Author: Yushan Han
Email: yshhan@ucdavis.edu
Copyright 2025-2026 Yushan Han
SyCLoPS 1970-2024 ERA5 low-pressure system catalogs and datasets can be downloaded via Zenodo: [https://doi.org/10.5281/zenodo.10906284]
(1) The SyCLoPS classifier now accepts the data grid resolution as a customizable input to refine tropical cyclone detection at any grid resolution.
(2) Improved detection of extratropical and tropical transitions in tropical cyclone tracks
(3) The introduction of the "--prioritize" argument in the StitchNodes command enables 6-hour interval tracking with a large range distance
(4) Improved support for unstructured grids
(5) A more convenient workflow for easily adapting to different models
(6) Automatic detection of TE runtime errors in subprocesses within the Python script
(7) A new tool (NodeFile_to_csv.py) uploaded to the optional folder enables direct transformation from DetectNodes outputs to a user-friendly csv.
The System for classification of Low-Pressure Systems (SyCLoPS) is an all-in-one framework for objective detection and classification of any type of surface low-pressure systems (LPSs) in any large atmospheric dataset and model outputs in one workflow. The required atmopsheric variables for the framework can be found at manual/Required_variable.png. SyCLoPS is capable of labeling the following types of LPS:
| LPS node Label | LPS Full Name |
|---|---|
| HAL / HATHL | High-altitude Low / High-altitude Thermal Low |
| THL / DOTHL | Thermal Low / Deep (Orographic) Thermal Low |
| DSD / DST / DSE | Dry / Tropical/ Extratropical Disturbance |
| TC | Tropical Cyclone |
| TD / TD(MD) | Tropical Depression / TD (Monsoon Depression) |
| TLO / TLO(ML) | Tropical Low / TLO (Monsoon Low) |
| SS(STLC) / PL(PTLC) | Subtropical Storm (Subtropical Tropical-Like Cyclone) / Polar Low (Polar TLC) |
| SC / EX | Subtropical Cyclone / Extratropical Cyclone |
It also labels the four high-impact LPS tracks: TC, MS (Monsoonal System), SS(STLC), and PL(PTLC) tracks. Tracks are labeled when LPSs are stably labeld as a type of LPS class for a period of time so it can be compared with the corresponding subjective dataset.
The SyCLoPS software is built upon TempestExtremes (TE), the state-of-the-art atmospheric feature detector. The main TE branch can be downloaded at: [https://github.com/ClimateGlobalChange/tempestextremes]
Note: TE now supports reading and calculating missing values in model outputs.
The SyCLoPS software requires the following Python packages: Xarray, Pandas, PyArrow, multiprocess, cftime, and Scipy.
Simply download the SyCLoPS package to your machine. Then:
-
Review the codes and comments in
SyCLoPS_main.py. Change variable names and other specifications according to your needs. -
Run
SyCLoPS_main.pyand follow the instructions carefully.
Optional steps for tagging precipitation and size blobs of LPSs can be found in the optional folder.
-
(New manual for v1.1 will be available soon) SyCLoPS manual (in PDF) can be found in the
manualfolder and can also found online here (the online version may be lagging): https://climate.ucdavis.edu/syclops.php -
Details of the various executables that are part of TempestExtremes can be found in the user guide: https://climate.ucdavis.edu/tempestextremes.php
If you use the SycLoPS 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.