Observational Insights of Nearshore Wind Stress and Parameterizations From Gaussian Process Regressions
Abstract The nearshore wind stress, u∗2, is examined using machine‐learning models for air‐ocean data collected via new flux buoys deployed across four experiments. Consistent with prior nearshore studies, existing open‐ocean models predict nearshore u∗2 with a large error of 0.0152 m2/s2. Gaussian...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-10-01
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| Series: | Geophysical Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2023GL106825 |
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| Summary: | Abstract The nearshore wind stress, u∗2, is examined using machine‐learning models for air‐ocean data collected via new flux buoys deployed across four experiments. Consistent with prior nearshore studies, existing open‐ocean models predict nearshore u∗2 with a large error of 0.0152 m2/s2. Gaussian Process Regression (GPR) for nearshore u∗BM2 is developed, reducing errors to 0.0108 m2/s2. Nearshore air‐sea parameterizations are examined with wind speed (61%) and the wind‐wave frequency of encounters (16%) being the most important. A simpler nearshore, GPR‐derived, wind‐dependent‐only model (u∗NSU2) is developed, with errors of 0.0135 m2/s2. GPRs, evaluated using identical variables, were applied to nearshore observations, and these observations modeled with open‐ocean formulations for an initial comparison of parameterizations between these two regimes. The parameterizations are similar, though with subtle nonlinear differences. The new nearshore data set and machine‐learning models enhance the accuracy of predictions and understanding of differences from the open‐ocean. |
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| ISSN: | 0094-8276 1944-8007 |