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
this https URL

通过使用 2D U-Net 模型进行空时特征学习，通过重要性采样和数据集准备，以及研究交叉熵损失函数、条件时间领先和上采样方法，提升天气预报准确性。