High Wind Geophysical Model Function Modeling for the HY-2A Scatterometer Using Neural Network

Under low to medium wind speeds and no rainfall, the retrieved vector wind from a scatterometer is accurate and reliable. However, under high wind conditions, the currently used geophysical model function (GMF), such as NSCAT-2, for wind vector retrieval has the disadvantage of overestimating the backscattering coefficient, which leads to a decrease in the quality of the retrieved ocean surface winds. To enhance the wind retrieval precision of the HY-2A scatterometer under high wind conditions, a new GMF for high wind (HW-GMF) is established by using the neural network method based on the backscattering coefficient data of the HY-2A scatterometer combined with the wind speed data of the Special Sensor Microwave Imager (SSM/I) and the Final (FNL) operational global analysis wind direction data from the National Centers for Environmental Prediction (NCEP). The absolute value of the mean deviation between the predicted sigma(0) by the HW-GMF and the measured sigma(0) by the HY-2A scatterometer is less than 0.1 dB, indicating that the HW-GMF has high accuracy. To verify the HW-GMF performance, the wind field inversion accuracy of the HW-GMF is compared with that of the NSCAT-2 GMF, a GMF currently used in the data processing of the HY-2A scatterometer. The experimental results show that the deviation between the HW-GMF retrieved wind speed and the SSM/I wind speed is within 2 m/s in the high wind speed range of 15-35 m/s, indicating that the HW-GMF improves the precision of the wind speed inversion of the HY-2A scatterometer under high wind speed conditions.

Automated summary

In order to improve the wind speed retrieval accuracy of the HY-2A scatterometer under high wind conditions, a new high wind GMF (HW-GMF) is established using a neural network method. Experimental results show that the HW-GMF significantly improves the precision of wind speed inversion under high wind speed conditions.