Quantifying interagency differences in intensity estimations of Super Typhoon Lekima (2019)

Abstract

There were significant discrepancies in the intensity estimations of Super Typhoon Lekima (2019) among the China Meteorological Administration (CMA), the United States Joint Typhoon Warning Center (JTWC), and the Japan Meteorological Agency (JMA) data sets, with a maximum difference of over 12 m/s and 16 m/s between the JTWC data set and the CMA and JMA data sets, respectively. During the intensification phase, disagreement on the maximum sustained wind (MSW) between these agencies was due to the use of different conversion tables for the current intensity number (CI) estimated by Dvorak technique-MSW. In addition, CI discrepancies and different available observational data were also important contributors to the different intensities estimated during the Lekima’s decay phase before landfall. The ability of various methods to minimize these discrepancies was evaluated in this study. Both the linear factor multiplication method and the remapping method using the same CI-MSW conversion table have substantially abilities to reduce intensity discrepancies, with the latter method being more effective. However, these improvements only hold for the intensification phase in the ocean. The CMA data set had more complete and accurate intensity estimations when Lekima made landfall in China. After its landfall, the intensity estimate of the CMA was comparable to that of the JMA, which differed greatly from that of the JTWC.

Graphical abstract

References

[1] Atkinson G D (1974). Investigation of gust factors in tropical cyclones. FLEWEACENTech. Note JTWC 74–1, Fleet Weather Center, Guam

[2] Atkinson G D, Holliday C R (1977). Tropical cyclone minimum sea level pressure/maximum sustained wind relationship for the western North Pacific. Mon Wea Rev, 105: 421–527

[3] Bai L, Yu H, Black P G, Xu Y, Ying M, Tang J, Guo R (2019). Reexamination of the tropical cyclone wind–pressure relationship based on pre-1987 aircraft data in the western North Pacific. Weather Forecast, 34(6): 1939–1954

[4] Barcikowska M, Feser F, von Storch H (2012). Usability of best track data in climate statistics in the western North Pacific. Mon Weather Rev, 140(9): 2818–2830

[5] China Meterological Administration (2012). Typhoon Operational Performance and Service Regulations. Beijing: China Meteorological Press (in Chinese)

[6] Dvorak V F (1975). Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon Weather Rev, 103(5): 420–430

[7] Dvorak V F (1984). Tropical cyclone intensity analysis using satellite data. NOAA/NESDIS Tech. Rep. 11

[8] Group of Satellite Imagery Analysis (1980a). Methods for typhoon prediction using the satellite imagery (I). Meteorol Mon, 6: 24–26 (in Chinese)

[9] Group of Satellite Imagery Analysis (1980b). Methods for typhoon prediction using the satellite imagery (II). Meteorol Mon, 6: 25–27 (in Chinese)

[10] Kang N Y, Elsner J B (2012). Consensus on climate trends in western north pacific tropical cyclones. J Clim, 25(21): 7564–7573

[11] Knaff J A, Zehr R M (2007). Reexamination of tropical cyclone wind–pressure relationships. Weather Forecast, 22(1): 71–88

[12] Knapp K R, Kruk M C (2010). Quantifying interagency differences in tropical cyclone best-track wind speed estimations. Mon Weather Rev, 138(4): 1459–1473

[13] Koba H, Hagiwara T, Osano S, Akashi S (1991). Relationships between CI number and minimum sea level pressure/maximum wind speed of tropical cyclones. Geophys Mag, 44: 15–25

[14] Kossin J P (2015). Hurricane wind–pressure relationship and eyewall replacement cycles. Weather Forecast, 30(1): 177–181

[15] Liu Y M (2015). Application of inner verification sequence alignment model to two data source splicing of AWS hourly precipitation. Meteorol Mon, 41: 1398–1407

[16] Lu X Q, Yu H (2013). An objective tropical cyclone intensity estimation model based on digital IR satellite images. Trop Cyclone Res Rev, 2: 233–241

[17] Lu X Q, Yu H, Yang X M, Li X F, Tang J (2019). A new technique for automatically locating the center of tropical cyclones with multi-band cloud imagery. Front Earth Sci, 13(4): 836–847

[18] Nolan D S, Zhang J A, Uhlhorn E W (2014). On the limits of estimating the maximum wind speeds in hurricanes. Mon Weather Rev, 142(8): 2814–2837

[19] Olander T L, Velden C S, Kossin J P (2004). The advanced objective dvorak technique (AODT)-continuing the journey. In: 26th AMS Conf. on Hurricane and Tropical Meteorology, Miami, FL, USA

[20] Olander T L, Velden C S (2007). The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Weather Forecast, 22(2): 287–298

[21] OSI SAF (2019). ASCAT Wind Product User Manual

[22] Ren F, Liang J, Wu G, Dong W, Yang X (2011). Reliability analysis of climate change of tropical cyclone activity over the western North Pacific. J Clim, 24(22): 5887–5898

[23] Sivareddy S, Ravichandran M, Girishkumar M S (2013). Evaluation of ASCAT-Based daily gridded winds in the tropical Indian Ocean. J Atmos Ocean Technol, 30(7): 1371–1381

[24] Song J J, Wang Y, Wu L (2010). Trend discrepancies among three best track datasets of western North Pacific tropical cyclones. J Geophys Res, 115(D12): D12128

[25] U.S. Fleet Weather Facility (2007). Annual Tropical Storm Report. U.S. Fleet Weather Facility, Miami, FL

[26] Velden C, Harper B, Wells F, Beven J L II, Zehr R, Olander T, Mayfield M, Guard C C H I P, Lander M, Edson R, Avila L, Burton A, Turk M, Kikuchi A, Christian A, Caroff P, McCrone P (2006). The Dvorak tropical cyclone intensity estimation technique: a satellite-based method that has endured for over 30 years. Bull Am Meteorol Soc, 87(9): 1195–1210

[27] Wu M C, Yeung K H, Chang W L (2006). Trends in western North Pacific tropical cyclone intensity. Eos (Wash DC), 87(48): 537–538

[28] Xu Y L, Zhang L, Xiang C Y (2015). Typhoon intensity estimation technique and its operational application: with example of Dvorak technique. Adv Met S & T, 5: 22–34 (in Chinese)

[29] Yeung K H (2006). Issues related to global warming—myths, realities and warnings. In: the 5th Conference on Catastrophe in Asia, Hong Kong Obs, Hong Kong, China

[30] Ying M, Cha E, Kwon H (2011). Comparison of three western North Pacific tropical cyclone best track datasets in a seasonal context. J Meteor Soc Japan, 89(3): 211–224

[31] Ying M, Zhang W, Yu H, Lu X, Feng J, Fan Y, Zhu Y, Chen D (2014). An overview of the China Meteorological Administration tropical cyclone database. J Atmos Ocean Technol, 31(2): 287–301

[32] Yu H, Hu C, Jiang L (2007). Comparison of three tropical cyclone strength datasets. Acta Meteorol Sin, 64: 357–363

[33] Yu H, Chen L S (2019). Impact assessment of landfalling tropical cyclones: introduction to the special issue. Front Earth Sci, 13(4): 669–671

[34] Zehr R (1989). Improved objective satellite estimates of tropical cyclone intensity. In: 18th Conf on Hurricanes and Tropical Meteorology, San Diego, CA. Amer Meteor Soc, J25–J28

Acknowledgments

We thank the meteorological observers, forecasters, and experts from within and outside of CMA, who were and are devoted to the construction of the database, for their hard work on data collection, processing, and analysis. This research was supported jointly by the Key Program for International S&T Cooperation Projects of China (No.2017YFE0107700), and the National Natural Science Foundation of China (Grant Nos. 41875080 and 42075056).

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