Improving Ocean Forecasting Using Deep Learning and Numerical Model Integration
Youngjin Choi, Youngmin Park, Jaedong Hwang, Kijune Jeong, Euihyun Kim
Abstract
In this paper, we propose a novel method to enhance the accuracy of a real-time ocean forecasting system. The proposed system consists of a real-time restoration system of satellite ocean temperature based on a deep generative inpainting network (GIN) and assimilation of satellite data with the initial fields of the numerical ocean model. The deep learning real-time ocean forecasting system is as fast as conventional forecasting systems, while also showing enhanced performance. Our results showed that the difference in temperature between in situ observation and actual forecasting results was improved by about 0.5 °C in daily average values in the open sea, which suggests that cutting back the temporal gaps between data assimilation and forecasting enhances the accuracy of the forecasting system in the open ocean. The proposed approach can provide more accurate forecasts with an efficient operation time.