A clear and consistent framework for the detection and classification of all cyclones - ranging from hurricanes and winter storms to monsoon-related events - is beneficial for the scientific research community because it can aid process-level understanding, enhance the efficiency of operational forecasting, and increase effective communication of risks. Ultimately, such a framework can safeguard lives and infrastructure.
present a novel detection and classification framework called the System for Classification of Low‐Pressure Systems (SyCLoPS). The authors use the data-driven framework to classify 16 different types of low-pressure systems across the world. SyCLoPS - a suitable designation based on the all-seeing Greek mythical Cyclops - is a fitting designation for a system designed to detect and track all kinds of storms, anywhere in the world.
SyCLoPS was used to identify more than 379 thousand distinct storm tracks through high-resolution global data sourced from the European Center for Medium Range Weather Forecasting's global data product between 1979 to 2022. The author's approach - the first to classify all low-pressure systems using a single global dataset - can be applied to any dataset that includes a basic set of atmospheric parameters, enabling consistent characterization and categorization of low-pressure systems. The implications are significant. Why? Because such a framework can use historical data to understand past trends and can be used to perform analysis of future projections, to improve understanding of likely changes.
Maintaining a consistent framework for the detection and classification of all cyclones is essential for improving understanding of how a warming climate may influence their frequency, landfall patterns, and impact zones. These changes could affect both densely populated urban areas and non-urban regions, including key agricultural zones that may become more vulnerable to storm activity. This study represents an important step toward building a unified framework for consistently identifying and linking past and future projections of storm systems.