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 this https URL.

使用 3D U-Nets 和 EarthFormers 基于多波段卫星图像对未来 8 小时的降雨进行了概率预测，并通过多模型协商和阈值优化获得了最终结果，最佳的卫星波段预测雨情的表征是云顶相位和云顶高度波段，是一项优化的模型方法。