Data Assimilation: Observing System
36 Analysis from ECMWF, NCEP and GMAO Data Assimilation: Observing System
37 CPTEC/INPE Analysis No QuikScat including QuikScat Data Assimilation: Observing System
38 Including SALLJEX data CPTEC/INPE SALLJEX ( ) Data Assimilation: Observing System
39 Including SALLJEX data - NCEP Data Assimilation: Observing System SALLJEX ( ) [email protected]
40 Data Assimilation at CPTEC/INPE RPSAS 40 km L38 conventional dataset, AMV, ATOVS, QuikScat and AIRS RPSAS 20 km L38 (Eta WS) pre-operational GPSAS T213 L42 Conventional dataset, CTW, ATOVS, QuikScat, TPW e AIRS BRAMS/PSAS same as RPSAS LETKF Global Model, Regional Model and Oceanic Model Data Assimilation: Observing System [email protected]
41 Data Assimilation Team Dr. Dirceu L. Herdies Dr. José A. Aravéquia Dr. Julio Pablo R. Fernandes Dr. Luciano P. Pezzi Dr. Luiz F. Sapucci Dra. Solange Souza M.Sc. Joao Gerd de Mattos (PhD student) M.Sc. Sergio H. Ferreira (PhD Student) Data Assimilation System: PSAS (oper.) e LETKF (res.) Model: Global, Regional Eta, BRAMS and MOM-4 Data Assimilation: Observing System [email protected]
42 Collaborators Dr. Arlindo da Silva and Dr. Ricardo Todling GMAO/NASA PSAS, QC Dr. Luís Gustavo G. de Gonçalves ESSIC and NASA SALDAS Dra. Eugenia Kalnay University of Maryland LETKF Dr. Steve English UKMet new dataset (IASI, GPS-RO, radar) Data Assimilation: Observing System
43 Data Assimilation Cycle o ω Observations a ω PSAS CPTEC GLOBAL MODEL Conventional Observations Non-Conventional Observations O-F Analysis Forecasts 06 to 72 hrs (6x6) 84, 96 hrs 120, 144, 168 hrs f ω Increm. FIRST GUESS 6 hrs. FG from CPTEC Data Assimilation: Observing System [email protected]
44 South America Regional Reanalysis SARR High Lights Eta Model - 40 km resolution over South America - 38 levels - NCEP horizontal boundary conditions Data Assimilation - RPSAS Regional Physical-space Statistical Analysis System - Four daily analyses from jan2000-dec2004 1,2 TB of the disk space 6 months to run using a NEC/SX6 computer SOUTH AMERICA REGIONAL REANALYSIS CPTEC/INPE, SARR CPTEC/INPE, SARR Data Assimilation: Observing System [email protected]
45 South American Regional Reanalysis SARR Modelo Regional ETA; RPSAS (Regional Physical-space Statistical Analysis System); Janeiro 2000 Dezembro 2004; Resolução horizontal de 40 km com 38 níveis na vertical; Dados utilizados: convencionais e de satélite; Análises (6 h) e Precip, Tmed, Tmax e Tmin - (24 h); LBA- DIS (ftp://lba.cptec.inpe.br/lba_archives/pc/ Surface Data PC-404/regional _reanalysis/)
46 Validation over SALLJEX area SARR NCEP/NCAR NCEP/NCAR Rawinsonde. SARR Rawinsonde Data Assimilation: Observing System
47 South American Land Data Assimilation SALDAS Evaluating SALDAS forcing derived from SARR and observation based d precipitation and radiation over the LBA region Surface Pressure (hpa) Surface Temperature (K) Day of the month Day of the month Bias reduced surface Pressure (left panel) and surface temperature are compared against NCDC surface stations over the LBA region. The average observed values are shown in light green while the region that comprises the observations standard deviation is shown in dark green. Model (SARR) derived values are shown in blue Data Assimilation: Observing System [email protected]
48 500hPa analysis RMS error (Global average) PSAS RMS=2. PSAS LETKF 0 LETKF PSAS PSAS LETKF LETKF RMS error (m/s) RMS=1. 0 RMS error (K) RMS=0. 8 RMS=0. 5 Time Time Zonal Wind Temperature Hong Li et al [email protected]
49 Feb. average analysis RMS error at different levels (Global average) PSAS LETKF PSAS LETKF RMS error Zonal Wind RMS error Temperature Hong Li et al. 2006
50 Observations Impact for Two Resolutions of Full DAS
51 ECMWF 2003
52 CENTRO DE PREVISÃO DO TEMPO E ESTUDOS CLIMÁTICOS CENTER FOR WEATHER FORECAST AND CLIMATE STUDIES CPTEC INPE THANK YOU! OBRIGADO INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS NATIONAL INSTITUTE FOR SPACE RESEARCH
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55 Data Assimilation Context Over 1.43B observations received per day (most satellite data global system does not include radar radial winds). Over 7M observations per day used. Data selection and quality control eliminate many observations Data selection applied because of: redundancy in data reduction in computational cost eliminate non-useful observations
56 Most applied research in atmospheric data assimilation done at operational centers Much of expertise and knowledge is undocumented or minimally documented papers are not the priority at operational centers Many opportunities to use new observations and to improve forward models for DA. Data assimilation is where everything comes together To use new observations properly requires one to become an expert in that particular instrument One must be knowledgeable on forecast model dynamics and physics to understand background errors Computational techniques are necessary to improve efficiency
57 Data Assimilation: Observing System
58 Projetos do Grupo de Assimilação de Dados Assimilação de dados de IWV, AIRS e AMSU AQUA ok Assimilação de Radiâncias AIRS AQUA /2008 Desenvolvimento do novo sistema LEKF /2008 Desenvolvimento do Sistema PSAS/BRAMS /2008 Assimilação de dados de superfície /2008 Inclusão de dados sintéticos - a ser implementado Assimilação de dados oceânicos - em andamento Assimilação de dados de radar proj. de doutorado Assimilação de dados de precipitação a ser iniciado Assimilação de Aerossóis a ser iniciado Assimilação de Dados no CPTEC/INPE [email protected]
Impact of ATOVS geopotential heights retrievals on analyses generated by RPSAS
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