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

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

Abstract

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

CLC Number:

P444

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.

Figures/Tables 7

Tab.1 Standard of tropical cyclone (TC) intensity classification

热带气旋分类	最小海平面气压阈值/hPa
热带风暴	1 005
1级飓风	990
2级飓风	975
3级飓风	960
4级飓风	945
5级飓风	925

Fig.1 The track density distribution of tropical cyclones by IBTrACS (a) and TC tracker (b) in the Northwest Pacific from 1985 to 2014

Fig.2 The cumulative monthly distribution frequency of the latitude positions of tropical cyclones by IBTrACS (a) and TC tracker (b) in the Northwest Pacific from 1985 to 2014 (The blue curve represents the monthly distribution centre of tropical cyclone tracks in the Northwest Pacific from1985 to 2014.)

Fig.3 The number of tropical cyclones in each category based on minimum sea level pressure by IBTrACS and TC tracker

Fig.4 Comparison of probability of detection (POD) and false alarm probability (FAR) among the novel tropical cyclone tracker and several commonly used trackers

Fig.5 The latitude error (a) and longitude error (b) of the tropical cyclones center positions hit by the novel TC tracker

Fig.6 The lifetime error of the tropical cyclones hit by the novel TC tracker

References 37

[1]	EMANUEL K. Increasing destructiveness of tropical cyclones over the past 30 years[J]. Nature, 2005, 436(7051): 686-688.
[2]	ZHANG Q, WU L G, LIU Q F. Tropical cyclone damages in China 1983-2006[J]. Bulletin of the American Meteorological Society, 2009, 90(4): 489-496.
[3]	张娇艳, 吴立广, 张强. 全球变暖背景下我国热带气旋灾害趋势分析[J]. 热带气象学报, 2011, 27(4):442-454.
	ZHANG J Y, WU L G, ZHANG Q. Tropical cyclone damages in China under the background of global warming[J]. Journal of Tropical Meteorology, 2011, 27(4): 442-454.
[4]	LU X Q, YU H. An objective tropical cyclone intensity estimation model based on digital IR satellite images[J]. Tropical Cyclone Research and Review, 2013, 2(4): 233-241.
[5]	KNAFF J A, LONGMORE S P, MOLENAR D A. An objective satellite-based tropical cyclone size climatology[J]. Journal of Climate, 2014, 27(1): 455-476.
[6]	HU Y, ZOU X. Comparison of tropical cyclone center positions determined from satellite observations at infrared and microwave frequencies[J]. Journal of Atmospheric and Oceanic Tech-nology, 2020, 37(11): 2101-2115.
[7]	CAMARGO S J, WING A A. Tropical cyclones in climate models[J]. WIREs Climate Change, 2016, 7(2): 211-237.
[8]	KNUTSON T, CAMARGO S J, CHAN J C L, et al. Tropical cyclones and climate change assessment: Part I: Detection and attribution[J]. Bulletin of the American Meteorological Society, 2019, 100(10): 1987-2007.
[9]	KNUTSON T, CAMARGO S J, CHAN J C L, et al. Tropical cyclones and climate change assessment: Part II: Projected response to anthropogenic warming[J]. Bulletin of the American Meteorological Society, 2020, 101(3): 303-322.
[10]	CHAN J C L. Interannual and interdecadal variations of tropical cyclone activity over the western North Pacific[J]. Meteorology and Atmospheric Physics, 2005, 89(1): 143-152.
[11]	ELSBERRY R L, LAMBERT T D B, BOOTHE M A. Accuracy of Atlantic and eastern north Pacific tropical cyclone intensity forecast guidance[J]. Weather and Forecasting, 2007, 22(4): 747-762.
[12]	RAPPAPORT E N, FRANKLIN J L, AVILA L A, et al. Advances and challenges at the national hurricane center[J]. Weather and Forecasting, 2009, 24(2): 395-419.
[13]	MANGANELLO J V, HODGES K I, KINTER J L III, et al. Tropical cyclone climatology in a 10-km global atmospheric GCM: Toward weather-resolving climate modeling[J]. Journal of Climate, 2012, 25(11): 3867-3893.
[14]	MURAKAMI H, VECCHI G A, UNDERWOOD S, et al. Simulation and prediction of category 4 and 5 hurricanes in the high-resolution GFDL HiFLOR coupled climate model[J]. Journal of Climate, 2015, 28(23): 9058-9079.
[15]	ROBERTS M J, CAMP J, SEDDON J, et al. Impact of model resolution on tropical cyclone simulation using the HighResMIP-PRIMAVERA multimodel ensemble[J]. Journal of Climate, 2020, 33(7): 2557-2583.
[16]	CAMARGO S J, ZEBIAK S E. Improving the detection and tracking of tropical cyclones in atmospheric general circulation models[J]. Weather and Forecasting, 2002, 17(6): 1152-1162.
[17]	HORN M, WALSH K, ZHAO M, et al. Tracking scheme dependence of simulated tropical cyclone response to idealized climate simulations[J]. Journal of Climate, 2014, 27(24): 9197-9213.
[18]	ZARZYCKI C M, ULLRICH P A. Assessing sensitivities in algorithmic detection of tropical cyclones in climate data[J]. Geophysical Research Letters, 2017, 44(2): 1141-1149.
[19]	WU T T, DUAN Z D. A new and efficient method for tropical cyclone detection and tracking in gridded datasets[J]. Weather and Climate Extremes, 2023, 42: 100626.
[20]	ZHAO M, HELD I M, LIN S J, et al. Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM[J]. Journal of Climate, 2009, 22(24): 6653-6678.
[21]	MURAKAMI H. Tropical cyclones in reanalysis data sets[J]. Geophysical Research Letters, 2014, 41(6): 2133-2141.
[22]	STRACHAN J, VIDALE P L, HODGES K, et al. Investi-gating global tropical cyclone activity with a hierarchy of AGCMs: The role of model resolution[J]. Journal of Climate, 2013, 26(1): 133-152.
[23]	HODGES K, COBB A, VIDALE P L. How well are tropical cyclones represented in reanalysis datasets?[J]. Journal of Climate, 2017, 30(14): 5243-5264.
[24]	TORY K J, CHAND S S, DARE R A, et al. The develop-ment and assessment of a model-, grid-, and basin-independent tropical cyclone detection scheme[J]. Journal of Climate, 2013, 26(15): 5493-5507.
[25]	WALSH K J E, FIORINO M, LANDSEA C W, et al. Objectively determined resolution-dependent threshold criteria for the detection of tropical cyclones in climate models and reanalyses[J]. Journal of Climate, 2007, 20(10): 2307-2314.
[26]	HODGES K I. A general method for tracking analysis and its application to meteorological data[J]. Monthly Weather Review, 1994, 122(11): 2573-2586.
[27]	KALNAY E, KANAMITSU M, KISTLER R, et al. The NCEP/NCAR 40-year reanalysis project[J]. Bulletin of the American Meteorological Society, 1996, 77(3): 437-471.
[28]	WRÓBEL-NIEDŹWIECKA I, DROZDOWSKA V, PISKOZUB J. Air-sea momentum flux climatologies: A review of drag relation for parameterization choice on wind stress in the North Atlantic and the European Arctic[J]. Ocean Science Discussions, 2018: 1-21.
[29]	SIMPSON R H, SAFFIR H. The hurricane disaster—Potential scale[J]. Weatherwise, 1974, 27(4): 169-186.
[30]	KNAFF J A, SAMPSON C R, DEMARIA M. An operational statistical typhoon intensity prediction scheme for the western north Pacific[J]. Weather and Forecasting, 2005, 20(4): 688-699.
[31]	KLOTZBACH P J, BELL M M, BOWEN S G, et al. Surface pressure a more skillful predictor of normalized hurricane damage than maximum sustained wind[J]. Bulletin of the American Meteorological Society, 2020, 101(6): 830-846.
[32]	KNUTSON T R, SIRUTIS J J, ZHAO M, et al. Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios[J]. Journal of Climate, 2015, 28(18): 7203-7224.
[33]	CHAVAS D R, REED K A, KNAFF J A. Physical under-standing of the tropical cyclone wind-pressure relationship[J]. Nature Communications, 2017, 8(1): 1360.
[34]	ZARZYCKI C M, ULLRICH P A, REED K A. Metrics for evaluating tropical cyclones in climate data[J]. Journal of Applied Meteorology and Climatology, 2021, 60(5): 643-660.
[35]	YU H, CHEN P Y, LI Q Q, et al. Current capability of operational numerical models in predicting tropical cyclone intensity in the western north Pacific[J]. Weather and Forecasting, 2013, 28(2): 353-367.
[36]	TAM H F, CHOY C W, WONG W K. Development of objective forecast guidance on tropical cyclone rapid intensity change[J]. Meteorological Applications, 2021, 28(2):e1981.
[37]	BOURDIN S, FROMANG S, DULAC W, et al. Intercom-parison of four algorithms for detecting tropical cyclones using ERA5[J]. Geoscientific Model Development, 2022, 15(17): 6759-6786.