A novel tracker for detecting tropical cyclones in the Northwest Pacific using reanalysis data

GU Shutao; LIAN Tao

doi:10.3969/j.issn.1001-909X.2024.04.004

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Journal of Marine Sciences ›› 2024, Vol. 42 ›› Issue (4) : 34-42. DOI: 10.3969/j.issn.1001-909X.2024.04.004

A novel tracker for detecting tropical cyclones in the Northwest Pacific using reanalysis data

GU Shutao ¹^,² ,
LIAN Tao ¹^,²^,^*

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Abstract

A simple tropical cyclone tracker based on wind stress characteristics was designed in this study, using the best track dataset from the United States Joint Typhoon Warning Center, wind speed data and sea level pressure field data from the European Centre for Medium-Range Weather Forecasts. The tracker was used to detect tropical cyclones in the Northwest Pacific from 1985 to 2014, and its performance metrics were evaluated. The results showed that the tracker was able to accurately reproduce the spatiotemporal structure of tropical cyclones in the Northwest Pacific. The peak period of activity was concentrated from August to October, and the latitudinal positions varied with the seasons, consistent with the observations. Additionally, the tracker used the minimum sea level pressure as a criterion for determining cyclone intensity, and the number of cyclones identified at different intensities closely matched the observations. The tracker performed well in terms of probability of detection and false alarm rate for tropical cyclones, comparable to previously used trackers. Regarding the tropical cyclones detected by the tracker, the research findings showed that approximately 90% of the center positions were within a deviation of 1 degree from the observed positions, and the lifetime deviation was within 2 days, indicating a good representation of the complete movement and evolution of tropical cyclones.

Key words

tropical cyclone / tracker / wind stress / Northwest Pacific / climate model / model bias

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GU Shutao , LIAN Tao. A novel tracker for detecting tropical cyclones in the Northwest Pacific using reanalysis data[J]. Journal of Marine Sciences. 2024, 42(4): 34-42 https://doi.org/10.3969/j.issn.1001-909X.2024.04.004

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