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ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) The Global Observing System Overview of data sources Data coverage Data.

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Presentation on theme: "ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) The Global Observing System Overview of data sources Data coverage Data."— Presentation transcript:

1 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) The Global Observing System Overview of data sources Data coverage Data used Data monitoring Use for model verification François Lalaurette and Jean-Noël Thépaut

2 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Overview of data sources SYNOP / SHIP/ METAR Meteorological/ Aeronautical weather stations (2m, except wind: 10m) Ships (variable height, default=25m) Some moored buoys (5m) BUOYS Moored (TAO, PIRATA) Drifters used parameters: wind, pressure, temperature

3 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

4 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Moored TAO buoy

5 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

6 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Overview of data sources (contd) TEMPSHIP / DROPSONDES ASAPs (commercial lines) in replacement of weather ships (stationary) Dropsondes from scientific aircrafts (NOAA, UKMO, DLR); used for FASTEX, NORPEX (winter adaptative observing network experiments), NA-TREC and Tropical Cyclones; parameters: Temperature, Wind, Pressure, Humidity PROFILERS UHF/VHF Doppler "clear air" radars (US and Europe); parameter: wind

7 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

8 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data reception (radiosondes)

9 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Profilers Profiler site near Haskell, OK (http://www-dd.fsl.noaa.gov/)

10 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data Coverage

11 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Overview of data sources (contd) Aircraft AIREPS ("manual" reports from pilots) AMDARs, ACARs, ASDARs: automated (high quality) parameters: wind, pressure, temperature (NO humidity)

12 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

13 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Overview of data sources: Satellite data Two different types of space agencies Research Agencies Operational Agencies Two ways of looking at the earth/atmosphere GEO (GEOstationary satellites) LEO (Low Earth Observing satellites)

14 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) GEOSTATIONARY OBSERVING SYSTEMS (36 000 km from the earth) Advantages: Wide space coverage (whole disk) Very high temporal coverage ( a few minutes) Particularly suitable for short-range NWP and Now-casting applications Suitable also for meteorological feature tracking –( Atmospheric Motion winds ) Suitable for applications in which the diurnal cycle representation is crucial Drawbacks: Spatial coverage limited to the disk (need for constellation) Unsuitable to observe the polar regions

15 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Low Earth Orbiting OBSERVING SYSTEMS (400 to 800 km from the Earth) Advantages: Cover the whole earth after several cycles (polar orbiting satellites) More suitable to sound the atmosphere in the microwave spectrum. Drawbacks: Moderate temporal sampling (several hours to go back to the same point) Requires constellation to ensure a reasonable temporal sampling

16 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) NOAA-15 NOAA-16 NOAA-17 Goes-W Goes-EMet-7 Met-5 GMS(Goes-9)

17 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Current Space based Observing System

18 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Overview of data sources (contd) AMV - Atmospheric Motion Vectors (formerly SATOB) geostationary satellites (GOES 9/10/12; METEOSAT 5/7) Polar orbiting (MODIS on Terra) Availability on a very rapid increase (higher space and time resolution, new platforms, quality indexes) Unknown parameter: height! Raw radiances HIRS, AMSU (NOAA 15/16/17/Aqua), AIRS (Aqua), METEOSAT/GOES

19 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

20 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Another type of inversion: Polar WV winds from MODIS Source: P. Menzel, 2003

21 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

22 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

23 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

24 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Overview of data sources (contd) Scatterometer (Microwave, active) 2 platforms (ERS2 and Quickscat) parameter: sea wind (+wave heights from ERS altimeter) DMSP/SSMI (Microwave, passive) 3 platforms (DMSP F-13/F-14/F-15); parameters: raw radiances (total column vapour water+sea wind) Ozone Envisat/NOAA/ERS

25 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

26 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

27 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data coverage

28 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) RESEARCH AGENCIES NASA: National Aeronautics and Space Administration JAXA: Japanese Aerospace eXploration Agency ESA: European Space Agency …(several other national agencies) Research Agencies promote demonstration missions, with innovative technologies Research instruments can provide independent information for model and/or other observations validation Near Real Time delivery of data is not necessarily a priority Research satellites pioneer future operational missions In principle, the life time of research missions is short (<10 years)

29 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) OPERATIONAL AGENCIES EUMETSAT: EUropes METeorological SATellite organisation NOAA: National Oceanic and Atmospheric Administration NOAA-NESDIS-DMSP JMA: Japan Meteorological Agency Russia, China,… Operational Systems inherit from Research demonstration missions Operational Satellites are committed to Real Time delivery to end-users Operational missions ensure a stabilised long-life mission technology (HIRS instrument onboard NOAA satellites has lasted for ~30 years)

30 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Operational versus Research Agencies Thanks to a WMO initiative, R&D satellites are now fully considered as part of the Global Observing System Should ease the transition from research to operations Has implications on NRT delivery requirements Operational centres use pragmatically R&D instruments: for model validation (POLDER, CERES,…) for data assimilation (ERS, QUIKSCAT, AIRS,…) Drawback of using research satellites: Lack of visibility on the modifications of instrument calibration/configuration Sometimes Take it or leave it approach…

31 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) ESA ENVISAT Heritage of ERS-2 –Multi-instrument platform –Ozone monitoring: »GOMOS,SCIAMACHY, MIPAS –Sea Ocean State monitoring »ASAR, MERIS, AATSR ADM-AEOLUS –Doppler wind lidar to provide 3D-wind coverage SMOS, EARTHCARE, WALES,…

32 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) EUMETSAT Geostationary program METEOSAT (currently 5 7) –Infrared window and water vapour –Visible »(Atmospheric Motion Winds) METEOSAT 2 nd GENERATION (8) SEVIRI ( Spinning Enhanced Visible and Infrared Imager ) –12 channels (T,q,O3,..) GERB ( Geostationary Earth Radiation Budget ) Preparation of METEOSAT 3 rd GENERATION (MTG)

33 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) EUMETSAT Polar program EPS: European Polar System Part of the Initial Joint Polar System –will include a NOAA satellite from the USA and a METeorological Operational (METOP) satellite from Europe Variety of instruments –IASI (high resolution interferometer) –ASCAT (wide swath scatterometer) –GOME (ozone measurement instrument) –NOAA package (HIRS/AMSU/AVHRR) –GRAS (GPS receiver)

34 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) EUMETSAT Satellite Application Facility for Numerical Weather Prediction Science Plan for NWP SAF deliverables User requirements ATOVS IASI MVIRI/SEVIRI (and other geostationary imagery) Scatterometers SSM/I and SSMIS Ozone monitoring Instruments Radiative Transfer Modelling Preprocessing packages

35 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data sources for the ECMWF Meteorological Operational System (EMOS). The numbers refer to all data items received over a 24 hour period in May 2003.

36 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data selection Used data only

37 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data monitoring Methodology statistics obs-model guess over large samples exchange of informations among NWP centres Results Blacklists Bias corrections Feedback to data providers (ECMWF WMO lead centre for radiosondes and pilot data monitoring)

38 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data monitoring: the AMV case Comparison against model guess, aircraft and radiosondes All sources point at an underestimation of the winds by the satellite tracking technique

39 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) AMV error correlations ( Bormann et al., MWR 2003) observation errors keep non-zero correlation over distances much larger than their nominal resolution

40 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Data monitoring: Bias correction Bias computed as a function of pressure level and solar elevation for two different sonde types To be corrected Not to be corrected

41 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Profilers

42 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) ECMWF Web service (http://www.ecmwf.int) Monitoring information available now: Data coverage maps (last 24h) Time series (last 30 days) Radiances monitoring Monthly Monitoring report GUAN

43 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) http://www.ecmwf.int/products/forecasts/d/charts/monitoring/covera ge/dcover

44 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Use for forecast verification

45 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Recent progress: Rainfall events distribution 4% Of SYNOP reports exceed 10mm/day

46 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Use for Forecast verification 1999-2000: Too many light rain… … too few heavy rain events

47 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Recent progress: Rainfall events distribution 2002-2003: less light rain… … and more heavy rain events

48 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Recent progress: Rainfall events distribution 1999-2000: T3192002-03: T511 + changes to convection

49 ECMWF MetTraining Course- Data Assimilation and use of satellite data (3 May 2005) Summary The range of observations that are nowadays available is quite large Data however are very inhomogeneous in quality, space and time resolution,etc… Quality control (and bias correction) is crucial Tools to help interpret the impact of observations on the model still in their infancy

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