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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Atkinson G D (1974). Investigation of gust factors in tropical cyclones. FLEWEACENTech. Note JTWC 74-1, Fleet Weather Center, Guam
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
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
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
China Meterological Administration (2012). Typhoon Operational Performance and Service Regulations. Beijing: China Meteorological Press (in Chinese)
Dvorak V F (1975). Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon Weather Rev, 103(5): 420–430
Dvorak V F (1984). Tropical cyclone intensity analysis using satellite data. NOAA/NESDIS Tech. Rep. 11
Group of Satellite Imagery Analysis (1980a). Methods for typhoon prediction using the satellite imagery (I). Meteorological Monthly, 6: 24–26 (in Chinese)
Group of Satellite Imagery Analysis (1980b). Methods for typhoon prediction using the satellite imagery (II). Meteorological Monthly, 6: 25–27 (in Chinese)
Kang N Y, Elsner J B (2012). Consensus on climate trends in western north pacific tropical cyclones. J Clim, 25(21): 7564–7573
Knaff J A, Zehr R M (2007). Reexamination of tropical cyclone wind-pressure relationships. Weather Forecast, 22(1): 71–88
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
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
Kossin J P (2015). Hurricane wind-pressure relationship and eyewall replacement cycles. Weather Forecast, 30(1): 177–181
Liu Y M (2015). Application of inner verification sequence alignment model to two data source splicing of AWS hourly precipitation. Meteorological Monthly, 41: 1398–1407
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
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
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
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
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
OSI SAF (2019). ASCAT Wind Product User Manual
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
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
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
U.S. Fleet Weather Facility (2007). Annual Tropical Storm Report. U.S. Fleet Weather Facility, Miami, FL
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
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
Xu Y L, Zhang L, Xiang C Y (2015). Typhoon intensity estimation technique and its operational application: with example of Dvorak technique. Advances in Met S & T, 5: 22–34 (in Chinese)
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
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
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
Yu H, Hu C, Jiang L (2007). Comparison of three tropical cyclone strength datasets. Acta Meteorol Sin, 64: 357–363
Yu H, Chen L S (2019). Impact assessment of landfalling tropical cyclones: introduction to the special issue. Front Earth Sci, 13(4): 669–671
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
Acknowledgements
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bai, L., Tang, J., Guo, R. et al. Quantifying interagency differences in intensity estimations of Super Typhoon Lekima (2019). Front. Earth Sci. 16, 5–16 (2022). https://doi.org/10.1007/s11707-020-0866-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11707-020-0866-5