Multi-source calibration of wind speed and wave height from altimeter by machine learning

Abstract

Altimeters can be used to observe global ocean dynamic environment parameters, including sea surface 10-m wind speed (U10) and significant wave height (SWH). Many altimeters are working in two frequency bands and thus have two independent sets of U10 and SWH observations. Their accompanying nadir-viewing microwave radiometers can also retrieve U10. Merging these independent retrievals can be helpful to achieve high accuracy. In this study, the U10 and SWH data from the Jason-1 satellite were calibrated against the buoy observations from the National Data Buoy Center (NDBC). A deep learning technique was used to merge the data from the Ku and C bands and the Jason microwave radiometer. This algorithm employs multiple altimeter-observed parameters, including SWH, backscatter cross-section, and brightness temperature, as inputs to effectively enhance the retrieval accuracy. The overall root mean square error was reduced from 1.42 m/s to 1.18 m/s for U10 and from 0.31 m to 0.26 m for SWH. A pronounced improvement was observed in wind speed data accuracy under rainfall conditions. The principles underlying this model can be further applied to other altimeter satellites, thereby enhancing their precision for wind speed and SWH.

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Funding

National Key Research and Development Program of China(2022YFC3104900/05)

National Natural Science Foundation of China(42376172)

Basic and Applied Basic Research Foundation of Guangdong Province(2024A1515012032)

RIGHTS & PERMISSIONS

The Author(s)