用集合数据和机器学习提升雷暴预报准确率
Increasing NWP Thunderstorm Predictability Using Ensemble Data and Machine Learning
- 用神经网络分析集合气象预报数据，识别雷暴发生
- 11小时集合预报效果相当于5小时确定性预报
- 模型可发现更长提前期的可预报模式，适合气象预警
尽管数值天气预报（NWP）模型对提前数小时预测雷暴至关重要，但其不确定性随预报时效增加，限制了雷暴预报的可预测性。本研究探讨如何利用集合NWP数据与机器学习（ML）提升雷暴预报技能。基于我们新提出的1维神经网络模型SALAMA 1D，该模型用于分析中欧区域对流允许尺度的ICON-D2-EPS集合预报，结果表明集合平均显著提升了预报性能。值得注意的是，11小时集合预报的技能水平相当于5小时确定性预报。为解释这一改进，我们推导出一个解析表达式，将技能差异与集合成员间的相关性关联起来，与观测性能提升一致。该表达式适用于任何独立处理集合成员的二元分类模型。此外，我们证明，如SALAMA 1D等机器学习模型能识别出比原始NWP输出更长时间可预测的雷暴发生模式。研究结果定量解释了集合平均的优势，鼓励开发面向雷暴及其他气象现象的机器学习预报方法。
原文摘要 · Abstract (English)
While numerical weather prediction (NWP) models are essential for forecasting thunderstorms hours in advance, NWP uncertainty, which increases with lead time, limits the predictability of thunderstorm occurrence. This study investigates how ensemble NWP data and machine learning (ML) can enhance the skill of thunderstorm forecasts. Using our recently introduced neural network model, SALAMA 1D, which identifies thunderstorm occurrence in operational forecasts of the convection-permitting ICON-D2-EPS model for Central Europe, we demonstrate that ensemble-averaging significantly improves forecast skill. Notably, an 11-hour ensemble forecast matches the skill level of a 5-hour deterministic forecast. To explain this improvement, we derive an analytic expression linking skill differences to correlations between ensemble members, which aligns with observed performance gains. This expression generalizes to any binary classification model that processes ensemble members individually. Additionally, we show that ML models like SALAMA 1D can identify patterns of thunderstorm occurrence which remain predictable for longer lead times compared to raw NWP output. Our findings quantitatively explain the benefits of ensemble-averaging and encourage the development of ML methods for thunderstorm forecasting and beyond.
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