Abstract
Accurate offshore surface wind forecasting is crucial for navigation safety and disaster prevention. However, significant biases exist in forecasting sea surface winds due to the uncertainties in estimating sea surface roughness. In this study, we propose a deep learning-based scheme (DL2023) for estimating sea surface roughness and integrate it into a regionally coupled ocean-atmosphere-wave model. Single-point experiments demonstrate that DL2023 achieves a remarkable 50% reduction in the Root Mean Square Error (RMSE) compared to the four traditional schemes. During five typhoon cases in August 2020, compared to the four traditional schemes, the RMSEs of forecasted surface winds using DL2023 are reduced by 6.02%–14.75%, 11.17%–18.30%, and 11.91%–19.46% at lead times of 24, 48, and 72 hr, respectively. Thus, the DL2023 scheme, trained using data from the Atlantic Ocean, successfully improves the forecast of surface winds over the Northwest Pacific Ocean.
| Original language | English |
|---|---|
| Article number | e2023GL106580 |
| Journal | Geophysical Research Letters |
| Volume | 50 |
| Issue number | 24 |
| DOIs | |
| State | Published - Dec 28 2023 |
| Externally published | Yes |
Funding
We thank the editor and two anonymous reviewers for constructive comments that led to the improvement of the manuscript. This work was jointly supported by National Key R&D Program of China (2020YFA0608000), National Natural Science Foundation of China (42275154), and Tsinghua University Initiative Scientific Research Program (2019Z07L02011).