The System for Classification of Low-Pressure Systems (SyCLoPS): An All-In-One Objective Framework for Large-Scale Data Sets

We propose the first unified objective framework (SyCLoPS) for detecting and classifying all types of low-pressure systems (LPSs) in a given data set. We use the state-of-the-art automated feature tracking software TempestExtremes (TE) to detect and track LPS features globally in ERA5 and compute 16 parameters from commonly found atmospheric variables for classification. A Python classifier is implemented to classify all LPSs at once. The framework assigns 16 different labels (classes) to each LPS data point and designates four different types of high-impact LPS tracks, including tracks of tropical cyclone (TC), monsoonal system, and tropical-like cyclones (subtropical storm and polar low). The framework thus provides the first global tropical-like cyclones (TLC) detection scheme by detecting similar physical features to TCs among non-tropical system candidates and optimizing detection thresholds against subjective data sets. The vertical cross section composite of the four types of high-impact LPS we detect each shows distinct structural characteristics. 

The classification process involves disentangling high-altitude and drier LPSs, differentiating tropical and non-tropical LPSs using novel criteria, and optimizing for the detection of the four types of high-impact LPS. A comparison of our labels with those in the International Best Track Archive for Climate Stewardship (IBTrACS) revealed an overall accuracy of 95% in distinguishing between tropical systems, extratropical cyclones, and disturbances, and a median error of 6 hours in determining extratropical transition completion time. We demonstrate that the SyCLoPS framework is valuable for investigating various aspects of mid-latitude storms and post-TCs in climate data, such as the evolution of a single storm track at every stage, patterns of storm frequencies, and precipitation or wind influence associated with impactful mid-latitude storms.