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Preprints
https://doi.org/10.5194/egusphere-2025-1328
https://doi.org/10.5194/egusphere-2025-1328
10 Apr 2025
 | 10 Apr 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

CoCoMET v1.0: A Unified Open-Source Toolkit for Atmospheric Object Tracking and Analysis

Travis Hahn, Hershel Weiner, Calvin Brooks, Jie Xi Li, Siddhant Gupta, and Dié Wang

Abstract. Advances in performance and analysis capabilities have accelerated the development of object tracking algorithms for atmospheric research. This has resulted in a growing number of studies using Lagrangian tracking techniques to analyze the evolution of atmospheric phenomena and the underlying processes. However, the increasing complexity and variety of tracking algorithms present a steep learning curve for new users and make it difficult for existing users to compare algorithm performance.

We introduce CoCoMET (Community Cloud Model Evaluation Toolkit), an open-source toolkit that addresses these issues. CoCoMET simplifies the process of running multiple tracking algorithms simultaneously and analyzing objects in both model and observational datasets by specifying parameters in a single configuration file. It standardizes input data from different sources into a consistent format and unifies the tracking output across algorithms. CoCoMET enhances the functionality of existing tracking methods by calculating additional properties such as cell growth and dissipation rates, perimeter, surface area, convexity, and irregularity. In addition, CoCoMET includes a novel method for identifying mergers and splits in 2D and 3D tracks and supports the integration of external Eulerian/stationary datasets for process studies. Its potential utility is demonstrated through examples of model intercomparison, model evaluation against observations, and comparisons between tracking algorithms. Designed for open-source environments, CoCoMET will continue to expand with future releases, incorporating more input data types and tracking algorithms.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Understanding how clouds evolve is important for improving weather predictions, but existing...
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