Evaporation Duct Height Nowcasting in China’s Yellow Sea Based on Deep Learning

Jie Han, Jiaji Wu, Qinglin Zhu, Hongguang Wang, Yufeng Zhou, Mingbo Jiang, Shoubao Zhang, Bo Wang

Remote Sensing · 19 April 2021

In one sentence. Deep learning nowcasts evaporation duct height in the Yellow Sea at 5-minute resolution for 0–2 h, outperforming traditional time-series methods.

Abstract

The evaporation duct is a weather phenomenon that often occurs in marine environments and affects the operation of shipborne radar. The most important evaluation parameter is the evaporation duct height (EDH). Forecasting the EDH and adjusting the working parameters and modes of the radar system in advance can greatly improve radar performance. Traditionally, short-term forecast methods have been used to estimate the EDH, which are characterized by low time resolution and poor forecast accuracy. In this study, a novel approach for EDH nowcasting is proposed based on the deep learning network and EDH data measured in the Yellow Sea, China. The factors that affect nowcasting were analyzed. The time resolution and forecast time were 5 min and 0–2 h, respectively. The results show that our proposed method has a higher forecast accuracy than traditional time series forecasting methods and confirm its feasibility and effectiveness.

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