Accurate and timely rain prediction is crucial for decision making and is also a challenging task. This paper presents a solution which won the 2 nd prize in the Weather4cast 2022 NeurIPS competition using 3D U-Nets and EarthFormers for 8-hour probabilistic rain prediction based on multi-band satellite images. The spatial context effect of the input satellite image has been deeply explored and optimal context range has been found. Based on the imbalanced rain distribution, we trained multiple models with different loss functions. To further improve the model performance, multi-model ensemble and threshold optimization were used to produce the final probabilistic rain prediction. Experiment results and leaderboard scores demonstrate that optimal spatial context, combined loss function, multi-model ensemble, and threshold optimization all provide modest model gain. A permutation test was used to analyze the effect of each satellite band on rain prediction, and results show that satellite bands signifying cloudtop phase (8.7 um) and cloud-top height (10.8 and 13.4 um) are the best predictors for rain prediction. The source code is available at https://github.com/bugsuse/weather4cast-2022-stage2.
翻译:准确而及时的降雨预测对于决策至关重要,也是一项具有挑战性的任务。本文件展示了在利用3D U-Nets和EarthFormermers 3D U-Nets 和EarthFormets 的天气4402022 NeurIPS 竞赛中获得2个奖项的解决方案,根据多波段卫星图像进行了8小时的概率性降雨预测。对输入的卫星图像的空间环境效应进行了深入的探索,并找到了最佳的背景范围。根据降雨分布不平衡的情况,我们培训了具有不同损失功能的多个模型。为了进一步改进模型性能,使用了多模型的共和阈值优化来生成最终的概率性降雨预测。实验结果和头板评分表明,最佳的空间环境环境、合并损失功能、多模型的共振荡和阈值优化都提供了微小的模型收益。使用了一个透析测试来分析每个卫星波段对降雨预测的影响,结果显示,表示云顶级(8.7微米)和云顶高度(10.8和13.4微米)的卫星波段是最佳的预测器。源代码可在 http://gitubbubbsyard/sirstyard2.