RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts

• URL: http://arxiv.org/abs/2212.04005v1
• Date: Wed, 7 Dec 2022 23:42:39 GMT
• Title: RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts
• Authors: Jinyoung Park, Minseok Son, Seungju Cho, Inyoung Lee, Changick Kim
• Abstract summary: This paper presents a solution to the Weather4cast 2022 Challenge Stage 2. The goal of the challenge is to forecast future high-resolution rainfall events obtained from ground radar. We suggest a solution that performs data preprocessing appropriate to the challenge and then predicts rainfall movies using a novel RainUNet.
• Score: 22.972610820962625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a solution to the Weather4cast 2022 Challenge Stage 2. The goal of the challenge is to forecast future high-resolution rainfall events obtained from ground radar using low-resolution multiband satellite images. We suggest a solution that performs data preprocessing appropriate to the challenge and then predicts rainfall movies using a novel RainUNet. RainUNet is a hierarchical U-shaped network with temporal-wise separable block (TS block) using a decoupled large kernel 3D convolution to improve the prediction performance. Various evaluation metrics show that our solution is effective compared to the baseline method. The source codes are available at https://github.com/jinyxp/Weather4cast-2022

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