A Visual Analysis Approach for Deep Learning-based Precipitation Forecasting

Meteorological experts achieved promising results in improving the effectiveness of neural weather networks in precipitation forecasting by incorporating precipitation-optimized objectives into the loss function. However, since neural networks are not based on explicit physical processes, meteorological experts may lack understanding and trust in the model’s predictions, and they cannot perform bias correction by adjusting physical parameters. Additionally, precipitation events are highly imbalanced, with heavy rainfall events being relatively rare but of greater importance. Therefore, traditional metrics for evaluating deep models are insufficient to fully assess precipitation forecasting performance. In this paper, we present a neural weather network visual analysis system designed to help domain experts understand and comprehensively compare the impact of different loss functions on neural networks. We customize the model evaluation process based on the characteristics of precipitation forecasting tasks and provide a reference for bias correction using historically similar data. To validate our approach, we perform two case studies using real-world reanalysis datasets, with feedback from domain experts further confirming its effectiveness.