Efficient Baseline for Quantitative Precipitation Forecasting in Weather4cast 2023
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Abstract (arXiv)
Accurate precipitation forecasting is indispensable for informed decision-making across various industries. However, the computational demands of current models raise environmental concerns. We address the critical need for accurate precipitation forecasting while considering the environmental impact of computational resources and propose a minimalist U-Net architecture to be used as a baseline for future weather forecasting initiatives.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 30 Nov 2023.
Builds on
7 of 12 references.
References · 7
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
- 2021XCiT: Cross-Covariance Image Transformerscited by 338
- 2022SimVP: Simpler yet Better Video Predictioncited by 184
- 2020SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecturecited by 71
- 2022RainUNet for Super-Resolution Rain Movie Prediction under Spatio-temporal Shiftscited by 3
- 2021Efficient spatio-temporal weather forecasting using U-Netcited by 1
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