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ECMWF – ESA Liaison Meeting 3 March 2011 ESA-funded projects

ECMWF – ESA Liaison Meeting 3 March 2011 ESA-funded projects . ESA Support. Support of ERS/ Envisat /Cryosat-2: Data m onitoring and assimilation Wind speed, wave height Temperature, trace gases Support of EarthCARE : Potential of radar/ lidar forN WP Clouds, precipitation, aerosols

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ECMWF – ESA Liaison Meeting 3 March 2011 ESA-funded projects

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  1. ECMWF – ESA Liaison Meeting3 March 2011ESA-funded projects

  2. ESA Support • Support of ERS/Envisat/Cryosat-2: Data monitoring and assimilation • Wind speed, wave height • Temperature, trace gases • Support of EarthCARE: Potential of radar/lidarforNWP • Clouds, precipitation, aerosols • Support of SMOS: Data monitoring/assimilation • Soil moisture • Support of ADM: Level-2 processing/assimilation • Wind • Support of Galileo: Optimal radio-occultation constellation • Temperature, moisture, pressure

  3. ESA Support • Support of ERS/Envisat/Cryosat-2: Data monitoring and assimilation • Wind speed, wave height • Temperature, trace gases • Support of EarthCARE: Potential of radar/lidarforNWP • Clouds, precipitation, aerosols • Support of SMOS: Data monitoring/assimilation • Soil moisture • Support of ADM: Level-2 processing/assimilation • Wind • Support of Galileo: Optimal radio-occultation constellation • Temperature, moisture, pressure

  4. 6-hour time window Envisat - Altimeter Operational altimeter data reception at ECMWF ~ 81% ~ 68% ED DC

  5. 8 6 4 2 0 0 2 4 6 8 10 12 14 WAM Wave Heights (m) Envisat - Altimeter Global comparison between altimeter and ECMWF wave model (WAM) first-guess SWH values (From 02 February 2010 to 01 February 2011) 14 100000 10000 12 5000 Envisat Jason-2 Jason-1 1000 500 10 100 STATISTICS 50 10 ENTRIES 1 1 2 5 9 0 8 5 MEAN WAM 2 . 6 0 1 4 ENVISAT Wave Heights (m) 1 MEAN ENVISAT 2 . 5 8 5 1 BIAS (ENVISAT - WAM) - 0 . 0 1 6 3 STANDARD DEVIATION 0 . 2 7 3 3 SCATTER INDEX 0 . 1 0 5 1 CORRELATION 0 . 9 7 8 6 SYMMETRIC SLOPE 1 . 0 0 2 6 REGR. COEFFICIENT 1 . 0 1 6 3 REGR. CONSTANT - 0 . 0 5 8 7

  6. Triple Collocation (1 August 2009 – 31 July 2010) 3 data sets: Model, Envisat, Buoys Model, Jason-2, Buoys Model, Jason-1, Buoys Model: For SWH: Wave model hindcast(i.e. a stand-alone wave model run without data assimilation, forced by consistent wind fields). For wind speed: NWP model day-1 forecast. Errors are assumed to be linear and independent from each other. Note: Jason-1 suffered from several periods of instability during 2009 especially between March and May 2009. Envisat - Altimeter

  7. Envisat - Altimeter Monthly Significant Wave Height (SWH) Errors(3-month running averages)

  8. Envisat – GOMOS/SCIAMACHY Operability of Envisatproducts to ECMWF in 2010 GOMOS SCIAMACHY Percentage of data received on time to be included in DCDA

  9. Envisat – SCIAMACHY SCIAMACHY – OMI SCIAMACHY – GOME-2

  10. GOMOS data was available in 2010, except in November after an orbit change. The quality was stable, in particular: Temperature: Good agreement in the Stratosphere (+/- 1%), but larger biases found in the Mesosphere where the T profiles are relaxed to a climatology. Ozone: first-guess and analysis departures are within -5 and +20% in the Stratosphere, but larger in the Mesosphere (>50% in places). The standard deviations of the departures are >15% at all levels. Water vapour:still show a poor level of agreement with the ECMWF analyses, one to four orders of magnitude difference between obs and fg/an. Impact of the ENVISAT orbit change: negligible on average, but the O3 data seem to have a larger noise in December 2010 than they had until October 2010  to be confirmed during 2011. Envisat – GOMOS Jun 2010 Dec 2010

  11. Envisat – MIPAS L1 MIPAS L1(5.02) radiances: O3(MLS(v2.2))-O3(An) Exp-CTRL Exp-MIPAS Residuals Std dev

  12. CryoSat-2 mission will be generating an ocean product called “Fast Delivery Ocean Level 2 (FDM)” while operating in Low Resolution Mode (LRM) on a “best-effort” basis ⇒ FDM not released yet. CNES is developing a prototype processor (for Sentinel-3) which they used to generate an independent experimental ocean product. CNES product was verified: very high quality! FDM as a fully supported operational product would be highly appreciated! Cryosat-2 Cryosatvs WAM Cryosatvs Buoys

  13. Cryosat-2 Global comparison between altimeter and ECMWF wave model (WAM) first-guess SWH values

  14. ESA Support • Support of ERS/Envisat/Cryosat-2: Data monitoring and assimilation • Wind speed, wave height • Temperature, trace gases • Support of EarthCARE: Potential of radar/lidarforNWP • Clouds, precipitation, aerosols • Support of SMOS: Data monitoring/assimilation • Soil moisture • Support of ADM: Level-2 processing/assimilation • Wind • Support of Galileo: Optimal radio-occultation constellation • Temperature, moisture, pressure

  15. EarthCARE Project: Quantitative Assessment of the Operational Value of Space-Borne Radar and Lidar Measurements of Cloud and Aerosol Profiles (QuARL) 1st Project (concluded) 2nd Project (under review) Future General Studies Support To Science Element EarthCARE? Model validation - clouds - aerosols Radar :Radar+Lidar : Radar+Lidar Simulator Simulator Monitoring demonstrator Experimental monitoring Operational monitoring Experimental assimilation Experimental assimilation Operational assimilation Assimilation strategies based on Cloudsat/Calipso data based on Cloudsat/Calipso data based on EarthCARE data • Main conclusions: • Model validation with profiling cloud/aerosol lidar/radar is fundamental for future parameterization developments with significant impact on cloud-aerosol-radiation interaction • Unique verification source of already assimilated data affected by clouds/aerosols • Assimilation experiments show beneficial impact – but localized

  16. -24 – -21 -21 – -18 -18 – -16 -16 – -12 -12 – -9 -9 – -6 -6 – -3 -3 – 0 0 – 3 3 – 6 6 – 9 9 – 12 12 – 15 15 – 18 EarthCARE 1D-Var Assimilation of Cloudsat Radar Reflectivities (dBZ) Model First-Guess Observation Analysis

  17. EarthCARE Model First-Guess 1D-Var Assimilation of Calipsolidar Backscatter Coefficients (km-1 sr-1) Observation Analysis

  18. EarthCARE 1D+4D-Var Assimilation of Cloudsat Radar Reflectivities (dBZ) GOES-12 EXP-CTRL IR-Imagery GOES-12 IR-Imagery NEXRAD EXP-CTRL Radar rainfall Radar rainfall

  19. ESA Support • Support of ERS/Envisat/Cryosat-2: Data monitoring and assimilation • Wind speed, wave height • Temperature, trace gases • Support of EarthCARE: Potential of radar/lidarforNWP • Clouds, precipitation, aerosols • Support of SMOS: Data monitoring/assimilation • Soil moisture • Support of ADM: Level-2 processing/assimilation • Wind • Support of Galileo: Optimal radio-occultation constellation • Temperature, moisture, pressure

  20. SMOS - Monitoring Collocation, screening, forward modelling, first-guess departures, etc. ESAC Computations in model space (gp_model) L1C-NRT BUFR product • Get SMOS data in grid point • call smos_process Convert to L1C-NRT ECMWF BUFR product • Forward model (CMEM) • physics interface routines • call callpar passive monitoring of NRT TB over land & sea Store in ECMWF archives Tatm Mapping and load data to ODB tables • call smos_screen • CMEM interface MARS ECFS • call mwave_screen • RTTOV interface ε • Pre-process data: • Consistency checks • Parallel data thinning • per angular bins Distribution per processor and grid point • Back to observation space • call smos_update BUFR files ODB data Acquisition, quality control, thinning, etc.

  21. SMOS - Monitoring Std of obs 1-7 Oct 2010 Standard deviation of Observations 1-7 October 2010 • TBxx:

  22. SMOS - Monitoring 3 months of data (28 Nov 2010-28 Feb 2011) Radiances at 10˚ incidence angle Thermal instability  science data is degraded 12 January  Hardware problem is solved (thermal control is restored, BUT, calibration of algorithm parameters need re-calibration. 18 February  NRT processor working again normal. Stability test (calibration event)  6 days

  23. SMOS - Next Data assimilation Data processing Thinning Noise filtering Bias correction Assimilation expt T2m, RH2m SMOS data T2m, RH2m, SMOS Maintenance, testing, reports, etc.

  24. ESA Support • Support of ERS/Envisat/Cryosat-2: Data monitoring and assimilation • Wind speed, wave height • Temperature, trace gases • Support of EarthCARE: Potential of radar/lidarforNWP • Clouds, precipitation, aerosols • Support of SMOS: Data monitoring/assimilation • Soil moisture • Support of ADM: Level-2 processing/assimilation • Wind • Support of Galileo: Optimal radio-occultation constellation • Temperature, moisture, pressure

  25. ADM-AeolusThe L2B implementations HLOS wind retrieval for use in NWP Core PDS Stand-alone version for late- and re-processing (ESRIN) NWP Centres Subroutine version for integration in data-assimilation systems Portable L2B software and documentation to ESA and NWP-SAF standards Two releases planned for 2011 “Final burst mode” + “First continuous mode” ECMWF/IFS For operational generation of ESA’s L2B and L2C products To complete/refine 2011/12

  26. ADM-Aeolus: ECMWF implementationOperational acquisition of inputs from ESA ECMWF polls the ESA ftp site every 10 minutes Additional transfers prepared for GSOV Part 2 2012

  27. ADM-Aeolus: ECMWF implementationProcessing from Level-1B to Level-2B/2C L2B/L2C/AuxMet data generated & stored in ECMWF’s Observation DataBase (ODB) Monitoring statistics on data quality are compiled/plotted routinely Data from ODB written in ESA’s Earth Explorer format for dissemination to PDS Already passed GSOV Part 1 2009, internal enhancements ongoing

  28. ADM-Aeolus: Example • The measuring capability of the Aeolus lidar instrument has been simulated • Real scattering measurements obtained from the LITE instrument • ESA’s software (E2S) is used to simulate what ADM would ‘see’ • The L1B software retrieves scattering ratio on the ADM measurement resolution

  29. ADM Aeolus: Ongoing activities • Adapting to continuous mode laser • Preparing for GSOV Part 2, 2012 • Participating in cal/val expert panel • Preparing for commissioning phase • Scientific validation of the processing chain (especially upstream calibration) and assimilation impact of real DWL data (airborne demonstrator flown by DLR) • Finalize the download website and license agreement for other users of the software • Supporting other users for integration/implementation within their own NWP systems • On standby in case EUMETSAT seeks a QRT L2B service for other users (cf KNMI option) • Exploring greater use of Aeolus aerosol products

  30. ESA Support • Support of ERS/Envisat/Cryosat-2: Data monitoring and assimilation • Wind speed, wave height • Temperature, trace gases • Support of EarthCARE: Potential of radar/lidarforNWP • Clouds, precipitation, aerosols • Support of SMOS: Data monitoring/assimilation • Soil moisture • Support of ADM: Level-2 processing/assimilation • Wind • Support of Galileo: Optimal radio-occultation constellation • Temperature, moisture, pressure

  31. Galileo Support of Galileo GNSS: Observing System Simulation Experiments (OSSE) to estimate the optimal radio occultation constellation number for NWP and climate research (submitted) • Background: • GNSS radio occultation observations provide fundamental (complementary) contribution to radiance data: NWP and Climate • Currently experienced decline in GNSS radio occultation observation numbers hopefully compensated by COSMIC-2 (2014) and, multiple transmitter capability (GPS, Galileo) in the future. • WMO Vision for GOS requires estimation of optimal constellation configuration • ECMWF proposal employs ensemble-based Observing System Simulation Experiments (OSSE) for this task ensuring consistency with recently performed Observing System Experiments (OSE) using real data.

  32. Galileo OSE with all (!) available GPSRO data to test saturation limit for NWP impact Future constellation impact will be evaluated with Ensemble-OSSEs (proposed to ESA under Galileo science programme)

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