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
EGUsphere, https://doi.org/10.5194/egusphere-2024-112,https://doi.org/10.5194/egusphere-2024-112, 2024
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
Overview: Precipitation characteristics and sensitivities to environmental conditions during GoAmazon2014/5 and ACRIDICON-CHUVA
Luiz A. T. Machado, Alan J. P. Calheiros, Thiago Biscaro, Scott Giangrande, Maria A. F. Silva Dias, Micael A. Cecchini, Rachel Albrecht, Meinrat O. Andreae, Wagner F. Araujo, Paulo Artaxo, Stephan Borrmann, Ramon Braga, Casey Burleyson, Cristiano W. Eichholz, Jiwen Fan, Zhe Feng, Gilberto F. Fisch, Michael P. Jensen, Scot T. Martin, Ulrich Pöschl, Christopher Pöhlker, Mira L. Pöhlker, Jean-François Ribaud, Daniel Rosenfeld, Jaci M. B. Saraiva, Courtney Schumacher, Ryan Thalman, David Walter, and Manfred Wendisch
Atmos. Chem. Phys., 18, 6461–6482, https://doi.org/10.5194/acp-18-6461-2018,https://doi.org/10.5194/acp-18-6461-2018, 2018
Short summary

Related subject area

Atmospheric sciences
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024,https://doi.org/10.5194/gmd-17-2419-2024, 2024
Short summary
Advances and prospects of deep learning for medium-range extreme weather forecasting
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024,https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024,https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024,https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024,https://doi.org/10.5194/gmd-17-2053-2024, 2024
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.