Uncertainty quantification via conformal prediction in data assimilation
Quantifying the evolution of uncertainty is critical to both probabilistic forecasting and data assimilation in numerical weather prediction. In this study, we investigate the applicability of conformal prediction (CP), a recent machine learning (ML) method, to quantify uncertainty in a controlled, idealized setting. We use the one dimensional modified shallow water model, designed to mimic the convective process. CP provides a set of possible outcomes with a chosen confidence level. Here, we compare and evaluate the average empirical coverage, the average interval length, miss low, miss high and average interval score loss (AISL) for three variants of CP, namely a) Standard CP, b) Normal...
| Authors | Catherine George, Alireza Javanmardi, Tijana Janjić et al. |
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| Primary category | cs.LG |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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