arXiv:2605.30122v1 Announce Type: new 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 model, we compare MSE, MAE, and multi-quantile pinball-loss training on radar precipitation nowcasting
The continuous advancement in deep learning techniques and computational resources allows for more sophisticated approaches to critical forecasting problems like precipitation nowcasting.
Improved precipitation nowcasting directly impacts areas from agriculture and disaster preparedness to water resource management and urban planning, reducing economic losses and saving lives.
The shift from pointwise loss functions to multi-quantile regression in deep learning models offers more robust and less 'smooth' forecasts, better capturing extreme weather events.
- · Meteorological agencies
- · Agricultural sector
- · Insurance industry
- · Urban planning
- · Traditional forecasting methods
- · Sectors reliant on less accurate, 'smoothed' forecasts
More accurate short-term weather predictions mitigate risks associated with sudden weather changes.
Reduced economic impact from extreme weather events due to better preparation and resource allocation.
Enhanced resilience of infrastructure and societal systems against climate variability and change.
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Read at arXiv cs.LG