[论文解读] Spatiotemporal Detection and Uncertainty Visualization of Atmospheric Blocking Events

Mingzhe Li, Peer Nowack+1 more|arXiv (Cornell University)|Jan 2, 2026

Climate variability and models被引用 0

一句话总结

本文提出一个基于几何框架的检测与跟踪大气阻塞事件的方法，并引入不确定性感知的可视化（等高线箱线图、频率热图和3D时间堆栈），应用于 ERA5 和 UKESM 数据，并与基线指数进行评估。

ABSTRACT

Atmospheric blocking events are quasi-stationary high-pressure systems that disrupt the typical paths of polar and subtropical air currents, often producing prolonged extreme weather events such as summer heat waves or winter cold spells. Despite their critical role in shaping mid-latitude weather, accurately modeling and analyzing blocking events in long meteorological records remains a significant challenge. To address this challenge, we present an uncertainty visualization framework for detecting and characterizing atmospheric blocking events. First, we introduce a geometry-based detection and tracking method, evaluated on both pre-industrial climate model simulations (UKESM) and reanalysis data (ERA5), which represent historical Earth observations assimilated from satellite and station measurements onto regular numerical grids using weather models. Second, we propose a suite of uncertainty-aware summaries: contour boxplots that capture representative boundaries and their variability, frequency heatmaps that encode occurrences, and 3D temporal stacks that situate these patterns in time. Third, we demonstrate our framework in a case study of the 2003 European heatwave, mapping the spatiotemporal occurrences of blocking events using these summaries. Collectively, these uncertainty visualizations reveal where blocking events are most likely to occur and how their spatial footprints evolve over time. We envision our framework as a valuable tool for climate scientists and meteorologists: by analyzing how blocking frequency, duration, and intensity vary across regions and climate scenarios, it supports both the study of historical blocking events and the assessment of scenario-dependent climate risks associated with changes in extreme weather linked to blocking.

研究动机与目标

• 在大地势高度场中开发一个基于几何的管道以检测和跟踪高压阻塞系统。
• 提供对阻塞事件在时空尺度上的不确定性感知摘要（等高线箱线图、频率热图、3D堆栈）的表示。
• 针对已建立的阻塞指数和地面真实数据评估检测性能。
• 通过2003年欧洲热浪的案例研究演示该框架，并讨论对气候风险分析的意义。

提出的方法

• 对500 hPa 的日地势高度异常进行计算，并通过去趋势和结合局部季节性变异性标准化形成 Z500_norm。
• 将高压系统作为超出阈值集合的连通分量进行跟踪，并以经度权重重叠形成跨日的轨迹。
• 将阻塞事件定义为轨迹至少持续五天的时间段，允许合并/分裂而不强制唯一对应。
• 用等高线箱线图（中位数、50%与100%包络）和频率热图对阻塞足迹的集合进行摘要，并用3D中位数和频率堆栈编码时序演化。
• 使用地面真实的 ERA5 和 UKESM 数据来调整阈值（lambda, C）并评估相对于基线指数 DG83 与 SOM-BI 的性能。
• 提供实现（Python）和可视化管道，并使用 ParaView 进行3D/体绘制。

Figure 1 : An example of contour boxplot. Left: an ensemble of nine contours generated by sine waves shifted horizontally and vertically. Right: a contour boxplot showing the median contour (purple), the $50\%$ central envelope (the inner band in gray), and the $100\%$ envelope (the outer band in li

实验结果

研究问题

• RQ1 基于几何的跟踪方法是否能在检测与跟踪阻塞事件方面优于现有阻塞指数？
• RQ2 阻塞的频率、持续时间和空间足迹在不同区域和气候情景（ERA5 与 UKESM）下如何变化？
• RQ3 哪种不确定性可视化能最好地传达阻塞事件的典型几何形状、变异性和时间演化？
• RQ4 提出的事件中心表示如何支持对历史阻塞及情景相关气候风险的分析？

主要发现

• 基于几何的检测在对 ERA5 和 UKESM 数据集的专家标注地面真值进行评估时，在检测准确度和F1-score方面优于基线指数 DG83 与 SOM-BI。
• 该方法给出明确的事件足迹（边界、面积、生命周期、轨迹），而非仅提供模式标签，便于进行事件中心分析。
• 等高线箱线图、频率热图和3D时间堆栈揭示了典型阻塞的定位、空间扩展和随季节的演化，跨集合的特征。
• 该框架支持考察阻塞的频率、持续时间和强度如何在区域和气候情景中变化，便于气候风险评估。
• 2003年欧洲热浪案例研究展示了不确定性可视化在理解时空阻塞模式方面的实用性。

Figure 2 : ERA5 dataset: the long-term daily mean (blue) of the geopotential height (Zg, unit: meter or m) at 59.375 $\degree$ N, 160.3125 $\degree$ W and its smoothed curve (yellow) by keeping the first six Fourier harmonics.