Precipitation Prediction Using an Ensemble of Lightweight Learners
Xinzhe Li Sun Rui Yiming Niu Yao Liu
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:
https://github.com/lxz1217/weather4cast-2023-lxz.