Treffer: 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, subtropical storm and polar low. 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. SyCLoPS produces a better TC detection skill compared to the previous algorithms, highlighted by an approximately 6% reduction in the false alarm rate compared to the previous TE algorithm. The vertical cross section composite of the four types of high‐impact LPS we detect each shows distinct structural characteristics. Finally, we demonstrate that SyCLoPS is valuable for investigating various aspects of LPSs in climate data, such as the evolution of a single LPS track, patterns of LPS frequencies, and precipitation or wind influence associated with a particular LPS class. Plain Language Summary: We create a new objective framework (SyCLoPS) that can detect, track, and categorize different kinds of cyclones (low‐pressure systems) in data sets. We use an advanced software called TempestExtremes to spot cyclones globally in ERA5 reanalysis and then use a Python program to sort all cyclones into 16 different groups based on their characteristics. We also identify four types of significant cyclone tracks: tracks of tropical cyclones, monsoonal systems, subtropical storms, and polar lows. The framework can recognize cyclones over high‐elevation areas and dry cyclones. It can also efficiently separate tropical low‐pressure systems and extratropical (non‐tropical) systems using a novel method. We compare our results against existing catalogs and find that the framework produces objectively tracked tropical cyclones that better match the observations, and the labels given by the framework are in good agreement with those given in the subjective catalogs. We also show the distinct vertical structures for the four types of significant cyclones we identify. Finally, we demonstrate that SyCLoPS can help us understand various aspects of low‐pressure systems in climate data, like how they evolve over time, where they occur more frequently, and their related extreme weather. Key Points: The first all‐inclusive low‐pressure system (LPS) detection and classification framework for climate data and model outputs is proposedThe framework substantially extends LPS track lengths while improving tropical cyclone detection skillThe framework is useful to study the frequency, structure, development, wind impact, and precipitation contribution of each type of LPS [ABSTRACT FROM AUTHOR]

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