Improved Calibration of Wind Estimates from Advanced Scatterometer MetOp-B in Korean Seas Using Deep Neural Network

Abstract

Satellite-based observations of sea wind are useful for forecasting marine weather and performing marine disaster management. Meteorological Operational Satellite-B (MetOp-B) is one of the satellites that provide wind products through a scatterometer named the Advanced Scatterometer (ASCAT). Since the linear regression method has been conventionally employed for calibrating remotely-sensed wind data, deviations and biases remain un-resolved to some degree. For coastal applications, these remotely-sensed wind data need to be calibrated again using local in-situ measurements in order to provide more accurate and realistic information. Thus, this study proposed a new method to calibrate ASCAT-based wind speed estimates using artificial neural networks. Herein, a deep neural network (DNN) model was applied. Wind databases collected during a period from 2012 to 2019 by the MetOp-B ASCAT and ten buoy stations in Korean seas were considered for deep learning-based calibration. ASCAT-based wind data and in-situ measurements were collocated in space and time. They were then separated into training and validation sets. A DNN model was designed and trained using multiple input variables such as observation location, sensing date and time, wind speed, and wind direction of the training set. The DNN-based best fit calibration model was evaluated using the validation set. The mean of biases between ASCAT-based and in-situ wind speeds was found to be decreased from 0.41 to 0.05 m/s on average for all buoy locations. The root mean squared error (RMSE) of wind speed was reduced from 1.38 m/s to 0.93 m/s. Moreover, the DNN-based calibration considerably improved the quality of wind speeds of less than 4 m/s, and of high wind speeds of 11–25 m/s. These results suggest that ASCAT-based observations can accurately represent real wind fields if a DNN-based calibration approach is applied.