Abstract
Significant wave height (SWH) is of great importance in industries such as ocean engineering, marine resource development, shipping and transportation. Haiyang-2C (HY-2C), the second operational satellite in China’s ocean dynamics exploration series, can provide all-weather, all-day, global observations of wave height, wind, and temperature. An altimeter can only measure the nadir wave height and other information, and a scatterometer can obtain the wind field with a wide swath. In this paper, a deep learning approach is applied to produce wide swath SWH data through the wind field using a scatterometer and the nadir wave height taken from an altimeter. Two test sets, 1-month data at 6 min intervals and 1-day data with an interval of 10 s, are fed into the trained model. Experiments indicate that the extending nadir SWH yields using a real-time wide swath grid product along a track, which can support oceanographic study, is superior for taking the swell characteristics of ERA5 into account as the input of the wide swath SWH model. In conclusion, the results demonstrate the effectiveness and feasibility of the wide swath SWH model.
Highlights
Significant wave height (SWH) is of great importance in industries such as ocean engineering, marine resource development, shipping and transportation
To make the width of the nadir SWH close to the dimension of SCA wind speed, different parameters are considered as inputs, including the nadir SWH, latitude, and longitude acquired by ALT, the wind speed collected by SCA L2B product, as well as swell, wind wave, and SWH obtained by ERA5
We utilize the wind speed and SWH simultaneously retrieved by HY-2C as features, and the SWH obtained by ERA5 as labels, so that a wide swath model can be built
Summary
Significant wave height (SWH) is of great importance in industries such as ocean engineering, marine resource development, shipping and transportation. The nadir SWH obtained by ALT is employed to select the wind speed of SCA, where the time difference is less than 5 s, and we spatially choose the closest wind column.
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