Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression

2026-05-28 · Source: Machine Learning · Field: Science & Research — Environmental Science & Earth Systems, Artificial Intelligence & Machine Learning, Research Methodology & Innovation · Depth: Advanced, quick

Summary

A recent study demonstrates that multi-quantile regression significantly enhances precipitation nowcasting models, addressing the common issue of overly smooth forecasts produced by traditional pointwise losses like mean squared error (MSE) or mean absolute error (MAE). Utilizing the SmaAt-UNet architecture, researchers compared training with MSE, MAE, and a multi-quantile pinball loss for radar precipitation nowcasting over the Netherlands. The findings indicate that multi-quantile training improved the central deterministic forecast, achieving an 8.6% reduction in test-set MSE compared to an MSE-trained model. Furthermore, this approach generated valuable upper-quantile outputs, which are particularly useful for risk-sensitive predictions of heavy rainfall events. This method offers a straightforward alternative to standard pointwise losses without necessitating new architectures or complex generative sampling procedures.

Key takeaway

For Machine Learning Engineers developing precipitation nowcasting models, you should consider adopting multi-quantile regression to overcome the limitations of traditional pointwise losses. This approach not only improves deterministic forecast accuracy, demonstrated by an 8.6% MSE reduction, but also provides critical upper-quantile outputs for more reliable heavy rainfall risk assessment. Integrate pinball loss training into your existing architectures to enhance both forecast precision and risk-sensitive prediction capabilities.

Key insights

Multi-quantile regression improves precipitation nowcasting by reducing MSE and providing risk-sensitive heavy rainfall predictions.

Principles

• Pointwise losses yield smooth forecasts.
• Quantile regression improves deterministic forecasts.
• Upper quantiles aid heavy precipitation risk.

Method

Reformulate nowcasting training as multi-quantile regression using pinball loss, comparing against MSE and MAE on an established architecture like SmaAt-UNet.

In practice

• Apply multi-quantile training to nowcasting.
• Use upper quantiles for heavy rain alerts.
• Replace MSE/MAE with pinball loss.

Topics

• Precipitation Nowcasting
• Multi-Quantile Regression
• SmaAt-UNet
• Pinball Loss
• Radar Meteorology
• Heavy Rainfall Prediction

Code references

• gijsvn/Multi-Quantile-Precipitation-Nowcasting

Best for: AI Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

Related on AIssential

• MambaRain: Multi-Scale Mamba-Attention Framework for 0-3 Hour Precipitation Nowcasting — MambaRain integrates Mamba and self-attention for improved 0-3 hour precipitation nowcasting, extending prediction horizons.
• Training ML Models with Predictable Failures — Extrapolating from top-k evaluation failures can predict deployment-scale ML model failure rates.
• Ensemble High-Resolution Weather Forecasting on AMD Instinct GPU Accelerators — Diffusion models enable robust ensemble weather forecasting by generating diverse atmospheric states to quantify uncertainty.
• The digital quant: instant portfolio optimization with JointFM — JointFM is a zero-shot foundation model predicting full multivariate distributions for instant, domain-agnostic time-series forecasting.
• HaorFloodAlert: Deseasonalized ML Ensemble for 72-Hour Flood Prediction in Bangladesh Haor Wetlands — Deseasonalized ML ensembles and SAR proxies accurately predict flash floods in complex, flat wetland environments.
• MSMixer: Learned Multi-Scale Temporal Mixing with Complementary Linear Shortcut for Long-Term Time Series Forecasting — Multi-scale MLP architectures with dynamic weighting and linear shortcuts improve long-term time series forecasting.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.