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CoCoMET v1.0: a unified open-source toolkit for atmospheric object tracking and analysis
by
Brooks, Calvin
, Hahn, Travis
, Wang, Dié
, Li, Jie Xi
, Gupta, Siddhant
, Weiner, Hershel
in
Algorithms
/ Atmospheric evolution
/ Atmospheric research
/ Clouds
/ Convexity
/ Datasets
/ Intercomparison
/ Learning curves
/ Machine learning
/ Precipitation
/ Simulation
/ Toolkits
/ Tracking
/ Tracking techniques
2025
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CoCoMET v1.0: a unified open-source toolkit for atmospheric object tracking and analysis
by
Brooks, Calvin
, Hahn, Travis
, Wang, Dié
, Li, Jie Xi
, Gupta, Siddhant
, Weiner, Hershel
in
Algorithms
/ Atmospheric evolution
/ Atmospheric research
/ Clouds
/ Convexity
/ Datasets
/ Intercomparison
/ Learning curves
/ Machine learning
/ Precipitation
/ Simulation
/ Toolkits
/ Tracking
/ Tracking techniques
2025
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Do you wish to request the book?
CoCoMET v1.0: a unified open-source toolkit for atmospheric object tracking and analysis
by
Brooks, Calvin
, Hahn, Travis
, Wang, Dié
, Li, Jie Xi
, Gupta, Siddhant
, Weiner, Hershel
in
Algorithms
/ Atmospheric evolution
/ Atmospheric research
/ Clouds
/ Convexity
/ Datasets
/ Intercomparison
/ Learning curves
/ Machine learning
/ Precipitation
/ Simulation
/ Toolkits
/ Tracking
/ Tracking techniques
2025
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CoCoMET v1.0: a unified open-source toolkit for atmospheric object tracking and analysis
Journal Article
CoCoMET v1.0: a unified open-source toolkit for atmospheric object tracking and analysis
2025
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Overview
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 Eulerian/stationary datasets external to the tracking data 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
Copernicus GmbH
Subject
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