TCTrack: Facilitating FAIR tropical cyclone tracking software and data

Cyclone tracks are an important diagnostic in model evaluation when looking at long-term historical statistics and are also of interest in future scenario projections. In addition to modelling, generating tracks from observational or reanalysis data is important in aiding comparisons and assessing trends.

Multiple approaches to generating cyclone tracks from model and observational data exist, each with their own opinion as to which variables are important, how candidate storms are identified, and how these candidates are stitched together to form tracks. Each method comes in its own codebase with varying degrees of user documentation meaning that those interested in tracking often select just one approach and stick to it. Further, all tracking codes produce different, often customised, output formats with minimal metadata that do not come close to meeting FAIR data standards.
This makes any downstream data use or tracking intercomparison a challenge.

We present TCTrack, an open-source Python-based package that provides a common interface to popular tropical cyclone tracking codes. Extensive documentation ensures accessible usage and manipulation of existing codes, as well as providing guidance on input requirements and preprocessing of data. Perhaps most notable is that all output data provided by TCTrack is in a common data format, regardless of the tracking algorithm used, and conforms to the Climate and Forecast (CF) metadata conventions (specifically H4:Trajectory data) preserving variable metadata information from the input files. This metadata-rich output aids in reproducibility and reusability and makes downstream analysis with a variety of other tools straightforward. Finally, TCTrack is built on an easily extendable framework meaning that addition of other tracking approaches from, and for use by, the community is both straightforward and encouraged.

This poster showcases key aspects of the TCTrack software, a discussion of tracking data format using the CF-Conventions, and some results from deployment in an intercomparison study of different tracking methods applied to CMIP data.

References:

• Atkinson, J.W. & Avis, S.J. (2025). TCTrack. https://github.com/Cambridge-ICCS/TCTrack
• Eaton, B., et al. (2025). NetCDF Climate and Forecast (CF) Metadata Conventions v1.13. https://cfconventions.org/
• Hodges, K., Cobb, A., & Vidale, PL. (2017). How Well Are Tropical Cyclones Represented in Reanalysis Datasets? Journal of Climate 30, 14: 5243-5264
• Ullrich, P.A., et al. (2021) TempestExtremes v2.1: A Community Framework for Feature Detection, Tracking, and Analysis in Large Datasets. Geoscientific Model Development 14, no. 8: 5023–48.
• Vitart, F., & Stockdale T.N. (2001) Seasonal Forecasting of Tropical Storms Using Coupled GCM Integrations. Monthly Weather Review 129, 10: 2521-2537