A new technique for automatically locating the center of tropical cyclones with multi-band cloud imagery

Abstract

A spiral cloud belt matching (SCBeM) technique is proposed for automatically locating the tropical cyclone (TC) center position on the basis of multi-band geo-satellite images. The technique comprises four steps: fusion of multi-band geo-satellite images, extraction of TC cloud systems, construction of a spiral cloud belt template (CBT), and template matching to locate the TC center. In testing of the proposed SCBeM technique on 97 TCs over the western North Pacific during 2012–2015, the median error (ME) was 50 km. An independent test of another 29 TCs in 2016 resulted in a ME of 54 km. The SCBeM performs better for TCs with intensity above “typhoon” level than it does for weaker systems, and is not suitable for use on high-latitude or landfall TCs if their cloud band formations have been destroyed by westerlies or by terrain. The proposed SCBeM technique provides an additional solution for automatically and objectively locating the TC center and has the potential to be applied conveniently in an operational setting. Intercomparisons between the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) and SCBeM methods using events from 2014 to 2016 reveal that ARCHER has better location accuracy. However, when IR imagery alone is used, the ME of SCBeM is 54 km, and in the case of low latitudes and low vertical wind shear the ME is 45–47 km, which approaches that of ARCHER (49 km). Thus, the SCBeM method is simple, has good time resolution, performs well and is a better choice for those TC operational agencies in the case that the microwave images, ASCAT, or other observations are unavailable.

Keywords

tropical cyclone / center location / geostationary satellite / matching technique

References

[1] Chen L S, Ding Y H (1979). General Features and Structure of Tropical Cyclones—An Introduction to the Western Pacific Typhoon. Beijing: Science press, 1–45 (in Chinese)

[2] Chen W M (2005). Application of Satellite Imagery in Tropical Weather Analysis and Prediction. Satellite meteorology (The second edition). Beijing: Meteorological press, 401–415 (in Chinese)

[3] Chen W M, Xiao W A (1988). Atlas of satellite cloud image Analysis, Atmospheric Physics department Technical Report. Nanjing Meteorology Institute, 11–17 (in Chinese)

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

[5] Dvorak V (1984). Tropical cyclone intensity analysis using satellite data. NOAA Technical Report, NESDIS 11, 47

[6] Dvorak V, Smigielski F (1993). Tropical Cyclone Center Location on Satellite Imagery, A Workbook on Tropical Clouds and Cloud Systems Observed in Satellite Imagery 1993, National Environmental Satellite, Data, and Information Service training materials. Translated by Guo W, Lu N M, Sun D L, and Shi C X, 1996, Beijing: Meteorological Press, 217–240 (in Chinese)

[7] Han Y, Wu R S (2007). On the spiral structure of typhoon. Journal of Nanjing University: (Nat Sci Ed), 6(43): 572–580 (in Chinese)

[8] Jin M (2008). Extraction and representation of cyclone image feature and typhoon center location. Dissertation for the Master’s Degree. Kunming: Tianjin, Tianjin University, (in Chinese)

[9] Lee R T, Lin J K (2001). An elastic contour matching model for tropical cyclone pattern recognition. IEEE Trans Syst Man Cybern B Cybern, 31(3): 413–417

[10] Liu K, Huang F, Luo J (2001). The study on automatic tracking method of typhoon spiral cloud bands. Computer Engineering, 27(10): 152–154 (in Chinese)

[11] Liu Z G, Lin K Y, Guo A M, Cheng Y (1997). The extraction of satellite cloud image feature. Research and Development of Computer, 9: 689–693 (in Chinese)

[12] Liu Z G, Zou L, Wu B (2003). The center location of eyed typhoon in satellite cloud image. Pattern Recognition and Artificial Intelligence, 16(3): 334–337 (in Chinese)

[13] Lu X Q, Yu H, Lei X T (2011). Statistics for size and radial wind profile of tropical cyclones in the western North Pacific. Acta Meteorol Sin, 25(1): 104–112

[14] Merrill R T (1984). A comparison of large and small tropical cyclones. Mon Weather Rev, 112(7): 1408–1418

[15] 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

[16] 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. Wea. Forceasting, 22(2): 287–298

[17] Qiang Z X, Peng J X, Wang H Q (2003). Remote sensing image fusion based on local deviation of wavelet transform. J Huazhong U Sci Tec, 31(6): 89–91

[18] Ren F M, 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

[19] Song J J, Klotzbach P J (2016). Wind structure discrepancies between two best track datasets for western north Pacific tropical cyclones. Mon Weather Rev, 144(12): 4533–4551

[20] Tao B J, Wang J R, Xu J P (2006). Study on image fusion based on different fusion rules of wavelet transform. Infrared technology, 28(7): 431–434

[21] Velden C, Harper B, Wells F, Beven J L II, Zehr R, Olander T, Mayfield M, Guard C, 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, 85: 353–385

[22] Velden C S, Olson W S, Roth B (1989). Tropical cyclone center-fixing using DMSP SSM/I data. In: 4th Conf. Sat. Meteor., San Diego, CA, Amer. Meteor. Soc.: 36–39

[23] Wang F N (2006). The researches of segmentation and central location of typhoon cloud department based on satellite cloud image. Dissertation for the Master’s Degree. Kunming: Yunnan Normal University, 1–20 (In Chinese)

[24] Wang Y Y, Ye Z, Sun Y C (2002). Typhoon center locating using rotation feature matching method. Journal of Image and Graphics, 7A(5): 491–494 (in Chinese)

[25] Wei K, Li Y X, Jing Z L, Liang X D, Lu X Q (2009). Typhoon cloud pattern discovery based on SOM clustering. Infrared, 30(12): 16–24 (in Chinese)

[26] William T C, Elsberry R L, Chan C L (1987). An objective technique for estimating present tropical cyclone locations. Mon Weather Rev, 115(6): 1073–1082

[27] Wimmers A J, Velden C S (2010). Objectively determining the rotational center of tropical cyclones in passive microwave satellite imagery. J Appl Meteorol Climatol, 49(9): 2013–2034

[28] Willoughby H E (1978). A possible mechanism for the formation of hurricane rain bands. J Atmos Sci, 35(5): 838–848

[29] Wimmers A J, Velden C S (2016). Advancements in objective multi satellite tropical cyclone center fixing. J Appl Meteorol Climatol, 55(1): 197–212

[30] Wong K Y, Yip C L, Li P W, Wan T W (2004). Automatic template matching method for tropical cyclone eye fix. In: Proc. 17th Int. Conf. Pattern Recognition (ICPR’04), Cambridge, United Kingdom, 650–653

[31] Wong K Y, Yip C L, Li P W (2007). A novel algorithm for automatic tropical cyclone eye fix using Doppler radar data. Meteorol Appl, 14(1): 49–59

[32] Wong K Y, Yip C L, Li P W (2008). Automatic tropical cyclone eye fix using genetic algorithm. Expert Syst Appl, 34(1): 643–656

[33] Wong K Y, Yip C L (2009). Identifying centers of circulating and spiral vector field patterns and its applications. Pattern Recognit, 42(7): 1371–1387

[34] Wu L G, Tian W, Liu Q Y, Cao J, Knaff J A (2015). Implications of the observed relationship between tropical cyclone size and intensity over the western North Pacific. J Clim, 28(24): 9501–9506

[35] Xie J Y, Ai Z Y, Gao Y (1997). Automatic recognition algorithm of spiral curve in position typhoon center. J Softw, 8(6): 398–403 (in Chinese)

[36] Yu H, Chen P Y, Li Q Q, Tang B (2013). Current capability of operational numerical models in predicting tropical cyclone intensity in the western north Pacific. Wea Forceasting, 28(2): 353–367

[37] Yu H, Hu C M, Jiang L Y (2006). Comparison of three tropical cyclone strength datasets. Acta Meteorol Sin, 64(3): 357–363 (in Chinese)

[38] Yurchak B S (2007). Description of cloud-rain bands in a tropical cyclone by a hyperbolic-logarithmic spiral. Russ Meteorol Hydrol, 32(1): 8–18

[39] Zhang Q P, Lai L L, Sun W C (2005). Location of tropical cyclone center with intelligent image processing technique. ICMLC 2005. Lect Notes Artif Intell, 3930: 898–907

Acknowledgments

The CMA and JTWC best track archives were obtained from Typhoon Online website and NDBC website, respectively. The real-time archives of ARCHER and ADT were downloaded from SSEC. WISC website. This study was supported by the Key Projects of the National Key R&D Program (No. 2018YFC1506300), the National Basic Research Program of China (No. 2015CB452806), and the Key Program for International S&T Cooperation Projects of China (No. 2017YFE0107700), the Natural Science Foundation of Shanghai (No. 15ZR1449900), and the National Natural Science Foundation of China (Nos. 41675116, 41575046, 41775065, and 41405060).

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