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Atmosphere 2016, 7(2), 14;

Optimized Estimation of Surface Layer Characteristics from Profiling Measurements

The Harker School, San Jose, CA 95128, USA
Department of Meteorology, Naval Postgraduate School, Monterey, CA 93943, USA
Author to whom correspondence should be addressed.
Academic Editor: Robert W. Talbot
Received: 23 September 2015 / Revised: 8 January 2016 / Accepted: 12 January 2016 / Published: 22 January 2016
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New sampling techniques such as tethered-balloon-based measurements or small unmanned aerial vehicles are capable of providing multiple profiles of the Marine Atmospheric Surface Layer (MASL) in a short time period. It is desirable to obtain surface fluxes from these measurements, especially when direct flux measurements are difficult to obtain. The profiling data is different from the traditional mean profiles obtained at two or more fixed levels in the surface layer from which surface fluxes of momentum, sensible heat, and latent heat are derived based on Monin-Obukhov Similarity Theory (MOST). This research develops an improved method to derive surface fluxes and the corresponding MASL mean profiles of wind, temperature, and humidity with a least-squares optimization method using the profiling measurements. This approach allows the use of all available independent data. We use a weighted cost function based on the framework of MOST with the cost being optimized using a quasi-Newton method. This approach was applied to seven sets of data collected from the Monterey Bay. The derived fluxes and mean profiles show reasonable results. An empirical bias analysis is conducted using 1000 synthetic datasets to evaluate the robustness of the method. View Full-Text
Keywords: surface layer; optimal estimation; turbulence characteristics surface layer; optimal estimation; turbulence characteristics

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Kang, D.; Wang, Q. Optimized Estimation of Surface Layer Characteristics from Profiling Measurements. Atmosphere 2016, 7, 14.

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