This paper presents a solution to the Weather4Cast 2023 competition, where the goal is to forecast high-resolution precipitation with an 8-hour lead time using lower-resolution satellite radiance images. We propose a simple, yet effective method for spatiotemporal feature learning using a 2D U-Net model, that outperforms the official 3D U-Net baseline in both performance and efficiency. We place emphasis on refining the dataset, through importance sampling and dataset preparation, and show that such techniques have a significant impact on performance. We further study an alternative cross-entropy loss function that improves performance over the standard mean squared error loss, while also enabling models to produce probabilistic outputs. Additional techniques are explored regarding the generation of predictions at different lead times, specifically through Conditioning Lead Time. Lastly, to generate high-resolution forecasts, we evaluate standard and learned upsampling methods. The code and trained parameters are available at https://github.com/rafapablos/w4c23-rainai.

翻译：本文提出了一种针对Weather4Cast 2023竞赛的解决方案，该竞赛的目标是使用低分辨率卫星辐射图像实现8小时提前期的高分辨率降水预报。我们提出了一种简单有效的时空特征学习方法，采用2D U-Net模型，在性能和效率上均优于官方3D U-Net基线方法。我们通过重要性采样和数据集预处理对数据集进行精炼，并证明这些技术对性能提升具有显著影响。我们进一步研究了一种替代的交叉熵损失函数，该函数在优于标准均方误差损失的同时，还能使模型生成概率输出。针对不同提前期的预测生成，我们还探索了其他技术，特别是通过条件提前期（Conditioning Lead Time）的方法。最后，为生成高分辨率预测，我们评估了标准上采样方法和学习型上采样方法。相关代码和训练参数已开源至https://github.com/rafapablos/w4c23-rainai。