Automatic identification, tracking, and classification of mesoscale atmospheric vortices in high-resolution numerical model data

Mesoscale coherent vortex structures (CVSs), including polar lows and tropical cyclones, play a central role in generating high-impact weather such as heavy precipitation, strong winds, and extreme air–sea fluxes. However, a consistent and physically interpretable climatology of these systems across different vortex types and regions remains limited, constraining our ability to assess associated atmospheric hazards.

This study presents a unified framework for identifying, tracking, and clustering mesoscale CVSs in the atmosphere. The analysis uses high-resolution numerical simulations over the North Atlantic and the Arctic. Vortex structures are identified with the Eulerian Rortex criterion, which isolates the rigid-body rotation component of the flow in three-dimensional velocity fields. Identified vortices are then tracked with a self-developed algorithm and subsequently classified by K-means clustering based on a minimal set of physically motivated features describing vortex dynamics, thermodynamics, and geometry.

First, to ensure the framework's physical interpretability, clustering is applied to reference sets of vortex tracks manually identified for 2010 (North Atlantic) and 2019 (Arctic) using the same Rortex-based detection. These tracks are linked to known vortex types using existing best-track datasets for tropical cyclones and polar mesocyclones, providing a physically grounded reference for the most intense and best-studied systems. Clustering the manual tracks enables evaluation of how known vortex types are represented in feature space, assessment of class separability, and identification of additional vortex types with distinct dynamical and thermodynamic characteristics. The resulting classes are further analyzed using a range of physically relevant metrics — including intensity, lifetime, and associated surface heat fluxes — allowing comparison of their relative contributions to hazardous weather and air–sea interaction processes.

The validated feature space and clustering strategy are then applied to a 40-year automatically tracked dataset, enabling a consistent large-scale climatological analysis. The results reveal changes in the relative occurrence and dominance of different vortex types over the past four decades, suggesting links to large-scale circulation variability and ongoing climate change. This approach improves the physical interpretability and robustness of mesoscale vortex climatologies and supports more reliable assessment of associated atmospheric hazards.