1. Also known as CMIS R. A. Brown 2005 LIDAR Sedona

2. Passive Radars

3. R. A. Brown 2003 U. Concepci Ó n Same principal as Scatterometer but signal is much weaker Hence: speed only from SMMR, SSMI,…..

4. Solar reflectance Brightness Temperature Two looks at the same spot R. A. Brown 2004

5. What is Ocean Observer? Operational data for Navy and NOAA Science data for NASA and NOAA R&D sensor proof of concept for NASA Operational transition for NASA and NOAA Team approach to solving mutual problems at for OMB Oceans mainly reduced agency cost

6. NPOESS WindSat becomes CMIS

7. Primary Contributions to EDRs by Sensor Environmental Data Records (EDRs) with Key Performance Parameters

8. Joint IPO/DoD/NASA Risk Reduction Demo WindSat/Coriolis Description: Measures Ocean Surface Wind Speed, Wind Direction, Using Polarimetric Radiometer on a Modified Satellite Bus, Launched Into a 830 km 98.7° Orbit by the Titan II Launch Vehicle. 3 Year Design Lifetime. Capability/Improvements Measure Ocean Surface Wind Direction (Non- Precipitating Conditions). Two looks at same spot.Measure Ocean Surface Wind Direction (Non- Precipitating Conditions). Two looks at same spot. 25km spatial resolution25km spatial resolution Secondary MeasurementsSecondary Measurements Sea Surface Temperature, Soil Moisture, Rain Rate, Ice, and Snow Characteristics, Water VaporSea Surface Temperature, Soil Moisture, Rain Rate, Ice, and Snow Characteristics, Water Vapor Launched: January 2003 ? (A stealth mission) R. A. Brown 2004 Data release: Sept. 2004

9. Neil Tyson’s address/campaign On the Future of NASA Jan 20, 2005 “LEO (low earth orbits) are old hat and boring. NASA must do new stuff – space” President’s commission --- “Vision” (thing) Winners: Space Exploration Planetary Science Astrobiology Astrophysics Astronomy Losers: Einstein prerogatives Earth Science R. A. Brown 2005 LIDAR Sedona

10. WindSAT Cal/Val with SLP Retrievals Ralph Foster, Applied Physics Laboratory, U. WA Jerome Patoux, R.A. Brown, Atmospheric Sciences, U. WA R. A. Brown 2005 LIDAR Sedona

11. Outline Two questions: –How well does WindSAT perform when it’s working at its best? –Can Sea-Level Pressure (SLP) fields help improve model function and ambiguity selection? Physics of SLP(U 10 ) QuikSCAT example Methodology WindSAT results –Comparison with ECMWF SLP Analyses & QuikSCAT wind distributions –Ambiguity selection procedure R. A. Brown 2005 LIDAR Sedona

12. SLP from Surface Winds UW PBL similarity model Use “inverse” PBL model to estimate from satellite Use Least-Square optimization to find best fit SLP to swaths Extensive verification from ERS-1/2, NSCAT, QuikSCAT (U G N ) R. A. Brown 2005 LIDAR Sedona

13. ECMWF analysis QuikScat analysis Surface Pressures

14. Surface Pressure as Surface Truth For good quality and consistent U 10 input, SLP fields are a good match to ECMWF analyses SLP/Model-derived U 10 is an “optimally smoothed” low-pass filtered comparison data set –Wind-sensor derived product only –Model U 10 tend to agree with input U 10 for good swath input If SLP fields are wrong, pressure gradients and hence U 10 are wrong. R. A. Brown 2005 LIDAR Sedona

15. Dashed: ECMWF

16. Dashed: ECMWF

17. All four swaths for both WindSAT and QuikSCAT

18. Results WindSAT is biased high for U 10 ~ < 8 m/s –Too few winds U 10 < 5 m/s –Too many winds 5 < U 10 < 8 m/s Implied grad(SLP) too high when U 10 ~< 8 m/s –Implications for assimilation in NWP Too few WindSAT winds in 8 < U 10 < 12 m/s Comparable to QuikSCAT 12 < U 10 < 15 m/s –SLP agrees better in higher wind regime Too small sample to assess higher winds R. A. Brown 2005 LIDAR Sedona

19. Use SLP to Assess Direction Winds derived from SLP are optimal smooth winds Arbitrary threshold of 35 o from Model U 10 used to distinguish potentially wrong ambiguity choice Look for a WindSAT ambiguity with closer direction to Model winds in these cases R. A. Brown 2005 LIDAR Sedona

20. Noisy directions Front captured Changed ambiguities away from clouds & low winds: Why?

21. Conclusions There is a lot of wind vector information in the WindSAT swaths The agreement of the WindSAT-derived SLP fields with ECMWF is surprisingly good for a first- cut model function. Better in higher winds An improved model function will produce better SLP SLP can be used to assess and improve the WindSAT wind data R. A. Brown 2005 LIDAR Sedona

22. Conclusions (cont.) SLP fields demonstrate that the current WindSAT model function often produces a poor wind speed distribution –Wind speed distribution can be robustly evaluated with SLP –Storm analyses will address high wind distribution Wind directions are noisy and there there is room for ambiguity selection improvement. SLP shows promise for this need R. A. Brown 2005 LIDAR Sedona

23. Next SLP adds the robust ECMWF & NCEP surface analyses and buoy pressure observations to the WindSAT Cal/Val data –We are developing methods to use buoy/analysis pressures to identify & correct deficiencies in model function, e.g. Zeng and Brown (JAM 37 1998) Continue development of SLP ambiguity selection procedure Combining SLP with water vapor, clouds & SST will greatly improve storms and fronts research and analysis R. A. Brown 2005 LIDAR Sedona