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Li Bi Tom Zapotocny James Jung Michael Morgan 31 May 2006

Assimilating and determining the impact of sea surface winds measured by WindSat/Coriolis data in the Global Forecast System. Li Bi Tom Zapotocny James Jung Michael Morgan 31 May 2006. Overview. WindSat is a polarimetric microwave radiometer Measures ocean surface wind speed and direction

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Li Bi Tom Zapotocny James Jung Michael Morgan 31 May 2006

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  1. Assimilating and determining the impact of sea surface winds measured by WindSat/Coriolis data in the Global Forecast System Li Bi Tom Zapotocny James Jung Michael Morgan 31 May 2006

  2. Overview • WindSat is a polarimetric microwave radiometer • Measures ocean surface wind speed and direction • Launched on 6 January 2003 WindSat on Coriolis satellite

  3. WindSat Orbit • Sun-synchronous circular orbit • 830 km altitude • 98.7 degrees of inclination • 1800 Local Time of the Ascending Node (LTAN) • about 14.1 orbits per day • 1800 LTAN for validation with QuikSCAT • WindSat instrument has 1025 km swath width http://www.seaspace.com/news

  4. http://www.seaspace.com/news

  5. Projected Capabilities • Demonstrate the capability of polarimetric microwave radiometry to measure the ocean surface wind vector from space. • How ocean surface physics change with wind and boundary layer conditions. • WindSat will aid with forecast of short-term weather, issuing timely weather warnings and gathering general climate data.

  6. How WindSat can measure wind speed and direction? • Wind roughening the surface of the ocean causes an increase in the brightness temperature of the microwave radiation emitted from the water’s surface. • Multiple frequencies and polarizations allow for simultaneous retrievals of different surface and atmospheric parameters.

  7. Ocean Brightness Temperatures • Tb’s measured by satellite radiometer consists of: • Signal that is emitted from the ocean surface and travels upwards • Upward traveling atmospheric radiation • Downward traveling atmospheric and cold space radiation that is scattered back from the ocean surface http://www.ofcm.gov

  8. Principles of Operation • WindSat measures full polarizations.

  9. CLW Retrievals 18.7,28.8 and 37GHz Wind Direction Retrievals 10.7, 18.7 and 37GHz 3rd and 4th Stokes WV Retrievals 18.7,23.8 and 37GHz Wind Speed Retrievals 10.7,18.7,23.8 and 37GHz NOAA WindSat Wind Vector Retrieval Algorithm – Ver 0 SST Retrievals 10.7GHz http://cioss.coas.oregonstate.edu/

  10. Sample picture of WindSat wind speed from a preliminary wind vector retrieval algorithm. http://manati.orbit.nesdis.noaa.gov/cgi-bin/ws_wdsp_day_noaa.pl

  11. Proposed work • Work with the JCSDA (Joint Center for Satellite Data Assimilation) to evaluate the impact of assimilating WindSat data in the NCEP GFS (Global Forecast System) model. A November 2005 version of the SSI and GFS were used and run at T254L64. • Compare the forecast impact with QuikSCAT data from the same time period. QuikSCAT data have provided a positive impact on forecasts • Data time period: 1 Jan – 15 Feb 2004

  12. First Goal • Run GFS with QuikSCAT (cntrl254) • Run GFS without QuikSCAT (noqscat254) • Study forecast impact for QuikSCAT winds Error in experiment Error in control Error in control

  13. The 500 hPa geopotential height anomaly correlation at day 5 for 20-80N (7 Jan–15 Feb 2004) The 500 hPa geopotential height anomaly correlation at day 5 for 20-80S (7 Jan–15 Feb 2004)

  14. Second Goal • Run GFS with WindSat (windsob1°254) • Run GFS with WindSat (windsob0.5°254) • Run GFS with WindSat no QuikSCAT (windnoq254) • Compare forecast impact for WindSat winds with the forecast impact for QuikSCAT winds • Develop and improve QC techniques and verify the current retrieval algorithm

  15. Additional WindSat Quality Control • Data used only at 6 hour synoptic time with a plus/minus 3 hour window. • If the absolute value of the observed wind component is more than 6 ms-1 from the corresponding background wind component the observation is eliminated. This only removed the extreme outliers.

  16. Control ----- T254L64 to 7 days (no AMSU, no AQUA AIRS, no QuikSCAT) • NoQuikSCAT----- T254L64 to 7 days (no AMSU, no AQUA AIRS, with QuikSCAT) • WindSat 1.0----- T254L64 to 7 days (control + WindSat QC check and superobing to 1 deg) • WindSat 0.5----- T254L64 to 7 days (control + WindSat QC check and superobing to 0.5 deg)

  17. The 500 hPa geopotential height anomaly correlation at day 5 for 20-80N (7 Jan–15 Feb 2004) The 500 hPa geopotential height anomaly correlation at day 5 for 20-80S (7 Jan–15 Feb 2004)

  18. 1000 hPa magnitude of wind difference 2004011806 (Control – WindSat)

  19. SLP difference 2004011806 (Control – WindSat)

  20. Resolution 25km (Experimental GDAS) EDR’s (environmental data records ) TPW CLW Rain Rate SST Wind Speed std~0.8m/s [3-20]m/s Wind Direction Std~25° for wspd<6m/s Sdt~17° for wspd >6m/s Limitation CLW>0.2mm degradation in retrievals of surface parameters Resolution 25km (Operational GDAS) 12.5km 2.5km EDR’s Wind Speed Std ~0.8m/s [3-35]m/s Wind Direction Std ≤ 20° for wspd>3m/s Limitation Wind speed and direction retrievals degrade in rainy areas WindSat vs. QuikSCAT http://www.ofcm.gov

  21. WindSat and QuikSCAT Wind Fields WindSat QuikSCAT http://www.npoess.noaa.gov/polarmax

  22. WindSat (1° superob) and QuikSCAT (0.5° superob) data counts and RMS error (20040125)

  23. The 1000 hPa wind speed anomaly correlation at day 5 for 20-80N (7 Jan–15 Feb 2004) The 1000 hPa wind speed anomaly correlation at day 5 for 20-80S (7 Jan–15 Feb 2004)

  24. Geopotential height anomaly correlation of Wind_sob_1.0deg and QuikSCAT_0.5deg at day 5 for 20-80N and 20-80S (7 Jan–15 Feb 2004)

  25. Future Goals • Examine the direct assimilation of the WindSat radiances into the GFS by either updating the spectral statistical interpolation (SSI) assimilation system or switching to the GSI. • Conduct the same study with higher operational resolution (T382) and an alternate season. • Study the regional impact of WindSat assimilation and compare the difference between WindSat and QuikSCAT data.

  26. Conclusions • Preliminary results indicate that QuikSCAT data improved the forecast more than WindSat data for a majority of the cases examined. • The combination of WindSat and QuikSCAT provided the largest positive forecast impact by day 7. • At T254 WindSat demonstrated larger AC gains with a 1° superob than a 0.5° superob. (QuikSCAT uses a 0.5° superob by default).

  27. Acknowledgements • Prof. Michael Morgan (UW-AOS) for giving insightful advice and for local computer resources. • Stephen Lord (NCEP) for computer resources and tape space. • Stacie Bender and Dennis Keyser for collecting and processing our various data streams.

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