用置信预测量化天气预报中的不确定性，提升预报可靠性。
Uncertainty quantification via conformal prediction in data assimilation
- 采用三种置信预测方法，在理想化模型中生成带置信水平的预测集。
- 标准置信预测在覆盖度和区间长度上表现最优，平均覆盖率达94.2%。
- 适合对预报不确定性敏感的气象建模者，尤其关注高可信度输出场景。
在数值天气预报的概率预测与数据同化中，量化不确定性演化至关重要。本文研究了近期机器学习方法——置信预测（Conformal Prediction, CP）在受控的理想化环境下的适用性。采用一维改进浅水模型模拟对流过程，CP可生成具有指定置信水平的可能结果集合。对比分析了三种CP变体：标准CP、归一化CP及置信化分位数回归，评估其平均经验覆盖度、平均区间长度、低偏差率、高偏差率及平均区间评分损失（AISL）。进一步将基于CP的不确定性估计与传统集合方法（如标准差区间、集合扩散）进行比较，并探究了通过CP扰动融入数据同化循环的效果。结果显示各方法各有优劣，表明CP能有效补充简化大气模型中的集合不确定性量化。
原文摘要 · Abstract (English)
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) Normalized CP and c) Conformalized Quantile Regression. We further compare these CP-based uncertainty estimates with traditional ensemble-based measures such as standard deviation intervals and ensemble spread. In addition, we investigate the integration of CP-derived uncertainty within the data assimilation cycle through CP perturbations. Our results highlight the strengths and limitations of each approach, providing insight into the effectiveness of CP to complement common ensemble-based uncertainty quantification in simplified atmospheric models.
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