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:
this https URL

通过使用多个学习者的集成学习框架，结合卫星图像，该研究论文提出了一种准确建模复杂降水模式的降水预测方法，并在 Weather4Cast 2023 竞赛中排名第一。

轻量级学习器集成的降水预测

Precipitation Prediction Using an Ensemble of Lightweight Learners

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。