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Spatiotemporal Detection and Uncertainty Visualization of Atmospheric Blocking Events

Mingzhe Li, Peer Nowack, Bei Wang

TL;DR

Atmospheric blocking events shape mid-latitude weather and are challenging to detect over long records. The authors propose a geometry-based tracking pipeline applied to normalized $Z^{\text{norm}}_{500}$ anomalies to produce explicit blocking footprints, paired with uncertainty-aware summaries (contour boxplots, frequency heatmaps, and 3D temporal stacks) for spatiotemporal analysis. Compared to baselines DG83 and SOM-BI, their method achieves higher F1-scores on ERA5 and UKESM data and provides event-centered representations suitable for historical analysis and scenario-based risk assessment. The approach is illustrated with a case study of the 2003 European heatwave, showing interpretable, regionally varying patterns and evolving footprints, with practical implications for understanding climate-change impacts on extreme weather.

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.

Spatiotemporal Detection and Uncertainty Visualization of Atmospheric Blocking Events

TL;DR

Atmospheric blocking events shape mid-latitude weather and are challenging to detect over long records. The authors propose a geometry-based tracking pipeline applied to normalized anomalies to produce explicit blocking footprints, paired with uncertainty-aware summaries (contour boxplots, frequency heatmaps, and 3D temporal stacks) for spatiotemporal analysis. Compared to baselines DG83 and SOM-BI, their method achieves higher F1-scores on ERA5 and UKESM data and provides event-centered representations suitable for historical analysis and scenario-based risk assessment. The approach is illustrated with a case study of the 2003 European heatwave, showing interpretable, regionally varying patterns and evolving footprints, with practical implications for understanding climate-change impacts on extreme weather.

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.
Paper Structure (19 sections, 1 equation, 10 figures, 5 tables)

This paper contains 19 sections, 1 equation, 10 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Technical Background: Contour Boxplot
  4. Datasets and Preprocessing
  5. Datasets
  6. Data Preprocessing
  7. Method
  8. Tracking High-Pressure Systems
  9. Detecting Blocking Events
  10. Uncertainty Visualization
  11. Implementation
  12. Experimental Results
  13. Baseline Detection Methods
  14. Blocking Event Detection Evaluation
  15. Case Study: 2003 European Heatwave
  16. ...and 4 more sections

Figures (10)

  • 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 light grey).
  • 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.
  • Figure 3: ERA5 dataset (1979–2019), June 5 daily ensemble. Left: the boundaries of 23 ensemble members, which are blocking events detected across 41 years. Right: the contour boxplot of the ensemble, including the median blocking boundary (orange), 50-percentile central envelope (meganta), and 100-percentile envelope (blue). The background frequency heatmap highlights blocking frequency.
  • Figure 4: 3D temporal stacks of a seasonal ensemble (ERA5 dataset, May 28–Sep 4, 1979–2019). Left: a 3D median stack, where daily median contours are stacked by date along the $z$-axis (earlier dates at the bottom). Right: a 3D frequency stack, constructed by stacking daily frequency heatmaps into a 3D volume along the same $z$-axis, with color encoding frequency. The red box highlights a mid-June interval during which the median contours cluster over the west of Europe. Together, these 3D temporal stacks summarize the evolution and spatial persistence of blocking events throughout the season.
  • Figure 5: ERA5 dataset: the frequency heatmap of normalized $Z_{500}$ anomalies over Europe for July 26 - August 14, 2003. White lines show national borders and coastlines. The black contour (isovalue=1.2) delineates the high-pressure boundary as defined by our detection method. Bottom annotations in each map report our detection and the ground-truth labels (GTD) for each date; tick = blocked, cross = not blocked.
  • ...and 5 more figures