2. Sources of Surface Wind Fields for Climate Studies From Surface Measurements –Ships –Buoys From Models –GCM (with K-theory PBLs) –UW Similarity Model (with OLE) From Satellites –Scatterometers –SAR, Altimeter, SSMI, WindSat…. –Lidar? R.A. Brown,, 2003

3. Sources of Tropospheric Wind Fields for Climate Studies From Surface Measurements –Radiosondes –Radar From Models –GCM From Satellites –Lidar R.A. Brown, 1997, 2001

4. Ways to improve Wind accuracy for Climate Studies ProblemRemedy Date ? Accuracy Better surface obs. (2010) Better satellite Model functions (2003 ) Improve Surface Wind Error in GCMs Increase Resolution to meters in the PBL 2025 Better PBL parameterizations Analytic Similarity Theory 2003 LES & CFD Numerical Models 2010 Sparsity More buoys (2010) More Satellite Data 1991 - Satellite Data Sparsity SSM/I (1988)  + WindSat (2003-) QuikScat(1996 ) + SeaWinds 2003  Lidar Troposphere Winds 2009 R.A. Brown 2003

5. SeaSat 1978 ERS -1 1991-95 ERS-2 1995-2001 NSCAT 1996-97 QuickScat 1999 - SeaWinds 2002 ASCAT 2004 R. A. Brown 2003

7. Better GCM Progs (initialization) (even better than Atlas says) Better Storms Definition (not quite as good as R. A. Brown says) Evolution of Fronts & Cyclones Better Hurricane Initialization, forecasts

8. Revelations 1 – Storms are : Often misplaced Stronger (deeper Pressures) More frequent than found in GCMs and climatology records. Hence tandem data will improve all of these R. A. Brown 2003

9. Revelations 2 There still exist large regions of High Winds (1000 km 2 /storm) that nobody knows of…… These do not appear in: GCM analyses Buoy data Climate data Satellite data (some) They will appear twice as often in tandem data R. A. Brown 2000-2003

10. Buoy winds are not good surface truth for U > 7 m/s GCM PBL models still have wrong physics There is no usable  o when there is a modest rain rate The  oV saturates (due to white water) @ U 10 ~ 35 m/s (but  oH may respond to 65 m/s) The PMF/scat pressure data has better resolution than GCMs. The tandem scatterometers are very valuable for research into synoptic and smaller scales The WindSat will attempt to replace scats --- chances are 50-50 at best CONCLUSIONS R.A. Brown 2003 3

11. Revelations 3 Fronts: Defined as lines of different sea state (roughness variation) From Scat data appear: Ubiquitous Long (1000s-km) Persistent (week) Unstable, cyclogenetic R. A. Brown 1/2000

12. What is a Front? Synopticians disagree. Defined by: Temperature difference Air mass difference (history) From our perspective, defined by: (sea-state)  Surface Wind difference What we know: There’s a difference in small-scale sea state along a line

13. The ( long, slow, tortuous ) Path to a Global PBL Model It must include all turbulence effects, from the surface to geostrophic (gradient) wind balance The Ekman solution with constant K or variable K (1904), was not observed The nonlinear equilibrium Ekman solution with organized large eddies (OLE) (1970) is observed A mixed-layer model for the tropics is patched to the OLE similarity model  global PBL model (2003) R.A. Brown 2003

15. The winds can be inferred from any measurement of a dependent variable in the balance equations or from a good numerical model (2003) Learning (and teaching) the dynamics is difficult. The sensors are expensive. Crude correlations with inexpensively measured passive variables (Temperature, clouds, aerosols..) are relatively easy. However, these winds are much less accurate, greatly averaged and lack the resolution of active satellite wind sensors. The active sensor winds yield good improvement in GCM at coarse resolution, smaller improvements at greater resolutions The greatest value of increased resolution (SAR, Lidar) and coverage (tandem) is in science applications The USA & NASA, probably lack the funds to do things right. Summary: Status of remote sensing of Winds R. A. Brown 2003

17. Wind vectors PBL turbulence spectrum Rolls Aerosol statistics Inversion height Surface characteristics R. A. Brown 2002

18. 0 1000 5000 Height in meters 10 Scatterometer Measurements Scatt correlation Scatt-PBL Inference Lidar Msmt Lidar inference Lidar Measurements

19. Surface Stress, u* Ocean surface Geostrophic Flow U 10 Surface Layer Ekman Layer with OLE Thermal Wind Nonlinear OLE Advection,centrifugal terms Non steady-state U 10 (u*) effects Stratification Variable Surface Roughness V G (u*) effects R.A. Brown PORSEC 2000 Gradient Wind 1-3 km 0 – 100 m

20. High Winds Study - Motivation High Marine Surface Winds do not appear in: –Buoy data –Climate records –General Circulation Models –Satellite sensor algorithms (most) Higher winds imply higher heat fluxes in climatology; revised ocean mixed layer models. High Marine Surface Winds do appear in: –Ocean Meteorology Ship reports –Dedicated Airplane PBL Flights –A PBL model that includes OLE R.A. Brown, 1997, 2000,2003

21. Surface ‘Truth’ Limits Ship winds : Sparse and inaccurate (except Met. Ships). Buoy winds : Sparse; tilt; a point average; variable height; miss high winds and low wind directions. GCM winds : Bad physics in PBL Models; Too low high winds, too high low winds. Resolution coarse (getting better). Satellite winds: Lack good calibration data. Coverage coarse (getting better). Practical Aspects of a Wind Model function 7/00 RAB

22. Practical Aspects of a Geostrophic Wind Model Function (Pressures) Surface ‘Truth’ Limits Buoy and ship pressures: Accurate in low and high wind regimes; sparse GCM: Good verification; compatible scale 11-99, RAB

23. The Measurement of very high winds Some Met. Ship records have sustained winds ~ 45 m/s. About 2-3 events/year Some airplane measurements ~ 35 m/s @ 50’; 50 m/s @ 500’; 70 m/s @ 5000’. Rare buoys ~ 45 m/s. Rare towers ~ 45 m/s. Pressure gradients V G ~ 60 m/s U 10 ~ 40 m/s R.A. Brown 2001