1. Serendipity
the faculty or phenomenon of finding valuable or agreeable things not sought for
R. A. Brown 2006

2. Mission statement for the scatterometer ’78
“We have an instrument that can measure surface stress (or winds near the surface) -- the most important forcing of the oceanic mixed layer.“
Or (might have been…)
We have an instrument that provides backscatter from a 25-km patch of ocean proportional to waves in the 1-6 cm.
R. A. Brown 2006

3. Scatterometer Product from Space Surface WIND vectors 1 9 7 8
Scatterometer
Product
from Space
Surface WIND vectors
R. A. Brown 2006

4. Scatterometer Products from Space 2 0 0 5 WIND vectors Ocean Fronts
Frontogenesis info
Near real time pressure maps for forecasts
Scatterometer
Products from Space
Ocean Fronts
WIND vectors
Atmospheric Fronts
Nonlinear model proof
Daily Global Marine Surface Pressure Fields
Land Vegetation
Pack Ice location, concentration, thickness
Storms: location, Strength
Antarctic ice flow movement
Mean PBL temperature
Mean PBL stratification
Surface stress vector
R. A. Brown 2006

5. Mission statement for a lidar:
“We have an instrument that can measure winds in the troposphere -- the most important dependent variable in the equations of motion --- essential to good weather & climate modelling“ Or
We have an instrument that provides doppler signal return from atmospheric aerosols between the satellite and surface.
R. A. Brown 2006

6. PBL turbulence spectrum Rolls
Lidar PBL
possibilities
GCM updates
Wind vectors
Air-surface Fluxes
PBL turbulence spectrum
Rolls
Aerosol statistics
PBL dynamics and air-surface fluxes
Inversion height
Surface characteristics
R. A. Brown 2006

7. Hazards of taking measurements in the Rolls
e.g. a dropsonde profile
Hodograph
from convergent zone
Hodograph
from center zone
1-km
The OLE winds
Station A 3 U
2 - 5 km 2
The Mean Wind
Z/ 1
Station B V
Mean Flow Hodograph
RABrown 2004

8. Principle of The Red Queen
Named after the chess piece in Alice in Wonderland, --- she moves faster & faster, in more complicated ways, yet Nothing Significant Changes.
Used in Evolution and Biology, mainly to describe the predator-prey relationship.
I note today that the principle seems to apply to modelling the turbulent PBL? (fantastically complicated turbulence models yet no better weather & climate models)
R. A. Brown 2005 EMS

9. Surface Pressures
from Space
R. A. Brown 2005 AGU

10. R. A. Brown 2005 AGU
R. A. Brown 2004 EGU

11. The UW PBL Model is now global and operational
Dashed: ECMWF
Solid: UW-Quikscat
The UW PBL Model is now global
and operational
R. A. Brown AGU

12. NCEP Forecast Analyses
Pressure Fields
used in
NCEP Forecast Analyses
R. A. Brown 2005 AGU

13. a b
996
991
999
996
OPC Sfc Analysis and IR Satellite Image 10 Jan UTC
GFS Sfc Analysis 10 Jan UTC c d
984
982
This example is from 10 January UTC.
Numerical guidance from the 0600UTC GFS model run (b) indicated a 999 hPa low at 43N, 162E.
The QuikSCAT pass from 0752 UTC (c) showed an area of hurricane force winds (red wind barbs) to the south west of the low center.
The GFS model only showed gale force winds. (not shown)
Because there were no surface observation in the vicinity of the low center (a), the forecaster chose not to deviate too far from model guidance, even though QuikSCAT indicated a significantly stronger low than the GFS model and analyzed a 997 hPa low at 42N, 163E on the 0600UTC OPC manual analysis. The pressure gradient on the OPC analysis and the GFS analysis was not representative of hurricane force winds.
The SLP field derived from the UWPBL model (d) using the surface winds from the 0709 UTC QuikSCAT pass produced a significantly deeper 982 hPa low at 42N, 164 E. The pressure field was consistent with the available surface pressure observations in (a) and the gradient was much better represented the stronger wind field.
UWPBL 10 Jan UTC
QuikSCAT 10 Jan UTC

14. Some Conclusions
R. A. Brown 2005 AGU

15. Surface pressures as surface ‘truth’ yield high wind predictions
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, ; Foster & Brown, 1994, On Large-scale PBL Modelling: Surface Wind and Latent Heat Flux Comparisons, The Global Atmos.-Ocean System, 2,
R. A. Brown 2005 AGU

16. 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 ; 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, , 1981.
R. A. Brown 2005 AGU

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

18. These data allow us to build a climatology of primary and secondary cyclones (in particular their kinematics as revealed by scatterometer winds): e.g. test the hypotheses that explosive frontal storm development may have predictors (e.g. upper level vorticity or surface vorticity anomolies) and the possibility that the strength of storms and fronts is increasing due to global warming.
References: Patoux, J. and R.A. Brown, 2002: Spectral Analysis of QuikSCAT Surface Winds and Two-Dimensional Turbulence, J. Geophys. Res., 106, D20, 23,995-24,005; Brown, R.A., 1998: Global High Wind Deficiency in Modeling, Chapter in Remote sensing of the Pacific Ocean by Satellites, p69-77, Southwood Press Pty Limited, Marrickville Australia, pp. 454.
R. A. Brown 2005 EMS

19. For Lidar, there is an Opportunity:
There exist no satellite determined winds in the PBL (there is a scatterometer, but it is due to expire; there’s a radiometer, but it is limited)
There are very few in situe direct measurements of winds in the PBL
Sonde and buoy point wind measurements incur large errors due to turbulence & OLE
The successful parameterization of fluxes (air-surface) requires good boundary layer winds
Climate Analyses are being made on extremely poor data
There are no USA wind satellites planned to launch
R. A. Brown 1/2006

20. Programs and Fields available on http://pbl. atmos. washington
Programs and Fields available on Questions to rabrown, ralph or
Direct PBL model: PBL_LIB. (’75 -’05) An analytic solution for the PBL flow with rolls, U(z) = f( P, To , Ta , )
The Inverse PBL model: Takes U10 field and calculates surface pressure field P (U10 , To , Ta , ) ( )
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 2006