Validation and Calibration of Significant Wave Height and Wind Speed Retrievals from HY2B Altimeter Based on Deep Learning

Remote Sensing

Lotfi Aouf¹  Jiuke Wang²  Yongjun Jia³  Youguang Zhang³ 

[1] Department of Marine and Oceanography, 31057 Toulouse, France;National Marine Environmental Forecasting Center, Beijing 100081, China;National Satellite Ocean Application Service, Beijing 100081, China;

关键词: HY2B;    SWH;    wind speed;    validation;    calibration;    deep learning;   

DOI  :  10.3390/rs12172858

来源: DOAJ

【 摘 要 】

HY2B is now the latest altimetry mission that provides global nadir significant wave height (SWH) and sea surface wind speed. The validation and calibration of HY2B are carried out against National Data Buoy Center (NDBC) buoy observations from April 2019 to April 2020. In general, the HY2B altimeter measurements agree well with buoy observation, with scatter index of 9.4% for SWH, and 15.1% for wind speed. However, we observed a significant bias of 0.14 m for SWH and −0.42 m/s for wind speed. A deep learning technique is novelly applied for the calibration of HY2B SWH and wind speed. Deep neural network (DNN) is built and trained to correct SWH and wind speed by using input from parameters provided by the altimeter such as sigma0, sigma0 standard deviation (STD). The results based on DNN show a significant reduction of the bias, root mean square error (RMSE), and scatter index (SI) for both SWH and wind speed. Several DNN schemes based on different combination of input parameters have been examined in order to obtain the best model for the calibration. The analysis reveals that sigma0 STD is a key parameter for the calibration of HY2B SWH and wind speed.

【 授权许可】

Unknown