Forecasting of Typhoon-Induced Wind-Wave by Using Convolutional Deep Learning on Fused Data of Remote Sensing and Ground Measurements.

Chih-Chiang Wei,Hao-Chun Chang

doi:10.3390/s21155234

Abstract

Taiwan is an island, and its economic activities are primarily dependent on maritime transport and international trade. However, Taiwan is also located in the region of typhoon development in the Northwestern Pacific Basin. Thus, it frequently receives strong winds and large waves brought by typhoons, which pose a considerable threat to port operations. To determine the real-time status of winds and waves brought by typhoons near the coasts of major ports in Taiwan, this study developed models for predicting the wind speed and wave height near the coasts of ports during typhoon periods. The forecasting horizons range from 1 to 6 h. In this study, the gated recurrent unit (GRU) neural networks and convolutional neural networks (CNNs) were combined and adopted to formulate the typhoon-induced wind and wave height prediction models. This work designed two wind speed prediction models (WIND-1 and WIND-2) and four wave height prediction models (WAVE-1 to WAVE-4), which are based on the WIND-1 and WIND-2 model outcomes. The Longdong and Liuqiu Buoys were the experiment locations. The observatory data from the ground stations and buoys, as well as radar reflectivity images, were adopted. The results indicated that, first, WIND-2 has a superior wind speed prediction performance to WIND-1, where WIND-2 can be used to identify the temporal and spatial changes in wind speeds using ground station data and reflectivity images. Second, WAVE-4 has the optimal wave height prediction performance, followed by WAVE-3, WAVE-2, and WAVE-1. The results of WAVE-4 revealed using the designed models with in-situ and reflectivity data directly yielded optimal predictions of the wind-based wave heights. Overall, the results indicated that the presented combination models were able to extract the spatial image features using multiple convolutional and pooling layers and provide useful information from time-series data using the GRU memory cell units. Overall, the presented models could exhibit promising results.

Highlights

Taiwan, located at 120◦ –122◦ E and 22◦ –25◦ N, experiences subtropical weather, is an island with over 95% of its commodity trade being conducted through sea transport (TIPC 2020), and boasts two major international ports (Figure 1)—namely, Keelung and Kaohsiung Ports
The results of this study indicated that WAVE-1 and WAVE-4 could determine the wave height trends at Longdong Buoy and Liuqiu Buoy for a lead time of 1 h
According to the wind and wave classifications provided by the Central Weather Bureau (CWB) of Taiwan [62], winds can be classified into three categories: winds with a velocity less than 8.0 m/s were low winds, those with a velocity between 8.0 m/s and

Summary

Introduction

Taiwan, located at 120◦ –122◦ E and 22◦ –25◦ N, experiences subtropical weather, is an island with over 95% of its commodity trade being conducted through sea transport (TIPC 2020), and boasts two major international ports (Figure 1)—namely, Keelung and Kaohsiung Ports. Keelung Port is located on the north coast of Taiwan, with the Northeast. Kaohsiung Port is located on the southwest coast of Taiwan. This port is the largest port in Taiwan and the 15th largest container port worldwide. Taiwan is influenced by monsoons and the oceanic climate and often experiences severe weather. Such weather has a considerable influence on port operations

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