Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression
📰 ArXiv cs.AI
Improve precipitation nowcasting with multi-quantile regression to better capture heavy rainfall, moving beyond traditional mean squared error (MSE) optimization
Action Steps
- Implement multi-quantile regression in your nowcasting model using SmaAt-UNet as the core architecture
- Train the model on a dataset with diverse precipitation patterns to capture a wide range of quantiles
- Evaluate the model's performance using metrics that account for heavy rainfall events, such as the mean absolute error of the 95th percentile
- Compare the results with traditional MSE-optimized models to assess the improvement in predictive performance
- Refine the model by adjusting the quantile levels and loss functions to optimize for specific precipitation forecasting tasks
Who Needs to Know This
Data scientists and researchers working on precipitation nowcasting models can benefit from this approach to improve forecast accuracy, particularly in capturing extreme weather events
Key Insight
💡 Multi-quantile regression can improve the predictive performance of precipitation nowcasting models by capturing a wider range of precipitation intensities, particularly heavy rainfall
Share This
Boost precipitation nowcasting accuracy with multi-quantile regression! Move beyond MSE and capture heavy rainfall events more effectively #nowcasting #precipitation #ml
Key Takeaways
Improve precipitation nowcasting with multi-quantile regression to better capture heavy rainfall, moving beyond traditional mean squared error (MSE) optimization
Full Article
Title: Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression
Abstract:
arXiv:2605.30122v1 Announce Type: cross Abstract: 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 m
Abstract:
arXiv:2605.30122v1 Announce Type: cross Abstract: 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 m
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