Precipitation Prediction Using an Ensemble of Lightweight Learners (2401.09424v1)
Abstract: Precipitation prediction plays a crucial role in modern agriculture and industry. However, it poses significant challenges due to the diverse patterns and dynamics in time and space, as well as the scarcity of high precipitation events. To address this challenge, we propose an ensemble learning framework that leverages multiple learners to capture the diverse patterns of precipitation distribution. Specifically, the framework consists of a precipitation predictor with multiple lightweight heads (learners) and a controller that combines the outputs from these heads. The learners and the controller are separately optimized with a proposed 3-stage training scheme. By utilizing provided satellite images, the proposed approach can effectively model the intricate rainfall patterns, especially for high precipitation events. It achieved 1st place on the core test as well as the nowcasting leaderboards of the Weather4Cast 2023 competition. For detailed implementation, please refer to our GitHub repository at: https://github.com/lxz1217/weather4cast-2023-lxz.
- Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast. CoRR, abs/2211.02556, 2022.
- Disentangling physical dynamics from unknown factors for unsupervised video prediction. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, pages 11471–11481. Computer Vision Foundation / IEEE, 2020.
- The era5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730):1999–2049, 2020.
- Evaluation of the era5 reanalysis precipitation dataset over chinese mainland. Journal of Hydrology, 595:125660, 2021.
- Fourcastnet: Accelerating global high-resolution weather forecasting using adaptive fourier neural operators. In Axel Huebl, Cristina Silvano, and Timothy Robinson, editors, Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2023, Davos, Switzerland, June 26-28, 2023, pages 13:1–13:11. ACM, 2023.
- Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. In Yike Guo and Faisal Farooq, editors, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19-23, 2018, pages 1930–1939. ACM, 2018.
- Weatherfusionnet: Predicting precipitation from satellite data. CoRR, abs/2211.16824, 2022.
- Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017.
- Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, editors, Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 802–810, 2015.
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