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A modified algorithm for identifying and tracking extratropical cyclones

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Abstract

In this study, a modified identification and tracking algorithm for extratropical cyclones is developed. This identification scheme is based on triangular-mesh contouring techniques combined with a connected-component labeling method in order to detect the outer boundaries and spatial domain characteristics of individual cyclones. A new tracking method allowing for the identification of cyclone merging and splitting events, as well as short-lived windstorms, is developed to reduce the uncertainty in the tracking of extratropical cyclones. I also show that this method excludes the tracks of open systems that would have been unnecessarily detected using conventional NCP methods. The climatological features of the distribution of cyclone frequencies are substantially larger over the traditional storm track regions compared to those seen in previous studies. Interestingly, a significant increase in the cyclone density in the Arctic occurs during all four seasons (up to 19% in summer) compared to that seen with a latitude–longitude gridded mesh analysis. I develop two new regional intensity indices (depth and vorticity) based on the cyclonic domain to better quantify the cyclonic activity in the Arctic region, and find that the interannual variabilities in these two indices are highly consistent. The results of this analysis may shed light on high-latitude cyclonic behavior studies via the newly detected 2D cyclone atlas derived from this cyclonic-domain-based algorithm.

摘要

建立了对于二维气旋的自动识别与追踪算法, 有助于提高热带外地区的多中心气旋以及中尺度风暴识别的可靠性, 并揭示了北极地区的气旋活动变化与北极涛动存在密切联系.

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Acknowledgements

This work was jointly sponsored by the National Basic Research Program of China (Grant No. 2015CB953904), the National Natural Science Foundation of China (Grant No. 41575081), the Natural Science Foundation of Jiangsu Province (Grant No. BK20161603), the Public Sector (Meteorology) Special Research Foundation (Grant No. GYHY201406024). The authors express their appreciation of the staff at ECMWF for making the reanalysis datasets freely available. I am grateful for the very constructive comments of the two reviewers.

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  1. Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Chuhan Lu

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Lu, C. A modified algorithm for identifying and tracking extratropical cyclones. Adv. Atmos. Sci. 34, 909–924 (2017). https://doi.org/10.1007/s00376-017-6231-2

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  • DOI: https://doi.org/10.1007/s00376-017-6231-2

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