1 / 25

Scatterometers at KNMI; Towards Increased Resolution

Ad.Stoffelen@KNMI.nl Hans Bonekamp Marcos Portabella http://www.knmi.nl/scatterometer. Scatterometers at KNMI; Towards Increased Resolution. Isabel. Overview. Scatterometer winds contain mesoscale detail not captured by NWP fields, but also noise

ghazi
Download Presentation

Scatterometers at KNMI; Towards Increased Resolution

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Ad.Stoffelen@KNMI.nl Hans Bonekamp Marcos Portabella http://www.knmi.nl/scatterometer Scatterometers at KNMI;Towards Increased Resolution Isabel

  2. Overview • Scatterometer winds contain mesoscale detail not captured by NWP fields, but also noise • Mesoscale information is useful for nowcasting • MSS: an effective way of controling the noise • Spatial analysis in progress Miami Workshop 8-10 Feb ‘05

  3. Spectral tail • Spectral response is used in engineering for design of noise properties • Being used now to increase SeaWinds resolution at KNMI Energy density Noisy Ideal Truncated Wave number Miami Workshop 8-10 Feb ‘05

  4. Bad rainy case • Nadir noisy Miami Workshop 8-10 Feb ‘05

  5. Local minima Probability of f Wind direction (f) Broad Wind Direction Minima Solution bands • Local minima do not represent solution P Miami Workshop 8-10 Feb ‘05

  6. Broad Minima A wide range of probable solutions exists in nadir (of 144 solutions per WVC) Locally, 100-km product is pretty Unique (P threshold is 10-7) Miami Workshop 8-10 Feb ‘05

  7. Meteorological balance (2D-VAR) Spatial filter: • Mass conservation • Continuity equation  0U = 0 • Vertical motions < horizontal motion • Little divergence • Mostly rotation (extratropics) Miami Workshop 8-10 Feb ‘05

  8. 100 km • Multiple Solution • Scheme • Full use of solution probability info • Meteorological balance in Ambiguity Removal (2D-VAR) • (Portabella&Stoffelen, 2003) • Smooth solution exists @100 km Miami Workshop 8-10 Feb ‘05

  9. Standard scheme: < 4 solutions • Erratic at low wind speeds Miami Workshop 8-10 Feb ‘05

  10. Multiple Solution Scheme • Smooth representation • Mesoscale detail kept Miami Workshop 8-10 Feb ‘05

  11. ECMWF First Guess ECMWF First Guess • ECMWF Position error Miami Workshop 8-10 Feb ‘05

  12. General MSS performance @100 km Mean vector RMS difference with ECMWF FGAT (m/s) • MSS better than 4-solution standard, in particular at nadir • NCEP background for 2DVAR much worse Miami Workshop 8-10 Feb ‘05

  13. 50 km Plots ! NOAA MSS @ 25 km Improved coldfront Better Around rain Miami Workshop 8-10 Feb ‘05

  14. NOAA MSS @ 25 km Better Around rain Improved inflow Miami Workshop 8-10 Feb ‘05

  15. MSS @ 25 kmNOAA NCEP Improved inflow Miami Workshop 8-10 Feb ‘05

  16. Summary • The use of more wind retrieval information in MSS allows consistent mesoscale features in the 25-km product • A balanced spatial filter such as 2D-VAR is effective in removing noise and keeping meteorology, direction or vector uniformity constraints are less effective • At 100-km the background wind used for ambiguity removal appears irrelevant, but this needs checking at 25 km • The spectral behaviour of 2D-Var at 25-km needs to be evaluated • Verification against buoys is underway Miami Workshop 8-10 Feb ‘05

  17. Further References For scatterometer-related papers, documentation, and wind products of the SAFs please refer to http://www.knmi.nl/scatterometer We look forward to sharing • Our scatterometer processing software • Our ERS and QuikScat products • Our new wind stress products • Our experience We fund visiting scientists E-mail:scat@KNMI.nl Thank you! Miami Workshop 8-10 Feb ‘05

  18. DIRTH (NOAA product) JPL’s Direction Interval Retrieval Threshold Nudging DIRTH TN removes noise in 25-km product, but at some expense • Unnormalised notion of P (WVC and speed dependence) • P segments exclude probable solutions (T=0.8; 0.2 left out) • Medium filter ignores P within segment • No meteorological balance constraints DIRTH results in • Very smooth fields (> 100 km) • Loss of meteorological detail • KNMI proposes Multiple Solution Scheme Miami Workshop 8-10 Feb ‘05

  19. Scatterometer Data Processor INPUT OUTPUT OUTPUT Ocean Surface Radar Backscatter Observations Ambiguity Ambiguity Wind Wind Inversion Inversion Observations Removal Removal Field Field Pre- Process Quality Quality Control Monitor Miami Workshop 8-10 Feb ‘05

  20. Ambiguity Probability Quadratic inner loop approximation? IFS experiments from KNMI + some visits Miami Workshop 8-10 Feb ‘05

  21. QuikSCAT http://www.knmi.nl/scatterometer Miami Workshop 8-10 Feb ‘05

  22. 29 10 2002 NWP Impact @ 100 km Storm near HIRLAM misses wave; SeaWinds should be beneficial! Miami Workshop 8-10 Feb ‘05

  23. Satellite Application Facilities Scatterometer sea surface wind R&D • Quality control, rain and ice screening • Spatial averaging (100 km  25 km) • Inversion: Computation of wind solutions and associated probabilities frommeasurementinformation • Determination of information content; Observation operatorAmbiguity removal (spatial filter to determine unique field) • Active monitoring and control (of instrument and processing) • Web site (visualisation) and product distribution • Product enhancement • Preparation for ASCAT wind production (METOP; 2006) Miami Workshop 8-10 Feb ‘05

  24. Detail in 100-km product KNMI 100km Miami Workshop 8-10 Feb ‘05

  25. Product Verification with ECMWF Winds Comparison for a set of triple KNMI-NOAA-ECMWF points • KNMI 100-km product better for NWP assimilation than NOAA • NOAA wind speed score relatively bad due to wind direction spatial filter • KNMI rejects less high wind points (Portabella &, 2000) Miami Workshop 8-10 Feb ‘05

More Related