TINT (TINT is not TITAN) is an easy-to-use storm cell tracking package based on the TITAN methodology by Dixon and Wiener. This code is in early alpha stage, so documentation and testing are still being built. If you have any suggestions or wish to contribute, please open an issue. Feel free to email me at mhpicel@gmail.com if you need assistance.
The development is currently led by the Data Informatics and Geophysical Retrievals (DIGR) group in the Environmental Sciences Group at Argonne National Laboratory.
To install TINT, first install the dependencies listed above. We recommend installing Py-ART from conda forge:
conda install -c conda-forge arm_pyart
Then clone:
git clone https://github.com/openradar/TINT.git
then:
cd TINT pip install -e .
Currently for citing please cite:
Fridlind, A. M., van Lier-Walqui, M., Collis, S., Giangrande, S. E., Jackson, R. C., Li, X., Matsui, T., Orville, R., Picel, M. H., Rosenfeld, D., Ryzhkov, A., Weitz, R., and Zhang, P.: Use of polarimetric radar measurements to constrain simulated convective cell evolution: a pilot study with Lagrangian tracking, Atmos. Meas. Tech., 12, 2979–3000, https://doi.org/10.5194/amt-12-2979-2019, 2019.
This work is the adaptation of tracking code in R created by Bhupendra Raut who was working at Monash University, Australia in the Australian Research Council's Centre of Excellence for Climate System Science led by Christian Jakob. This work was supported by the Department of Energy, Atmospheric Systems Research (ASR) under Grant DE-SC0014063, “The vertical structure of convective mass-flux derived from modern radar systems - Data analysis in support of cumulus parametrization”
The development of this software is supported by the Climate Model Development and Validation (CMDV) activity which funded by the Office of Biological and Environmental Research in the US Department of Energy Office of Science.
Dixon, M. and G. Wiener, 1993: TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology. J. Atmos. Oceanic Technol., 10, 785–797, doi: 10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.
Leese, J.A., C.S. Novak, and B.B. Clark, 1971: An Automated Technique for Obtaining Cloud Motion from Geosynchronous Satellite Data Using Cross Correlation. J. Appl. Meteor., 10, 118–132, doi: 10.1175/1520-0450(1971)010<0118:AATFOC>2.0.CO;2.