Articles | Volume 14, issue 12
Atmos. Meas. Tech., 14, 7435–7451, 2021
https://doi.org/10.5194/amt-14-7435-2021
Atmos. Meas. Tech., 14, 7435–7451, 2021
https://doi.org/10.5194/amt-14-7435-2021

Research article 30 Nov 2021

Research article | 30 Nov 2021

Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry

Xingou Xu and Ad Stoffelen

Data sets

Wind products KNMI https://scatterometer.knmi.nl/archived_prod/

Wind products EUMETSAT https://osi-saf.eumetsat.int/products/wind-products

Precipitation Data Directory NASA https://gpm.nasa.gov/data/directory

JAXA Himawari Monitor Japan Aerospace Exploration Agency (JAXA) https://www.eorc.jaxa.jp/ptree/index.html

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Short summary
The support vector machine can effectively represent the increasing effect of rain affecting wind speeds. This research provides a correction of deviations that are skew- to Gaussian-like features caused by rain in Ku-band scatterometer wind. It demonstrates the effectiveness of a machine learning method when used based on elaborate analysis of the model establishment and result validation procedures. The corrected winds provide information previously lacking, which is vital for nowcasting.