1. The Scatterometer is no longer a competitor for lidar winds R. A. Brown 2007 Lidar Miami

2. RIP ( USA ) R. A. Brown 2007 Lidar Miami

3. UW; Patoux, ‘03 R. A. Brown 2007 Lidar Miami

4. SeaSat 1978 ERS -1 1991-95 ERS-2 1995-2001 NSCAT 1996-97 QuickScat 1999 - SeaWinds1 1998-1998 SeaWinds2 2002 - 2002 ASCAT 2007 R. A. Brown 2007 Lidar Miami ERS-2 1995-2001; 2003 -

5. R. A. Brown 2003 U. Concepci Ó n

7. R. A. Brown 2004 EGU

8. The solution for the PBL boundary layer (Brown, 1974, Brown and Liu, 1982), may be written U/V G = e i  - e –z [e -iz + ie iz ]sin  + U 2 where V G is the geostrophic wind vector, the angle between U 10 and V G is  [u*,  H T, (T a – T s,) PBL ] and the effect of the organized large eddies (OLE) in the PBL is represented by U 2 (u*, T a – T s,  H T) U/V G =  {  (u*), U 2 (u*), u*, z o (u*), V T (  H T),  (T a – T s ), } Or U/V G =  [u*, V T (  H T),  (T a – T s ),, k, a ] =  {u*,  H T, T a – T s }, for = 0.15, k= 0.4 and a = 1 V G =  (u*,  H T, T a – T s )   n (  P, , f) Hence  P =  n [ u*(k, a, ),  H T, T a – T s, , f ]  f n (  o ) This may be written: In particular, R. A. Brown 2006 AMS

9. SLP from Surface Winds UW PBL similarity model joins two layers: Use “inverse” PBL model to estimate from satellite. Get non-divergent field U G N. Use Least-Square optimization to find best fit SLP to swaths There is extensive verification from ERS-1/2, NSCAT, QuikSCAT (U G N ) UGUG R. A. Brown 2006 AMS

10. Dashed: ECMWF J. Patoux & R. A. Brown

11. ECMWF analysis QuikScat analysis Surface Pressures J. Patoux & R. A. Brown

12. The low is deeper, and the pressure gradients are stronger, especially where the winds are strong and where the streamlines are curved the most. This can be appreciated on the western flank of the low. The whole structure of the pressure field is affected by the gradient wind correction: it is more asymmetric, as well as deeper. Note that the uncorrected low is shallower than indicated by ECMWF, but that the corrected low is deeper than indicated by ECMWF. R. A. Brown 2003 U. ConcepciÓn EC ----- UW + gradient wind UW ___

13. Raw scatterometer winds UW Pressure field smoothed JPL Project Local GCM nudge smoothed = Dirth (with ECMWF fields) (JPL) R. A. Brown 2006 AMS

14. The nonlinear solution applied to satellite surface winds yields accurate surface pressure fields. These data show: * Agreement between satellite and ECMWF pressure fields indicate that both Scatterometer winds and the nonlinear PBL model (V G /U 10 ) are accurate within  2 m/s. * A 3-month, zonally averaged offset angle of 19° suggests the mean PBL state is near neutral (the angle predicted by the nonlinear PBL model). * Swath deviation angle observations infer thermal wind and stratification. * Higher winds are obtained from pressure gradients and used as surface truth (rather than from GCM or buoy winds). * V G (pressure gradients) rather than U 10 is being used to initialize GCMs R. A. Brown 2006 AMS

15. The nonlinear solution applied to satellite surface winds yields accurate surface pressure fields. These data show: * Agreement between satellite and ECMWF pressure fields. This indicates that both the Scatterometer winds and the nonlinear PBL model (V G /U 10 ) are accurate within  2 m/s. * A 3-month, zonally averaged offset angle of 19° suggests the mean PBL state is near neutral (the angle predicted by the nonlinear PBL model). * Swath deviation angle observations can be used to infer thermal wind and stratification. * Higher winds are obtained from pressure gradients and used as surface truth (rather than from GCM or buoy winds). * V G (pressure gradients) rather than U 10 could be used to initialize GCMs R. A. Brown 2006 AMS

16. NCEP real time forecasts use PBL model Even the best NCEP analysis, used as the first guess in the real time forecasts, is improved with the QuikScat surface pressure analyses. R. A. Brown 2007 Lidar Miami

17. This is the 0600 UTC Ocean Prediction Center hand drawn surface analysis superimposed over the GOES12 0615UTC Image.

18. This is a cut and paste of two QuikScat passes. L 993 L 999 L 1003

19. This is a composite of two runs of the model using the 0549 QuikSCAT pass and the 0648 QuikSCAT pass.

20. This is the composite of three consecutive runs of the uwpbl model using the 0543, 0648 and 0834 QuikSCAT passes superimposed on the GOES 12 image from 0645 UTC

21. This is the Ocean Prediction Center hand drawn surface analysis 0526 0600 UTC with the surface vorticity field from the uwpbl model overlaid. The vorticity field is a composite of three passes (0543, 0648 and 0834 UTC); it shows the locations of the frontal zones well.

22. OPC Sfc Analysis and IR Satellite Image 10 Jan 2005 0600 UTCUWPBL 10 Jan 2005 0600 UTC ab cd QuikSCAT 10 Jan 2005 0709 UTC 991 982 996 999 984 This example is from 10 January 2005 0600UTC. Numerical guidance from the 0600UTC GFS model run (a) indicated a 999 hPa low at 43N, 162E. QuikScat winds (b) suggested strong lows --- OPC analysis uses 996. UW-PBL analysis indicates 982. GFS Sfc Analysis 10 Jan 2005 0600 UTC

23. Programs and Fields available on http://pbl.atmos.washington.edu Questions to rabrown, Ralph or jerome @atmos.washington.edu Direct PBL model: PBL_LIB. (’75 -’05) An analytic solution for the PBL flow with rolls, U(z) = f(  P,  T o,  T a, ) The Inverse PBL model: Takes U 10 field and calculates surface pressure field  P (U 10,  T o,  T a, ) (1986 - 2005) Pressure fields directly from the PMF:  P (  o ) along all swaths (exclude 0 -  5° lat.?) (2001) (dropped in favor of I-PBL) Global swath pressure fields for QuikScat swaths (with global I- PBL model) (2005) Surface stress fields from PBL_LIB corrected for stratification effects along all swaths (2006) R. A. Brown 2007 Lidar Miami

24. R. A. Brown, J. Patoux R. A. Brown 2005 EGU

25. In this case, the system is decaying but a secondary low is developing behind the remnants of the cold front. Note also the correspondance between convergence and clouds. R. A. Brown 2003 U. ConcepciÓn