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
A new set of indicators for model evaluation complementing FAIRMODE's modelling quality objective (MQO)
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025,https://doi.org/10.5194/gmd-18-4231-2025, 2025
Short summary
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025,https://doi.org/10.5194/gmd-18-4075-2025, 2025
Short summary
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025,https://doi.org/10.5194/gmd-18-3985-2025, 2025
Short summary
Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025,https://doi.org/10.5194/gmd-18-3921-2025, 2025
Short summary
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025,https://doi.org/10.5194/gmd-18-3965-2025, 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