Probability calibration for precipitation nowcasting

• URL: http://arxiv.org/abs/2510.00594v1
• Date: Wed, 01 Oct 2025 07:21:05 GMT
• Title: Probability calibration for precipitation nowcasting
• Authors: Lauri Kurki, Yaniel Cabrera, Samu Karanko,
• Abstract summary: We introduce the expected thresholded calibration error (ETCE), a new metric that better captures miscalibration in ordered classes like precipitation amounts.<n>Our results show that selective scaling with lead time conditioning reduces model miscalibration without reducing the forecast quality.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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