Skip to content

This is a fork of the TINT repository, intended to be used to track large mesoscale objects in radar data. Currently referring to this version as MINT (mesoscale TINT) to distinguish it from base TINT.

License

THOR-proj/TINT

 
 

Repository files navigation

MINT (Mesoscale TINT)

This is a fork of the base TINT repository, intended to be used for tracking and analysing large mesoscale objects in radar reflectivity images. Modifications made by Ewan Short while undertaking a PhD at the University of Melbourne.

Below are some animations for example systems identified in the CPOL research radar record. Click on an animation to view at native resolution, or right click "save image as" to download.

Example front fed trailing stratiform system. MINT

Example front fed leading stratiform system. MINT

Example rear fed trailing stratiform system. MINT

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
python setup.py install

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.

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.

About

This is a fork of the TINT repository, intended to be used to track large mesoscale objects in radar data. Currently referring to this version as MINT (mesoscale TINT) to distinguish it from base TINT.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 99.7%
  • Shell 0.3%