Articles | Volume 18, issue 11
https://doi.org/10.5194/essd-18-7963-2026
https://doi.org/10.5194/essd-18-7963-2026
Data description article
 | 
02 Nov 2026
Data description article |  | 02 Nov 2026

MLAWind: a monthly sea surface wind dataset derived from an interpretable machine learning approach integrating in-situ observations and satellite data

Weihao Guo, Rongwang Zhang, Xin Wang, and Dongxiao Wang

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Cited articles

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This study develops a monthly near-global sea surface wind dataset (MLAWind) from 1950–2023 through an interpretable machine learning framework integrating in-situ observations and satellite data. Evaluations show that MLAWind achieves comparable performance to the widely-used reanalysis datasets at different time scales. It provides reliable historical wind data during both satellite and non-satellite periods, demonstrating broad application prospects in ocean and climate research.
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