RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts
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Abstract (arXiv)
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
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 7 Dec 2022.
Builds on
11 of 18 references.
References · 11
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- 2022Optimization Planning for 3D ConvNetscited by 5
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1.
3 papers.