Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2753-2023
https://doi.org/10.5194/gmd-16-2753-2023
Methods for assessment of models
 | 
23 May 2023
Methods for assessment of models |  | 23 May 2023

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

Related authors

Tracking precipitation features and associated large-scale environments over southeastern Texas
Ye Liu, Yun Qian, Larry K. Berg, Zhe Feng, Jianfeng Li, Jingyi Chen, and Zhao Yang
Atmos. Chem. Phys., 24, 8165–8181, https://doi.org/10.5194/acp-24-8165-2024,https://doi.org/10.5194/acp-24-8165-2024, 2024
Short summary
A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes
Jianfeng Li, Andrew Geiss, Zhe Feng, L. Ruby Leung, Yun Qian, and Wenjun Cui
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-112,https://doi.org/10.5194/essd-2024-112, 2024
Revised manuscript accepted for ESSD
Short summary
Climatological occurrences of hail and tornadoes associated with mesoscale convective systems in the United States
Jingyu Wang, Jiwen Fan, and Zhe Feng
Nat. Hazards Earth Syst. Sci., 23, 3823–3838, https://doi.org/10.5194/nhess-23-3823-2023,https://doi.org/10.5194/nhess-23-3823-2023, 2023
Short summary
A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017
Jianfeng Li, Zhe Feng, Yun Qian, and L. Ruby Leung
Earth Syst. Sci. Data, 13, 827–856, https://doi.org/10.5194/essd-13-827-2021,https://doi.org/10.5194/essd-13-827-2021, 2021
Short summary
The Green Ocean: precipitation insights from the GoAmazon2014/5 experiment
Die Wang, Scott E. Giangrande, Mary Jane Bartholomew, Joseph Hardin, Zhe Feng, Ryan Thalman, and Luiz A. T. Machado
Atmos. Chem. Phys., 18, 9121–9145, https://doi.org/10.5194/acp-18-9121-2018,https://doi.org/10.5194/acp-18-9121-2018, 2018

Related subject area

Atmospheric sciences
Carbon dioxide plume dispersion simulated at the hectometer scale using DALES: model formulation and observational evaluation
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart J. H. van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
Geosci. Model Dev., 18, 4571–4599, https://doi.org/10.5194/gmd-18-4571-2025,https://doi.org/10.5194/gmd-18-4571-2025, 2025
Short summary
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025,https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
SynRad v1.0: a radar forward operator to simulate synthetic weather radar observations from volcanic ash clouds
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025,https://doi.org/10.5194/gmd-18-4417-2025, 2025
Short summary
Chempath 1.0: an open-source pathway analysis program for photochemical models
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev., 18, 4433–4454, https://doi.org/10.5194/gmd-18-4433-2025,https://doi.org/10.5194/gmd-18-4433-2025, 2025
Short summary
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
Geosci. Model Dev., 18, 4353–4398, https://doi.org/10.5194/gmd-18-4353-2025,https://doi.org/10.5194/gmd-18-4353-2025, 2025
Short summary

Cited articles

Ashley, W. S., Mote, T. L., Dixon, P. G., Trotter, S. L., Powell, E. J., Durkee, J. D., and Grundstein, A. J.: Distribution of Mesoscale Convective Complex Rainfall in the United States, Mon. Weather Rev., 131, 3003–3017, https://doi.org/10.1175/1520-0493(2003)131<3003:DOMCCR>2.0.CO;2, 2003. 
Barber, K. A., Burleyson, C. D., Feng, Z., and Hagos, S. M.: The influence of shallow cloud populations on transitions to deep convection in the Amazon, J. Atmos. Sci., 79, 723–743, https://doi.org/10.1175/jas-d-21-0141.1, 2021. 
Catto, J. L., Jakob, C., and Nicholls, N.: Can the CMIP5 models represent winter frontal precipitation?, Geophys. Res. Lett., 42, 8596–8604, https://doi.org/10.1002/2015GL066015, 2015. 
Chen, J., Hagos, S., Feng, Z., Fast, J. D., and Xiao, H.: The Role of Cloud-Cloud Interactions in the Organization of Shallow Cumulus Clouds, J. Atmos. Sci., 80, 671–686, https://doi.org/10.1175/jas-d-22-0004.1, 2022. 
Download
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 demonstrates applications for tracking deep convective cells and mesoscale convective systems from observations and model simulations at a wide range of scales.
Share