1. R. A. Brown 2005 AGU

2. The Satellite + PBL Model calculation of surface pressure The microwave scatterometers, radiometers, SARs and altimeters have now provided nearly three decades of inferred surface winds over the oceans. These can all be converted to excellent surface pressure fields. R. A. Brown 2005 AGU Often these products are revolutionary, changing the way we view the world.

3. 1980 – 2005: Using surface roughness as a lower boundary condition on the PBL, considerable information about the atmosphere and the PBL has been inferred. The symbiotic relation between surface backscatter data and the PBL model has been beneficial to both. The PBL model has established superior ‘surface truth’ winds and pressures for the satellite model functions. Satellite data have shown that the nonlinear PBL solution with OLE is observed most of the time.

4. R. A. Brown 2005 AGU

5. fV + K U zz - p z /  = 0 fU - K V zz + p z /  = 0 The solution, U (f, K,  p ) was found by Ekman in 1904. State of The analytic solution for a PBL fV + K U zz - p z /  = 0 fU - K V zz + p z /  = A(v 2 w 2 ) Solution, U (f, K,  p ) found in 1970. OLE are part of solution for 80% of observed conditions (near-neutral to convective). Unfortunately, this was almost never observed. The complete nonlinear solution for OLE exists, including 8 th order instability solution, variable roughness, stratification and baroclinicity, 1996. Being integrated into MM5, NCEP (2005) Unfortunately, this scale was difficult to observe. R. A. Brown 2005 EGU

6. Station B 2 - 5 km Hazards of taking measurements in the Rolls Station A 1-km RABrown 2004 U VMean Flow Hodograph Z/  1 2 3 The Mean Wind The OLE winds Hodograph from center zone Hodograph from convergent zone

7. The dynamics of the typical PBL revealed in remote sensing data indicate that K-theory in the PBL models is physically incorrect. This will mean revision of all GCM PBL models as resolution increases. Brown, R.A., 2001: On Satellite Scatterometer Model functions, J. Geophys. Res., Atmospheres, 105, n23, 29,195-29,205; Patoux, J. and R.A. Brown, 2001: Spectral Analysis of QuikSCAT Surface Winds and Two-Dimensional Turbulence, J. Geophys. Res., 106, D20, 23,995-24,005; Patoux, J. and R.A. Brown, 2002: A Gradient Wind Correction for Surface Pressure Fields Retrieved from Scatterometer Winds, Jn. Applied Meteor., Vol. 41, No. 2, pp 133-143; R.A. Brown & P. Mourad, 1990: A Model for K-Theory in a Multi- Scale Large Eddy Environment, AMS Preprint of Symposium on Turbulence and Diffusion, Riso, Denmark. On the Use of Exchange Coefficients and Organized Large Scale Eddies in Modeling Turbulent Flows. Bound. Layer Meteor., 20, 111-116, 1981. R. A. Brown 2005 EGU

8. SLP from Surface Winds UW PBL similarity model joins two layers: The nonlinear Ekman solution R. A. Brown 2005 AGU to the log layer solution. Use “inverse” PBL model to estimate from satellite. Use vector math to 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 G (U G ) =  P(U 10 ) U 10

9. 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 2005 EGU

10. The nonlinear solution applied to satellite surface winds yields accurate surface pressure fields. These data show: * The agreement between satellite and ECMWF pressure fields indicate 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 that the mean marine 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 2005 EGU

11. The nonlinear PBL solution applied to satellite surface winds provides sufficient accuracy to determine surface pressure fields from satellite data alone. Patoux, J. and R.A. Brown, 2002: A Scheme for Improving Scatterometer Surface Wind Fields, J. Geophys. Res., 106, No. 20, pg 23,985-23,994 R. A. Brown 2005 EGU

12. R. A. Brown 2004 EGU R. A. Brown 2005 EGU

13. Dashed: ECMWF R. A. Brown 2005 EGU

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

15. QuikSCAT 10 Jan 2005 0709 UTCOPC Sfc Analysis and IR Satellite Image 10 Jan 2005 0600 UTC UWPBL 10 Jan 2005 0600 UTC GFS Sfc Analysis 10 Jan 2005 0600 UTC ab cd

16. OPC 08 Jul 2005 GFS 08 Jul 2005 1001 992 996 1003 QuikSCAT 08 Jul 2005 UWPBL 08 Jul 2005 d c a b

17. Raw scatterometer winds UW Pressure field smoothed JPL Project Local GCM nudge smoothed = Dirth (with ECMWF fields) (JPL) R. A. Brown 2005 EGU To get smooth synoptic wind fields from a scatterometer

18. We can uniquely offer a continuous record of QuikScat-derived surface pressure fields: These pressure fields extend through the Tropics - a region that is poorly characterized by numerical weather forecast models - and contain fine details that are absent from numerical model analyses Patoux, J., R.C. Foster and R.A. Brown, 2003:: Global Pressures from Scatterometer Winds, Jn. Applied Meteor. 42, 813-826 R. A. Brown 2005 EGU

19. Surface pressures as surface ‘truth’ yield high wind predictions. This suggests that the global climatology surface wind record is too low by 10 – 20%. Brown, R.A., & Lixin Zeng, 2001: Comparison of Planetary Boundary Layer Model Winds with Dropwindsonde Observations in Tropical Cyclones, J. Applied Meteor., 40, 10, 1718-1723; Foster & Brown, 1994, On Large-scale PBL Modelling: Surface Wind and Latent Heat Flux Comparisons, The Global Atmos.-Ocean System, 2, 199- 219. R. A. Brown 2005 EGU

20. There is evidence from these data that the secondary flow characteristics of the nonlinear PBL solution (Rolls or Coherent Structures) are present more often than not over the world’s oceans. This contributes to basic understanding of PBL modelling and air-sea fluxes. Brown, R.A., 2002: Scaling Effects in Remote Sensing Applications and the Case of Organized Large Eddies, Canadian Jn. Remote Sensing, 28, 340-345; Levy G., 2001, Boundary Layer Roll Statistics from SAR. Geophysical Research Letters. 28(10),1993-1995. R. A. Brown 2005 EGU

21. Programs and Fields available on http://pbl.atmos.washington.edu Questions to rabrown, neal or jerome@atmos.washington.edu Direct PBL model: PBL_LIB. (’75 -’00) 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 - 2000) 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) (2004) Surface stress fields from PBL_LIB corrected for stratification effects along all swaths (2005) R. A. Brown 2005 EGU

22. Surface Winds & Pressure Fields from Space The ability to extract surface pressure maps from satellite scatterometer data has been described in a series of papers since the ‘80s. The technique has been recently improved for the purpose of providing near real-time surface pressures for NCEP forecasters. The fields have proved more valuable to the forecasters than the raw QuikScat winds while providing more detail in the pressure fields than ever before. Marine Weather from Satellites and PBL models

23. Status of organized large eddies (OLE) verification Airplane campaigns in cold air outbreaks (1976 - ). Ground based Lidar detects OLE (1996 -); Lidar from Aircraft PBL flights (1999 -). Satellite derived surface pressures (1997) using nonlinear PBL model are accurate. Satellite SAR data of ocean surface shows evidence of ubiquitous OLE (1978; 1986; 1997-). R. A. Brown 2005 EGU

24. Surface Pressure fields The basic PBL model uses the geostrophic wind (aka pressure gradient) as a boundary condition to calculate the wind profile and surface stress. It was evident that the same conceptual model could use surface stress as input and calculate the UG or SP. Gad Levy took this task for his thesis work (1981) and the result was the "Inverse Model" which was used to calculate surface pressure fields from satellite data (plus a surface pressure magnitude) (Ocean Surface Pressure Fields from Satellite Sensed Winds, (R.A. Brown and G. Levy), Mon. Wea. Rev., 114, pp 2197- 2206, 1986). This has proven to be an extremely valuable contribution of scatterometer data and has been revised and perfected by Lixin Zeng and Jerome Patoux (Estimating Central Pressures of Oceanic Mid-latitude Cyclones, (R.A. Brown and Lixin Zeng) J. Applied Meteor., 33, 9, 1088- 1095, 1994; A Scheme for Improving Scatterometer Surface Wind Fields, J. Geophys. Res., Patoux, J. and R.A. Brown, 106, No. 20, pg 23,985-23,994, 2002; Global Surface Pressures from Satellite Scatterometers, Patoux and Brown, in press, Jn. Applied Meteor. ) The Surface Pressure (SP) fields were used to correct the MF for high winds by assuming that the pressure measurements on the buoys were still OK in high winds and using SP plus the PBL model to calculate U10. They were routinely 10% higher than the GCM products. Since the inverse pressure fields agreed well with GCM values (in the Northern Hemisphere where there were sufficient surface measurements for the GCM), I proposed that the scatterometer could be viewed as a pressure measurement instrument, and we developed a MF that successfully related SP directly to the scatterometer backscatter measurement. On the other hand, the remote sensing data has given proof that the nonlinear equilibrium PBL solution is the correct one. The first verification came when Mike Freilich ran a UG field simultaneous with the U10 MF parameterization analysis. He noted that they were both good robust model functions and that the UG was turned 19 deg from U10 direction. The model predicts a turning of 18 deg at neutral stratification. The data suggests that the model is correct in the mean, and that the average oceanic PBL is nearly neutrally stratified. However, the most convincing satellite data for rolls comes from the synthetic aperture Radar (SAR) data. These show that a wind MF applied to backscatter data on 100-m resolution reveal long (100s-km) lines of higher roughness/winds, roughly parallel to the wind and separated by the usual roll wavelength of 1-3-km. We obtained a NASA grant to analyze the statistics of these observations and Levy found roll signatures over 50% of the time in the North Pacific. Since I expected the downdraft regions to be sufficient to see only occasionally, the large frequency of observations indicates that the rolls are almost always there. R. A. Brown 2005 EGU