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.
Original language | English |
---|---|
Article number | e2023GL104228 |
Number of pages | 11 |
Journal | Geophysical Research Letters |
Volume | 50 |
Issue number | 15 |
DOIs | |
Publication status | Published - 2023 |
Keywords
- machine learning
- Obukhov length
- radars
- regression
- SAR
- surface-layer stability