Obukhov Length Estimation From Spaceborne Radars

Owen O’Driscoll, Alexis Aurelien Mouche, Bertrand Chapron, Marcel Kleinherenbrink, Paco López‐Dekker

Geophysical Research Letters · 10 August 2023

In one sentence. Machine learning on SAR-derived features estimates atmospheric surface-layer Obukhov length, explaining about 80% of variance for convective scenes.

Abstract

Abstract Two air‐sea interaction quantification methods are employed on synthetic aperture radar (SAR) scenes containing atmospheric‐turbulence signatures. Quantification performance is assessed on Obukhov length L, an atmospheric surface‐layer stability metric. The first method correlates spectral energy at specific turbulence‐spectrum wavelengths directly to L. Improved results are obtained from the second method, which relies on a machine‐learning algorithm trained on a wider array of SAR‐derived parameters. When applied on scenes containing convective signatures, the second method is able to predict approximately 80% of observed variance with respect to validation. Estimated wind speed provides the bulk of predictive power while parameters related to the kilometer‐scale distribution of spectral energy contribute to a significant reduction in prediction errors, enabling the methodology to be applied on a scene‐by‐scene basis. Differences between these physically based estimates and parameterized numerical models may guide the latter's improvement.

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