Articles | Volume 17, issue 13
https://doi.org/10.5194/gmd-17-5309-2024
https://doi.org/10.5194/gmd-17-5309-2024
Development and technical paper
 | 
11 Jul 2024
Development and technical paper |  | 11 Jul 2024

tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena

G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever

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Cited articles

Allan, D. B., Caswell, T., Keim, N. C., van der Wel, C. M., and Verweij, R. W.: soft-matter/trackpy: Trackpy v0.5.0, Zenodo [code], https://doi.org/10.5281/zenodo.4682814, 2021. 
Bluestein, H. B., McCaul, E. W., Byrd, G. P., Walko, R. L., and Davies-Jones, R.: An Observational Study of Splitting Convective Clouds, Mon. Weather Rev., 118, 1359–1370, 1990. 
Bukowski, J. and van den Heever, S. C.: Direct radiative effects in haboobs, J. Geophys. Res., 126, e2021JD034814, https://doi.org/10.1029/2021jd034814, 2021. 
Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A., Moffat, J., Golland, P., and Sabatini, D. M.: CellProfiler: image analysis software for identifying and quantifying cell phenotypes, Genome Biol., 7, R100, https://doi.org/10.1186/gb-2006-7-10-r100, 2006. 
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Short summary
Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
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