Short-term forecasting of typhoon rainfall with a deep-learning-based disaster monitoring model

Doyi Kim, Yeji Choi, Minseok Seo, Seungheon Shin, Hyun-Jin Jeong

Environmental Data Science · 01 January 2023

In one sentence. Two-stage deep learning predicts typhoon satellite images and converts them to rainfall maps, reaching correlation 0.81 at 3 hours ahead.

Abstract

Abstract Accurate and reliable disaster forecasting is vital for saving lives and property. Hence, effective disaster management is necessary to reduce the impact of natural disasters and to accelerate recovery and reconstruction. Typhoons are one of the major disasters related to heavy rainfall in Korea. As a typhoon develops in the far ocean, satellite observations are the only means to monitor them. Our study uses satellite observations to propose a deep-learning-based disaster monitoring model for short-term typhoon rainfall forecasting. For this, we consider two deep learning models: a video frame prediction model, Warp and Refine Network (WR-Net), to predict future satellite observations and an image-to-image translation model, geostationary rainfall product (GeorAIn) (based on the Pix2PixCC model), to generate rainfall maps from predicted satellite images. Typhoon Hinnamnor, the worst typhoon case in 2022 in Korea, is selected as a target case for model verification. The results show that the predicted satellite images can capture the structures and patterns of the typhoon. The rainfall maps generated from the GeorAIn model using predicted satellite images show a correlation coefficient of 0.81 for 3-hr and 0.56 for 7-hr predictions. The proposed disaster monitoring model can provide us with practical implications for disaster alerting systems and can be extended to flood-monitoring systems.

Labels

Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.