jimeking88/tint

TINT Is Not TITAN. Python code for tracking objects. Specifically storm cells.sti风暴追踪雷达回波移动方向和速度的提取

TINT

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 [email protected] if you need assistance.

Check out this demonstration

The development is currently led by the Data Informatics and Geophysical Retrievals (DIGR) group in the Environmental Sciences Group at Argonne National Laboratory.

Dependencies

• NumPy
• Pandas
• SciPy
• matplotlib
• cartopy
• Py-ART
• ffmpeg

Install

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 .

Citing

Currently for citing please cite:

Raut, B. A., Jackson, R., Picel, M., Collis, S. M., Bergemann, M., & Jakob, C. (2021). An Adaptive Tracking Algorithm for Convection in Simulated and Remote Sensing Data. Journal of Applied Meteorology and Climatology, 60(4), 513-526.

Acknowledgements

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.

References

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.

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.

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.