THUNER - Thunderstorm Event Reconnaissance

Welcome to THUNER’s documentation!

The Thunderstorm Event Reconnaissance (THUNER) package is a flexible toolkit for multi-feature detection, tracking, tagging and analysis of events in meteorological datasets. THUNER’s intended application is to the tracking and analysis of convective weather events. If you use this package in your work, consider citing the following papers;

• Leese et al. (1971)

• Dixon and Wiener (1993)

• Whitehall et al. (2015)

• Fridlind et al (2019)

• Raut et al (2021)

• Short et al. (2023)

Note many excellent alternatives to THUNER exist, including PyFLEXTRKR, GTG, TAMS, tobac and MOAAP. When designing a tracking based research project involving THUNER, consider performing sensitivity tests using these alternatives.

Check out the Installation section for instructions on installing and running THUNER

Contents

• 1. Introduction
  • 1.1. History
  • 1.2. Objectives
  • 1.3. Design
• 2. Installation
  • 2.1. From GitHub
  • 2.2. From conda-forge
  • 2.3. From PyPI
• 3. Tutorials
  • 3.1. Basics: GridRad Severe
  • 3.2. Working Remotely: NCI Gadi
  • 3.3. Tracking Methods: CPOL
  • 3.4. Testing: Synthetic Data
  • 3.5. Himawari
• 4. API
  • 4.1. thuner.utils
  • 4.2. thuner.option
  • 4.3. thuner.default
  • 4.4. thuner.attribute
  • 4.5. thuner.track
  • 4.6. thuner.parallel
  • 4.7. thuner.data
  • 4.8. thuner.detect
  • 4.9. thuner.group
  • 4.10. thuner.match
  • 4.11. thuner.analyze
  • 4.12. thuner.visualize
• 5. For Developers
  • 5.1. Input Records
  • 5.2. Tracks

Acknowledgements

THUNER was developed by Ewan Short while supported by Australian Research Council grants CE170100023. and DP200102516. Computational resources during development were provided by the Australian National Computational Infrastructure (NCI). THUNER’s documentation is hosted on Read the Docs.