Advertisement

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

  • Xiaoqin LuEmail author
  • Hui Yu
  • Xiaoming Yang
  • Xiaofeng Li
  • Jie Tang
Research Article

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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

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).

References

  1. Chen L S, Ding Y H (1979). General Features and Structure of Tropical Cyclones—An Introduction to theWestern Pacific Typhoon. Beijing: Science press, 1–45 (in Chinese)Google Scholar
  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)Google Scholar
  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)Google Scholar
  4. Dvorak V (1975). Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon Weather Rev, 103(5): 420–430CrossRefGoogle Scholar
  5. Dvorak V (1984). Tropical cyclone intensity analysis using satellite data. NOAA Technical Report, NESDIS 11, 47Google Scholar
  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 GuoW, Lu N M, Sun D L, and Shi C X, 1996, Beijing: Meteorological Press, 217–240 (in Chinese)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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): 413–417CrossRefGoogle Scholar
  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)CrossRefGoogle Scholar
  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)Google Scholar
  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)Google Scholar
  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): 104–112CrossRefGoogle Scholar
  14. Merrill R T (1984). A comparison of large and small tropical cyclones. Mon Weather Rev, 112(7): 1408–1418CrossRefGoogle Scholar
  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, USAGoogle Scholar
  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): 287–298CrossRefGoogle Scholar
  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): 89–91Google Scholar
  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): 5887–5898CrossRefGoogle Scholar
  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): 4533–4551CrossRefGoogle Scholar
  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): 431–434Google Scholar
  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: 353–385Google Scholar
  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.: 36–39Google Scholar
  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, 1–120 (In Chinese)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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): 1073–1082CrossRefGoogle Scholar
  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): 2013–2034CrossRefGoogle Scholar
  28. Willoughby H E (1978). A possible mechanism for the formation of hurricane rain bands. J Atmos Sci, 35(5): 838–848CrossRefGoogle Scholar
  29. Wimmers A J, Velden C S (2016). Advancements in objective multi satellite tropical cyclone center fixing. J Appl Meteorol Climatol, 55(1): 197–212CrossRefGoogle Scholar
  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, 650–653Google Scholar
  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): 49–59CrossRefGoogle Scholar
  32. Wong K Y, Yip C L, Li P W (2008). Automatic tropical cyclone eye fix using genetic algorithm. Expert Syst Appl, 34(1): 643–656CrossRefGoogle Scholar
  33. Wong K Y, Yip C L (2009). Identifying centers of circulating and spiral vector field patterns and its applications. Pattern Recognit, 42(7): 1371–1387CrossRefGoogle Scholar
  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): 9501–9506CrossRefGoogle Scholar
  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)Google Scholar
  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): 353–367CrossRefGoogle Scholar
  37. Yu H, Hu C M, Jiang LY (2006). Comparison of three tropical cyclone strength datasets. Acta Meteorol Sin, 64(3): 357–363 (in Chinese)Google Scholar
  38. Yurchak B S (2007). Description of cloud-rain bands in a tropical cyclone by a hyperbolic-logarithmic spiral. Russ Meteorol Hydrol, 32(1): 8–18CrossRefGoogle Scholar
  39. Zhang Q P, Lai L L, Sun WC (2005). Location of tropical cyclone center with intelligent image processing technique. ICMLC 2005. Lect Notes Artif Intell, 3930: 898–907Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xiaoqin Lu
    • 1
    Email author
  • Hui Yu
    • 1
  • Xiaoming Yang
    • 2
  • Xiaofeng Li
    • 3
    • 4
  • Jie Tang
    • 1
  1. 1.China Meteorological AdministrationShanghai Typhoon InstituteShanghaiChina
  2. 2.Ocean DepartmentShanghai Ocean UniversityShanghaiChina
  3. 3.CAS Key Laboratory of Ocean Circulation and Waves, Institute of OceanologyChinese Academy of SciencesQingdaoChina
  4. 4.Center for Ocean Mega-ScienceChinese Academy of SciencesQingdaoChina

Personalised recommendations