TC size calculator and analysis

Author: Yushan Han
Email: yshhan@ucdavis.edu

Copyright 2026 Yushan Han

Linked Zenodo DOI: [Available Soon]

Introduction

This repository contains software for detecting and calculating the size of tropical cyclones (TCs) in atmospheric reanalysis products and model outputs. This software was used to produce the results presented in our paper (preprint link available soon). The repository also contains the tools and Jupyter notebooks used to create the figures in the paper. Data produced for the paper can be downloaded via the links provided in the corresponding subfolders.

Details of how we define and calcualte TC size can be found in Section 2.5 and supporting information text S5 of the paper.

Dependencies

Wind profile detection requires the installation of TempestExtremes (TE) software, which is available at: [https://github.com/ClimateGlobalChange/tempestextremes]

Some additional Python packages may be required. Please refer to the relevant Python/Jupyter notebook script for more information.

Usage

To calculate TC size in model outputs, first have your objectively detected TC tracks by SyCLoPS ready in the classified_track/SyCLoPS_track folder. Then follow the steps below:

1. Use write_nodefile.py to transform the SyCLoPS classified catalog (in Parquet format) into StitchNodes-format text files containing only TC records.

2. Modify and run TE_NodefileEditor.sh to create quadrantal radial wind profiles and output wind radii at a specific wind speed. The output quadrant order is "NE, SE, SW, NW," as in IBTrACS. TE installation is required.

3. Run the script TC_size_calculator.py to calculate the final TC size based on the quadrantal wind radii provided by TE's NodeFileEditor.

Other useful tools used in our study are available in the tools folder. These include:

1. assign_basin.py: This tool assigns basin labels to each TC or low-pressure system (LPS) record based on the basin mask file (basin_mask_05deg.nc).

2. obs_track_type.py: Assign a precursor type and a posterior scenario label to each observed IBTrACS TC track, based on the ERA5 LPS tracks that are classified by SyCLoPS. Details on the definition of track type can be found in Section 2.4 of the paper.

3. processing_IB.py: Preprocesses the raw IBTrACS file to meet the needs of the study.

4. TC_detection_skill_node.py: Computes TC detection skills (hit rate, false alarm rate, and CSI) at the node level of an objective TC dataset.

Publications

If you use this software please cite our publications:

[https://doi.org/10.1029/2024JD041287] Han, Y. and P.A. Ullrich (2025) "The System for Classification of Low-Pressure Systems (SyCLoPS): An all-in-one objective framework for large-scale datasets" J. Geophys. Res. Atm. 130 (1), e2024JD041287, doi: 10.1029/2024JD041287.

[https://dx.doi.org/10.5194/gmd-14-5023-2021] Ullrich, P.A., C.M. Zarzycki, E.E. McClenny, M.C. Pinheiro, A.M. Stansfield and K.A. Reed (2021) "TempestExtremes v2.1: A community framework for feature detection, tracking and analysis in large datasets" Geosci. Model. Dev. 14, pp. 5023–5048, doi: 10.5194/gmd-14-5023-2021.

[http://dx.doi.org/10.5194/gmd-2016-217] Ullrich, P.A. and C.M. Zarzycki (2017) "TempestExtremes v1.0: A framework for scale-insensitive pointwise feature tracking on unstructured grids" Geosci. Model. Dev. 10, pp. 1069-1090, doi: 10.5194/gmd-10-1069-2017.