Preprints
https://doi.org/10.5194/egusphere-2022-1136
https://doi.org/10.5194/egusphere-2022-1136
09 Nov 2022
 | 09 Nov 2022

PyFLEXTRKR: a Flexible Feature Tracking Python Software for Convective Cloud Analysis

Zhe Feng, Joseph Hardin, Hannah C. Barnes, Jianfeng Li, L. Ruby Leung, Adam Varble, and Zhixiao Zhang

Abstract. This paper describes the new open-source framework PyFLEXTRKR (Python FLEXible object TRacKeR), a flexible atmospheric feature tracking software package with specific capabilities to track convective clouds from a variety of observations and model simulations. This software can track any atmospheric 2D objects and handle merging and splitting explicitly. The package has a collection of multi-object identification algorithms, scalable parallelization options and has been optimized for large datasets including global high-resolution data. We demonstrate applications of PyFLEXTRKR on tracking individual deep convective cells and mesoscale convective systems from observations and model simulations ranging from large-eddy resolving (~100s m) to mesoscale (~10s km) resolutions. Visualization, post-processing, and statistical analysis tools are included in the package. New Lagrangian analyses of convective clouds produced by PyFLEXTRKR applicable to a wide range of datasets and scales facilitate advanced model evaluation and development efforts as well as scientific discovery.

Zhe Feng et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1136', Anonymous Referee #1, 18 Nov 2022
    • AC3: 'Reply on RC1', Zhe Feng, 23 Jan 2023
  • CEC1: 'Comment on egusphere-2022-1136', Juan Antonio Añel, 12 Dec 2022
    • AC1: 'Reply on CEC1', Zhe Feng, 12 Dec 2022
  • RC2: 'Comment on egusphere-2022-1136', Anonymous Referee #2, 13 Dec 2022
    • AC2: 'Reply on RC2', Zhe Feng, 23 Jan 2023

Zhe Feng et al.

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Short summary
PyFLEXTRKR is a flexible atmospheric feature tracking framework with specific capabilities to track convective clouds from a variety of observations and model simulations. The package has a collection of multi-object identification algorithms and has been optimized for large datasets. This paper describes the algorithms and demonstrate applications on tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.