Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression

Researchers demonstrate that multi-quantile regression training improves deep learning precipitation forecasting models compared to traditional mean squared error optimization. The approach reduces forecast smoothing, better captures extreme rainfall events, and achieves 8.6% lower test error while providing probabilistic outputs without requiring new architectures.

This research addresses a fundamental challenge in weather prediction: standard loss functions like MSE optimize for average accuracy but fail to capture the distributional properties of extreme weather events. By reformulating the training objective as multi-quantile regression using pinball loss, the team shows that deterministic nowcasting models can simultaneously improve central forecasts and generate meaningful uncertainty estimates across the precipitation distribution.

The advancement emerges from broader recognition that weather prediction requires probabilistic rather than purely deterministic outputs. Traditional approaches optimize for mean error, which inadvertently smooths forecasts and underestimates rare but high-impact rainfall events. Multi-quantile regression directly addresses this by training models to predict specific quantiles of the precipitation distribution, allowing the same architecture to learn both central tendencies and tail behavior.

This approach carries practical implications for risk-sensitive applications including flood forecasting, water resource management, and emergency response planning. Meteorological services and climate adaptation organizations require not just point forecasts but calibrated uncertainty estimates to support decision-making. The 8.6% MSE improvement demonstrates measurable performance gains without architectural innovations, making adoption feasible for existing operational systems.

The research's significance lies in showing that loss function design can substantially improve weather model performance with minimal additional computational overhead. The open-source implementation enables rapid adoption across meteorological research and operational forecasting centers. Future work likely explores combining this approach with ensemble methods and generative models for even more robust probabilistic predictions.

• →Multi-quantile regression improves precipitation nowcasting accuracy by 8.6% MSE reduction compared to MSE training alone.
• →Quantile-based training enables single models to produce both deterministic forecasts and probabilistic uncertainty estimates for extreme events.
• →The method requires no new architecture or generative sampling, making it practical for existing operational weather prediction systems.
• →Risk-sensitive applications like flood forecasting gain access to calibrated upper-quantile predictions for better decision-making under uncertainty.
• →Open-source implementation availability accelerates adoption across meteorological organizations and research institutions.