用神经天气模型+深度学习，提升雷暴大风提前3天的预测精度。
Improving Predictions of Convective Storm Wind Gusts through Statistical Post-Processing of Neural Weather Models
- 用卷积神经网络对气象环境图进行后处理，优化风速预测。
- 在瑞士5个区域验证，极端风速预测性能优于直接建模方法。
- 适合气象预警系统研发者，尤其关注极端天气早期响应场景。
及时发布强天气预警有助于减轻潜在灾害。近年来，神经天气模型（NWMs）以0.25°全球网格提供高效快速的大气环境预报。针对雷暴，可通过经验后处理方法预测特定位置的风速分布。基于Pangu-Weather NWM，我们采用一系列统计与深度学习后处理方法，对未来3天内每小时风速阵风进行预报。为保证概率预报的统计稳健性，我们在瑞士五个区域使用广义极值分布进行约束。利用卷积神经网络对预测大气环境的空间模式进行后处理，表现最佳，在不同预报时效和风速范围内均优于直接建模方法。结果表明，神经天气模型在极端风速预报中具有显著价值，尤其适用于设计更灵敏的早期预警系统。
原文摘要 · Abstract (English)
Issuing timely severe weather warnings helps mitigate potentially disastrous consequences. Recent advancements in Neural Weather Models (NWMs) offer a computationally inexpensive and fast approach for forecasting atmospheric environments on a 0.25° global grid. For thunderstorms, these environments can be empirically post-processed to predict wind gust distributions at specific locations. With the Pangu-Weather NWM, we apply a hierarchy of statistical and deep learning post-processing methods to forecast hourly wind gusts up to three days ahead. To ensure statistical robustness, we constrain our probabilistic forecasts using generalised extreme-value distributions across five regions in Switzerland. Using a convolutional neural network to post-process the predicted atmospheric environment's spatial patterns yields the best results, outperforming direct forecasting approaches across lead times and wind gust speeds. Our results confirm the added value of NWMs for extreme wind forecasting, especially for designing more responsive early-warning systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。