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Surface Pressures from Space. R. A. Brown 2005 AGU. The Satellite + PBL Model calculation of surface pressure.

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Surface Pressures from Space

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Surface Pressures

from Space

R. A. Brown 2005 AGU

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.

  • Often these products are revolutionary, changing the way we view the world.

R. A. Brown 2005 AGU

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 dataand 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.

The PBL Model

(surface winds to pressures}

R. A. Brown 2005 AGU

State of The analytic solution for a PBL

fV + K Uzz - pz / = 0

fU - K Vzz + pz /  = 0

The solution, U (f, K,p ) was found by Ekman in 1904.

Unfortunately, this was almost never observed.

fV + K Uzz - pz/ = 0

fU - K Vzz + pz/ = A(v2w2)

Solution, U (f, K,p ) found in 1970. OLE are part of solution for 80% of observed conditions (near-neutral to convective).

Unfortunately, this scale was difficult to observe.

The complete nonlinear solution for OLE exists, including 8th order instability solution, variable roughness, stratification and baroclinicity, 1996. Being integrated into MM5, NCEP (2005)

R. A. Brown 2005 EGU

Hazards of taking measurements in the Rolls


from convergentzone


from center zone


The OLE winds

Station A



2 - 5 km


The Mean Wind



Station B


Mean Flow Hodograph

RABrown 2004

  • 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

SLP from Surface Winds

  • UW PBL similarity model

    joins two layers:

    The nonlinear Ekman



to the log layer solution.

Use “inverse” PBL model to estimate from

satellite . Use vector math to get non-divergent field UGN. Use Least-Square optimization to find best fit SLP to swaths:


(UG) = P(U10)

There is extensive verification from ERS-1/2, NSCAT, QuikSCAT

R. A. Brown 2005 AGU

The solution for the PBL boundary layer (Brown, 1974, Brown and Liu, 1982), may be written

U/VG = ei - e –z[e-iz + ieiz]sin  +U2

where VG is the geostrophic wind vector, the angle between U10 and VG is [u*, HT, (Ta – Ts,)PBL] and the effect of the organized large eddies (OLE) in the PBL is represented by U2(u*, Ta – Ts, HT)

This may be written:

U/VG ={(u*), U2(u*), u*, zo(u*), VT(HT), (Ta – Ts), }

OrU/VG= [u*, VT(HT), (Ta – Ts), , k, a]=  {u*, HT, Ta – Ts},

for  = 0.15, k= 0.4 and a = 1

In particular,

VG =  (u*,HT, Ta – Ts) n(P, , f)

HenceP= n [u*(k, a, ), HT, Ta – Ts, , f ] fn(o)

R. A. Brown 2005 EGU

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 (VG/U10) are accurate within  2 m/s.

* A 3-month, zonally averaged offset angle <VG, U10> 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).

* VG (pressure gradients) rather than U10 could be used to initialize GCMs

R. A. Brown 2005 EGU

R. A. Brown 2005 EGU

  • 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

R. A. Brown 2005 EGU

R. A. Brown 2004 EGU



R. A. Brown 2005 EGU

Surface Pressures

QuikScat analysis

ECMWF analysis

J. Patoux & R. A. Brown



OPC Sfc Analysis and IR Satellite Image 10 Jan 2005 0600 UTC

QuikSCAT 10 Jan 2005 0709 UTC



UWPBL 10 Jan 2005 0600 UTC

GFS Sfc Analysis 10 Jan 2005 0600 UTC

GFS 08 Jul 2005

OPC 08 Jul 2005






UWPBL 08 Jul 2005

QuikSCAT 08 Jul 2005





To get smooth synoptic wind fields from a scatterometer


Raw scatterometer winds


Pressure field smoothed

JPL Project Local GCM nudge smoothed = Dirth (with ECMWF fields)

R. A. Brown 2005 EGU

R. A. Brown 2005 EGU

  • 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 thatare 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

  • 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

  • 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 RollStatistics from SAR. Geophysical Research Letters. 28(10),1993-1995.

R. A. Brown 2005 EGU

Programs and Fields available onhttp://pbl.atmos.washington.eduQuestionsto rabrown, neal [email protected]

  • Direct PBL model: PBL_LIB. (’75 -’00) 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 , ) (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

R. A. Brown 2005 EGU

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

R. A. Brown 2005 EGU

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