May 11, 2001

[1996: Biography of R.A. Brown. Taken from biography for the AMS annual meeting on Boundary Layers and Turbulence, keynote talk on "90 years of PBL Modeling".]

Biographical data for Prof. R.A. Brown

R.A. Brown entered geophysics in 1966, and became the first Ph.D. graduate from the University of Washington Geophysics program in 1969. His thesis was the analytic solution for the flow in the turbulent boundary layer in a rotating frame of reference --- the planetary boundary layer (PBL). This nonlinear equilibrium solution to the turbulent Navier-Stokes equations replaced the Ekman solution using an instability analysis, a finite perturbation, and equilibrium set by the energy equation. The resulting modified Ekman spiral contained large, PBL sized eddies in the form of counter-rotating helical roll vortices. It was used to explain the frequent appearance of cloud streets in the atmosphere, Langmuir circulation in the ocean and other related phenomena. This was the first analytic solution of large-scale organization --- coherent structures --- embedded in a turbulent flow. The theory was extended to include buoyant energy during a visit to NCAR's Advanced Study Program in 1970-71.

In 1971, Brown joined the Arctic Joint Experiment at the University of Washington with the task of calculating surface stress on the pack ice. He achieved this by patching the modified Ekman layer solution to the surface layer (log-layer) solution for the flow very near the surface. The result was a similarity equation linking surface stress, represented by the surface friction velocity, u* = Öt/r , to the gradient velocity, determined by the pressure gradient. This single parameter similarity theory replaced the classical multi-parameter empirical model with an analytic generalization. It became the basis of the PBL model (PBL_LIB) used widely for the past 20 years. It is at present still the only point model for PBL flow that explicitly contains organized large eddies. It thus differs from all other eddy-diffusion (K-theory) and higher order approximations that use diffusion coefficients by correctly representing the physics of large-eddies in an advective sense, as part of the mean flow (nonlinear) solution. The inverse version of this model, developed in the 1980s, allows satellite sensed surface winds to derive the surface pressure fields that can be used to initialize general circulation models more affectively than surface winds.

Beginning in 1983, Brown has been a professor in the Dept. of Atmospheric Sciences at the University of Washington. He has taught Fluid Dynamics of the Atmosphere and written texts on Planetary Boundary Layer Modeling (1974, John Wylie Press) and Fluid Mechanics of the Atmosphere, published by Academic Press in their Geophysics Series (1991). He has taught introductory Atmospheric Sciences, Turbulence in the planetary boundary layer and satellite remote sensing. He is chapter author or co-author of ten other books and over 80 papers. In 1998 he edited the book Remote sensing of the Pacific Ocean by Satellites.

Brown's main interest in geophysicalfluiddynamics theory took a turn toward applications in 1978,  when he got involved in satellite remote sensing, starting with the Seasat project and continuing to the present. He is currently on the Science Teams for the NASA scatterometers NSCAT, Quickscat and SeaWinds, and the Lidar wind sensor (NOAA and NASA), and has been a Principle Investigator for the NASA WETNET project using the SSM/I (radiometer) and a co-PI on a RADARSAT project and two EOS grants.



Remote sensing Book bio 1998:

R.A. Brown produced the basic analytic solution for the flow in a Planetary Boundary Layer, modifying Ekman's solution to introduce the now well known organized large eddies in the PBL (1970). He has written two seminal texts, on Planetary Boundary Layer Modeling (1974, John Wylie Press) and Fluid Mechanics of the Atmosphere,  published by Academic Press in the Geophysics Series (1991), plus chapters in many other texts. Since 1978 he has been a member of numerous NASA satellite sensors Science Teams.