Obukhov Length Estimation From Spaceborne Radars

Type Article
Date 2023-08-16
Language English
Author(s) O’driscoll Owen1, Mouche AlexisORCID1, Chapron BertrandORCID1, Kleinherenbrink MarcelORCID2, López‐dekker Paco2
Affiliation(s) 1 : University Brest CNRS Ifremer IRD Laboratoire d’Océanographie Physique et Spatiale (LOPS) IUEM Plouzané, France
2 : Geoscience and Remote Sensing Civil Engineering and Geosciences Delft University of Technology Delft ,The Netherlands
Source Geophysical Research Letters (0094-8276) (American Geophysical Union (AGU)), 2023-08-16 , Vol. 50 , N. 15
DOI 10.1029/2023GL104228
Keyword(s) Obukhov length, surface-layer stability, SAR, machine learning, regression

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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