Probability calibration for precipitation nowcasting
This work addresses the need for reliable precipitation nowcasting for weather-sensitive decision-making, but it is incremental as it extends existing techniques to a new domain.
The paper tackled the problem of poorly calibrated probabilistic forecasts from neural weather models for precipitation nowcasting by introducing the expected thresholded calibration error (ETCE) metric and applying post-processing techniques, resulting in reduced miscalibration without degrading forecast quality.
Reliable precipitation nowcasting is critical for weather-sensitive decision-making, yet neural weather models (NWMs) can produce poorly calibrated probabilistic forecasts. Standard calibration metrics such as the expected calibration error (ECE) fail to capture miscalibration across precipitation thresholds. We introduce the expected thresholded calibration error (ETCE), a new metric that better captures miscalibration in ordered classes like precipitation amounts. We extend post-processing techniques from computer vision to the forecasting domain. Our results show that selective scaling with lead time conditioning reduces model miscalibration without reducing the forecast quality.