Welcome to tintX’s documentation!

TintX is an adaptation of the tint tracking algorithm. Tint and tintX are easy-to-use storm cell tracking packages. While Tint is meant to be applied to radar data using the py-ART toolkit, tintX can be applied with any data - for example output from numerical weather prediction models.

How does the tint algorithm work?

The original tracking algorithm that has been developed by a team of researchers at Monash University Raut et al. 2020. The algorithm is designed to track storm cells using phase correlation between two consecutive time steps which is followed by an application of the Hungarian Maximum Matching Algorithm to identify cells that are connected in time. The algorithm assigns every identified storm cell a unique identifier (uid). The Hungarian Matching Algorithm decides whether a new uid (a new storm cell appears or splits from another system) or an existing uid is assigned (a storm cell from the previous time step). This unique identifiers allow for a connection of individual storm cells in time and space.

The original tint package, which has been developed for radar data only, is adopted to be able to track model and radar based rainfall data alike.

If you just want to try the usage and play with tracking data you can follow this link to start a binder session and familiarise yourself with the tracking by executing one of the example notebooks.

Installation

The tintX package can bin installed using the conda-forge conda channel:

conda install -c conda-forge tintx

Alternatively the package can be installed with pip:

python3 -m pip install tintx

Citation

The original version of the Tint tracking algorithm can be found under:

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. [1]

The TintX tracking package is introduced in:

Bergemann, M. Lane T. P., Wales, S., Narsey, S., Louf, V. (2022), High Resolution Simulations of Tropical Island Thunderstorms: Does an Increase in Resolution Improve the Representation of Extreme Rainfall?. [2]

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 was funded by the Australian Research Council’s Centre of Excellence for Climate Extremes under the funding number CE170100023.

Documentation content:

• Quick usage reference
  • The tintX user interface
  • Methods and properties
• Creating cell tracks with the RunDirectory class
  • RunDirectory
• Configuring the tracking Algorithm
  • Tracking tuning parameters
  • Setting and getting the tuning parameters
  • get()
  • set
• Example I: Tracking data stored in files
  • The tuning parameters
  • Accessing the track data
• Example II: Tracking already loaded data
  • The tuning parameters
  • Accessing the track data
• Using the command line interface
  • Applying the tracking algorithm
  • Plotting the data

See also

Module xarray

How to work with xarray datasets.

Module pandas

How to work with pandas DataFrames

[1]

doi: 10.1175/JAMC-D-20-0119.1

[2]

doi: 10.1002/qj.4360

Indices and tables

• Index

• Module Index

• Search Page