FrontFinder AI：利用UNET3+模型架构高效识别美国本土及NOAA统一地表分析域内的锋面边界

发布时间：2025-01-24 打印

FrontFinder AI: Efficient Identification of Frontal Boundaries over the Continental United States and NOAA’s Unified Surface Analysis Domain using the UNET3+ Model Architecture

　　FrontFinder AI is a novel machine learning algorithm trained to detect cold, warm, stationary, and occluded fronts and drylines. Fronts are associated with many high-impact weather events around the globe. Frontal analysis is still primarily done by human forecasters, often implementing their own rules and criteria for determining front positions. Such techniques result in multiple solutions by different forecasters when given identical sets of data. Numerous studies have attempted to automate frontal analysis through numerical frontal analysis. In recent years, machine learning algorithms have gained more popularity in meteorology due to their ability to learn complex relationships. Our algorithm was able to reproduce three-quarters of forecaster-drawn fronts over CONUS and NOAA’s Unified Surface Analysis domain on independent testing datasets. We applied permutation studies, an Explainable Artificial Intelligence method, to identify the importance of each variable for each front type. The permutation studies showed that the most “important” variables for detecting fronts are consistent with observed processes in the evolution of frontal boundaries. We applied the model to an extratropical cyclone over the Central US to see how the model handles the occlusion process, with results showing that the model can resolve the early stages of occluded fronts wrapping around cyclone centers. While our algorithm is not intended to replace human forecasters, the model can streamline operational workflows by providing efficient frontal boundary identification guidance. FrontFinder has been deployed operationally at NOAA’s Weather Prediction Center.

　　FrontFinder AI是一种新颖的机器学习算法，经过训练能够监测冷锋、暖锋、静止锋、锢囚锋以及干线。锋面与众多高影响天气事件相关。目前，锋面分析仍主要由预报员完成，预报员根据自己的经验和标准来确定锋面的位置，由此往往导致不同预报员在处理相同数据时可能会得出不同的预报方案。许多研究尝试通过数值锋面分析来实现锋面分析的自动化。近年来，由于机器学习算法具有学习复杂关系的能力，因此在气象学中越来越受欢迎。本研究基于机器学习算法设计了一种新模式，经与预报员绘制的锋面进行比较，新模式绘制的美国本土域内，以及NOAA统一地表分析域内的锋面，重现率可达75%以上。

UNET3+模型的架构用于预测冷锋、暖锋、静止锋、锢囚锋和干线

　　研究人员采用“排列研究法”（permutation studies），来识别每种锋面类型中各个变量的重要性。排列研究表明，（新算法用于监测）锋面时“最重要”的变量与锋面边界演变过程中观测到的过程是一致的。研究人员用新模式监测美国中部一个温带气旋的发展，以观察模式如何处理锢囚过程，结果表明，该模式能够解析锢囚锋围绕气旋中心缠绕的早期阶段。

美国本土分析域（USAD）上，冷锋的排列研究结果分为：a) 分组变量，b) 分组垂直层次，以及 c) 单一层次上的变量，按重要性从1到60排序，其中1（60）表示最重要（最不重要）的变量和层次组合。

　　研究人员强调，FrontFinder并非旨在取代人工预报员，但该模式可以通过提供高效的锋面边界识别指导来简化业务流程。目前，FrontFinder已在NOAA的天气预测中心投入业务运行。