Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression
For operational weather forecasting, this offers a simple drop-in replacement for standard loss functions that improves both deterministic accuracy and risk-sensitive predictions without architectural changes.
This study shows that training a deterministic precipitation nowcasting model (SmaAt-UNet) with multi-quantile regression reduces test MSE by 8.6% compared to standard MSE training, while also providing useful upper-quantile outputs for heavy rainfall prediction.
Deep-learning precipitation nowcasting models are often optimized using pointwise losses such as mean squared error or mean absolute error, which can lead to overly smooth forecasts and poor representation of heavy rainfall. This study investigates whether the predictive performance of an established deterministic nowcasting architecture can be improved by reformulating training as a multi-quantile regression problem. Using SmaAt-UNet as a core model, we compare MSE, MAE, and multi-quantile pinball-loss training on radar precipitation nowcasting over the Netherlands. The results show that multi-quantile training improves the central deterministic forecast, decreasing test-set MSE by 8.6\% compared to a model trained using MSE, while also producing upper-quantile outputs that are useful for risk-sensitive prediction of heavy precipitation. These findings suggest that quantile regression provides a simple alternative to standard pointwise losses without requiring a new architecture or generative sampling procedure. The implementation of our models and training setup is available on \href{https://github.com/gijsvn/Multi-Quantile-Precipitation-Nowcasting}{GitHub}.