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EUMETNET, the Network of European Meteorological Services, PowerPoint Presentation
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EUMETNET, the Network of European Meteorological Services,

EUMETNET, the Network of European Meteorological Services,

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EUMETNET, the Network of European Meteorological Services,

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  1. E-GVAPThe EUMETNET GPS Water Vapour ProgrammePresentor: Henrik Vedel Danish Meteorological InstituteCoordinator of E-GVAPemail: egvap@dmi.dk. E-GVAP Space Weather Week, Brussels 2006-11-15

  2. EUMETNET, the Network of European Meteorological Services, is an association of European national met. offices Memberships: E-GVAP / EUMETNET / none (by Jan 2006) (Meteo-France will become a member of E-GVAP by April 2007) E-GVAP Space Weather Week, Brussels 2006-11-15

  3. Why are GPS delay data interesting to meteorology? The zenith total delay, ZTD, which can be estimated processing data from ground based GPS receivers, is sensitive to properties of the atmosphere of importance to meteorology. E-GVAP Space Weather Week, Brussels 2006-11-15

  4. ZTD is the delay one would observe was there a GPS (GNSS) satellite at zenith. ZTD is estimated by fitting models including mapping functions and the ZTD to observations of the visible GPS satellites (We consider all ionospheric influence removed, by means of L1 L2 information) E-GVAP Space Weather Week, Brussels 2006-11-15

  5. From geodetic side • ZTD = ZHD + ZWD (zenith hydrostatic and zenith wet delay) • TD = f_H(θ) ZHD + f_W(θ) ZWD (f_ = ’known’ mapping functions θ = zenith angle) • ZHD = f(position) p_c (f ’known’, p_c pressure from climatology) • ZWD is estimated by fitting a model including ZWD to observations toward individual GPS satellites obtained from a ground based GPS receiver. Corrections due to various time changing effects, such as Earth tide displacement, ocean and atmospheric loading, and post glacial rebound are applied. • Depending on the processing method a simultaneous fitting is done for various other effect, such as clock and orbit errors. Some of those may be provided as input (estimated apriori). • Different processing software packages and strategeies have been found (e.g. in the TOUGH project, http://tough.dmi.dk) to produce ZTD results of comparable quality when the necessary input data are available. And that may vary, according to method and strategy. E-GVAP Space Weather Week, Brussels 2006-11-15

  6. GPS Processing Strategy rnx1 rnx1 rnx2 rnxi rnxn rnx2 Data reduction Orbits+clk ERP Site Coord. rnxn Orbits ERP Site Coord. Data reduction Data reduction Data reduction ZTD ZTD VAR/COV Matrix ZTD ZTD Network Approach simultaneous analysis of all the data Precise Point Positioning each station is analyzed independently • Computing time increases more than proportionally with the number of stations • Computing time increases linearly with the number of stations • Network has to be split in sub network • Parallel processing • VAR/COV Matrix • No correlation between sites IMPORTANT: orbits and site coordinates must be in the same RF (This slide courtesy Olivia Lesne, ACRI-ST) E-GVAP Space Weather Week, Brussels 2006-11-15

  7. From meteorological side • ZTD = ZHD + ZWD • ZHD = f(position) p_a (f ’known’ p_a = pressure at GPS antenna) • ZWD = f(T) IWV (IWV = integrated water vapour, • f known, weakly depending on temperature profile) • Properties like pressure and IWV are important to numerical weather prediction (NWP) models and to now-casting. • Currently water vapour measurements for meteorology are scarce. • However, to be of value to meteorology, the ZTD data must be: • Available at the right time (1h 40min for NWP, faster for now-casting) • Stable, both short and long term. • Of consistent quality. • From relevant region. E-GVAP Space Weather Week, Brussels 2006-11-15

  8. Purpose of E-GVAP • The main purpose of EGVAP is to provide quality checked ground based GPS delay and integrated water vapour data (ZTDs and IWVs) in near real time (NRT) for use in operational numerical weather prediction (NWP) models and in now-casting to the participating EUMETNET members. • To improve on the data quality and enlarge data coverage • To assist in utilising the data for weather forecasting. E-GVAP Space Weather Week, Brussels 2006-11-15

  9. Build on results and collaboration established in COST716, TOUGH, and other fora. • Arrangements on the European level between EUREF and E-GVAP • Arrangements on national level between national met office and national GPS site/data owners (to avoid problems related to transfer of national data over borders). • For same reason no single central processing centre. Processing to take place where it is most practical in a given situation. • Build mainly on exchange of data (GPS data versus meteorological data) and on sharing of resources (e.g. GPS stations being placed on meteorological sites. • Funding for services are arranged on national level. Mainly for extra expenses in setting up data transfer, for transfer of processing expertise, in some cases for processed data. Approach E-GVAP Space Weather Week, Brussels 2006-11-15

  10. NRT GPS Processed Data Flow(As of Dec 2005. Since KNMI has started processing data, NKG and NKGS have been replace be SMHI, and ACRI and ROB has stopped processing. Likely data from Belgium will soon be processed again, at the met office) GFZ BKG ASI ACRI GOPE IEEC LPT(R) THORN FTP server METO BUFR BUFR Linux Workstation MetDB BUFR NKG BUFR NKGS BUFR NRT Users & mirror sites GTS Users ROB SGN (Figure by Dave Offiler, UK Met Office) E-GVAP Space Weather Week, Brussels 2006-11-15

  11. DATA COVERAGE Status map from 20061113 from the E-GVAP validation site. (See egvap.dmi.dk under validation for current situation). Data available at ftp-server at MetO: thorn.meto.gov.uk E-GVAP Space Weather Week, Brussels 2006-11-15

  12. Timeliness monitoring E-GVAP Space Weather Week, Brussels 2006-11-15

  13. Arrival time monitoring. E-GVAP Space Weather Week, Brussels 2006-11-15

  14. Quality monitoring • Continuous quality monitoring is performed and shown at the validation site. Monitoring is against NWP HIRLAM data and against radiosonde data. • Will be updated with automatic flagging of deviating data. • Will be updated with automatic feedback to processing centres/site owners in case of detected problems. • Statistics is compiled for the NWP-GPS offsets and presented at the KNMI validation site. • Periodic reports on performance of all stations/centres against NWP and other data will be made. These can be used to access the quality of various processing methods. E-GVAP Space Weather Week, Brussels 2006-11-15

  15. Quality monitoring (2) • Besides processing local stations all processing centres are to process ZTDs for a common set of about 10 super sites • Comparisons of NRT results between centres and to post processed values will be made to access quality of processing methods. • Many super sites will include additional observing equipment, e.g. radiosondes and/or water vapour radiometers. E-GVAP Space Weather Week, Brussels 2006-11-15

  16. E-GVAP Space Weather Week, Brussels 2006-11-15

  17. E-GVAP Space Weather Week, Brussels 2006-11-15

  18. Expert team on data processingExpert team on data utilisationData liaison group. E-GVAP Space Weather Week, Brussels 2006-11-15

  19. Examples of use E-GVAP Space Weather Week, Brussels 2006-11-15

  20. IWV map/films for now-casting.’Weather’ is visible in high density regions, example of few station artefacts seen in some of the data sparse areas. E-GVAP Space Weather Week, Brussels 2006-11-15

  21. Impact in NWP models • The ZTD data are entered into the NWP models via data assimilation. Typically 3 or 4 dimensional variational data assimilation is used to assimilate ZTD. The GPS ZTD data correspond only to a minute fraction of all the observation assimilated. • In general an improvement of rain forecasts is found. At some centres only at mid and high precipitation levels. • Some other fields (e.g., humidity/geopotential height/2mT..) are improved, varying from met centre to met centre. • Importance of bias correction unclear – might be NWP model dependent. • Data quality is an issue. Sometimes a degradation of the forecasts is seen. Sometimes due to poor GPS ZTDs, also problems in the data assimilation and NWP systems play a role. E-GVAP Space Weather Week, Brussels 2006-11-15

  22. Impact in NWP models 12 hour precipitation. Observed (left) versus NWP without GPS (centre) and NWP with GPS (right). Sattler and Vedel, DMI. E-GVAP Space Weather Week, Brussels 2006-11-15

  23. Positive impact on precipitation from GPS data (and RH2m data). INM study for Spain, covering mid April to mid May 2004, by Jana Sanchez Arriola, Beatriz Navascues, and Jose Garcia-Moya. RE=no extra data RH=RH2m data GP=GPS data RG=GP+RH2m data Notice that combining the data (RG) is less good than just GPS, suggesting problems in the data assimila- tion software E-GVAP Space Weather Week, Brussels 2006-11-15

  24. Positive impact on precipitation distribution from additional GPS data in a data sparse region INM study for Spain, covering mid April to mid May 2004, by Jana Sanchez Arriola, Beatriz Navascues, and Jose Garcia-Moya. E-GVAP Space Weather Week, Brussels 2006-11-15

  25. Positive impact on relative humidity from additional GPS data in a data sparse region INM study for Spain, covering mid April to mid May 2004, by Jana Sanchez Arriola, Beatriz Navascues, and Jose Garcia-Moya. E-GVAP Space Weather Week, Brussels 2006-11-15

  26. Oct 2004: Height improved but temperature bias got worse. Sattler and Vedel, DMI E-GVAP Space Weather Week, Brussels 2006-11-15

  27. Example of problem to be solved, using GPS ZTDs in NWP The errors of the GPS ZTDs are correlated, on both small and large scales(Stoew, Johansson, and Elgered at Chalmers and Ridal at SMHI) Black: Error correlation of NWP HIRLAM Red: Error correlation of GPS ZTDs Blue: Total spatial correlation for GPS-NWP offsets. GPS ZTD errors correlated on both small and large scales! E-GVAP Space Weather Week, Brussels 2006-11-15

  28. Thank you for your attentionEnd / Questions Contact point: egvap@dmi.dk Further info: http://egvap.dmi.dk E-GVAP Space Weather Week, Brussels 2006-11-15