A kernel density estimation approach of North Indian Ocean tropical cyclone formation and the association with convective available potential energy and equivalent potential temperature

Meteorology and Atmospheric Physics volume 132, pages603–612(2020)Cite this article

Abstract

Tropical cyclone (TC) is the one of the most devastating weather systems which causes enormous loss of life and property in the coastal regions of North Indian Ocean (NIO) rim countries. TC modelling can help decision-makers and inhabitants in shoreline zones to take necessary planning and actions in advance. To model TC activity, it is essential to know the factors that effect TC activities. The formation of tropical cyclones in the NIO basin is significantly modulated by Convective Available Potential Energy (CAPE) and Equivalent Potential Temperature (EPT). In this paper, a kernel density estimation approach (KDE) has been developed and evaluated to determine the extent of this modulation for the period 1979–2016. The distribution of genesis was defined by the KDE approach and validated by both classical and standard plug-in estimators. Results suggest a strong correlation of TC genesis densities with CAPE in the month of October–November (post-monsoon season) followed by the month of April–May (pre-monsoon season). Findings indicate the potential for predicting TC activities in the NIO well before the TC season.

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Reproduced from Wahiduzzaman et al. (2017)

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References

  1. Alam E, Collins AE (2010) Cyclone disaster vulnerability and response experiences in coastal Bangladesh. Disasters 34(4):931–954

    Article  Google Scholar 

  2. Alam MM, Hossain MA, Shafee S (2003) Frequency of Bay of Bengal cyclonic storms and depressions crossing different coastal zones. Intl J Climatol 23(9):1119–1125

    Article  Google Scholar 

  3. Balaguru K, Taraphdar S, Leung LR, Foltz GR (2014) Increase in the intensity of postmonsoon Bay of Bengal tropical cyclones. Geophys Res Lett 41(10):3594–3601

    Article  Google Scholar 

  4. Bashtannyk DM, Hyndman RJ (2001) Bandwidth selection for kernel conditional density estimation. Comp Stat Data Anal 36:279–298

    Article  Google Scholar 

  5. Casson E, Coles S (2000) Simulation and extremal analysis of hurricane events. J R Stat Soc Ser C Appl Stat 49(2):227–245

    Article  Google Scholar 

  6. Camargo SJ, Emanuel KA, Sobel AH (2007) Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J Clim 20:4819–4834

    Article  Google Scholar 

  7. Camargo SJ (2013) Global and regional aspects of tropical cyclone activity in the CMIP5 models. J Clim 26(24):9880–9902

    Article  Google Scholar 

  8. Camp J, Roberts M, Maclachlan C, Wallace E, Hermanson L, Brookshaw A, Arribas A, Scaife AA (2015) Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system. QJRMS 141(691):2206–2219

    Article  Google Scholar 

  9. Chand SS, Walsh KJE (2012) Modeling seasonal tropical cyclone activity in the Fiji region as a binary classification problem. J Clim 25(14):5057–5071

    Article  Google Scholar 

  10. Doblas-Reyes FJ, García-Serrano J, Lienert F, Biescas AP, Rodrigues LRL (2013) Seasonal climate predictability and forecasting: status and prospects. Wiley Interdiscip Rev 4(4):245–268

    Google Scholar 

  11. Doswell CA, Rasmussen EN (1994) The effect of neglecting the virtual temperature correction on CAPE calculations. Weather Forecast 9(4):625–629

    Article  Google Scholar 

  12. Emanuel K, Ravela S, Vivant E, Risi C (2006) A statistical deterministic approach to hurricane risk assessment. Bull Am Meteorol Soc 87(3):299–314

    Article  Google Scholar 

  13. Girishkumar MS, Ravichandran M (2012) The influences of ENSO on tropical cyclone activity in the Bay of Bengal during October–December. J Geoph Res 117(C2):2033

    Article  Google Scholar 

  14. Hall TM, Jewson S (2007) Statistical modelling of North Atlantic tropical cyclone tracks. Tellus A 59A(4):486–498

    Article  Google Scholar 

  15. Islam T, Peterson RE (2009) Climatology of landfalling tropical cyclones in Bangladesh 1877–2003. Nat Hazards 48(1):115–135

    Article  Google Scholar 

  16. Jagger TH, Elsner JB (2009) Modeling tropical cyclone intensity with quantile regression. I J Climatol 29(10):1351–1361

    Article  Google Scholar 

  17. James MK, Mason LB (2005) Synthetic tropical cyclone database. J Waterw Port Coast Ocean Eng 131(4):181–192

    Article  Google Scholar 

  18. Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–470

    Article  Google Scholar 

  19. Knapp KR, Kruk MC, Levinson DH, Diamond HJ, Neumann CJ (2010) The international best track archive for climate stewardship (IBTrACS). Bull Am Meteorol Soc 91(3):363–376

    Article  Google Scholar 

  20. Loader CR (1999) Bandwidth selection: classical or plug-in? Ann Stat 27(2):415–438

    Article  Google Scholar 

  21. Lin II, Chen CH, Pun IF, Liu WT, Wu CC (2009) Warm ocean anomaly, air sea fluxes, and the rapid intensification of tropical cyclone Nargis (2008). Geophys Res Lett. https://doi.org/10.1029/2008GL035815

    Article  Google Scholar 

  22. Marguerite L, Frisius T (2018) On the role of convective available potential energy (CAPE) in tropical cyclone intensification. Tellus A 70(1):1433433

    Google Scholar 

  23. Mohapatra M, Bandyopadhyay BK, Tyagi A (2014) Status and plans for operational tropical cyclone forecasting and warning systems in the North Indian Ocean region. In: Mohanty MM, Singh OP, Bandyopadhyay BK, Rathore LS (eds) Monitoring and prediction of tropical cyclones in the Indian Ocean and climate change. Springer Netherlands and Capital Publishing Company, New Delhi, pp 149–162

    Google Scholar 

  24. Nath S, Kotal SD, Kundu PK (2015) Seasonal prediction of tropical cyclone activity over the North Indian Ocean using the neural network model. Atmosfera 28(4):271–281

    Article  Google Scholar 

  25. Ng EKW, Chan JCL (2012) Interannual variations of tropical cyclone activity over the north Indian Ocean. Intl J Climatol 32(6):819–830

    Article  Google Scholar 

  26. Nolan DS, McGauley MG (2011) Tropical cyclogenesis in wind shear: climatological relationships and physical processes. In: Oouchi K, Fudeyasu H (eds) Cyclones: formation, triggers, and control. Nova Science Publishers, Happauge

    Google Scholar 

  27. Pattanaik DR, Mohapatra M (2016) Seasonal forecasting of tropical cyclogenesis over the North Indian Ocean. J Earth Syst Sci 125(2):231–250

    Article  Google Scholar 

  28. Rajasekhar M, Kishtawal CM, Prasad MYS, Seshagiri Rao V, Rajeevan M (2014) Extended range tropical cyclone predictions for East Coast of India. In: Monitoring and prediction of tropical cyclones in the Indian Ocean and climate change. pp 137–148

  29. Rumpf J, Weindl H, Höppe P, Rauch E, Schmidt V (2007) Stochastic modelling of tropical cyclone tracks. Math Methods Oper Res 66(3):475–490

    Article  Google Scholar 

  30. Stull RB (1988) An introduction to boundary layer meteorology, vol 13. Kluwer Academic Publishers, Springer Netherlands, p666

  31. Tippett MK, Camargo SJ, Sobel AH (2011) A poisson regression index for tropical cyclone genesis and the role of large-scale vorticity in genesis. J Clim 24:2335–2357

    Article  Google Scholar 

  32. Turlach BA (1993) Bandwidth selection in kernel density estimation: a review. CORE Inst Stat 19:1–33

    Google Scholar 

  33. Tyagi A, Bandyopadhyay BK, Mohapatra M (2010) Monitoring and prediction of cyclonic disturbances over North Indian Ocean by regional specialised meteorological centre, New Delhi (India):problems and prospective. Indian Ocean tropical cyclones and climate change. Springer Nature, Switzerland, pp 93–103

    Google Scholar 

  34. Vickery PJ, Skerjl P, Steckley AC, Twinsdale L (2000) Simulation of hurricane risk in the united states using an empirical storm track modeling technique. J Struct Eng 126:1222–1237

    Article  Google Scholar 

  35. Vissa NK, Satyanarayana ANV, Prasad Kumar B (2013) Intensity of tropical cyclones during pre- and post-monsoon seasons in relation to accumulated tropical cyclone heat potential over Bay of Bengal. Nat Hazards 68(2):351–371

    Article  Google Scholar 

  36. Wahiduzzaman M, Oliver ECJ, Wotherspoon SJ, Holbrook NJ (2017) A climatological model of North Indian Ocean tropical cyclone genesis, tracks and landfall. Clim Dyn 49:2585–2603

    Article  Google Scholar 

  37. Wahiduzzaman M, Oliver ECJ, Klotzbach PJ, Wotherspoon SJ, Holbrook NJ (2019) A statistical seasonal forecast model of North Indian Ocean tropical cyclones using the quasi-biennial oscillation. Intl J Climatol 39(2):934–952

    Article  Google Scholar 

  38. Wahiduzzaman M, Alea Y (2019) Statistical forecastiong of tropical cyclones landfall activities over the North Indian Ocean rim countries. Atmos Res 227:89–100

    Article  Google Scholar 

  39. Watterson IG, Evans JL, Ryan BF (1995) Seasonal and interannual variability of tropical cyclogenesis: diagnostics from large-scale fields. J Clim 8:3052–3066

    Article  Google Scholar 

  40. Webster PJ (2008) Myanmar’s deadly daffodil. Nat Geosci 1(8):488–490

    Article  Google Scholar 

  41. Wheeler DC, Páez A (2010) Geographically weighted regression. In: Fischer M, Getis A (eds) Handbook of Applied Spatial Analysis. Springer, Berlin, Heidelberg, pp 461–486

    Google Scholar 

  42. Yonekura E, Hall TM (2011) A statistical model of tropical cyclone tracks in the western North Pacific with ENSO-dependent cyclogenesis. J Appl Meteorol Climatol 50(8):1725–1739

    Article  Google Scholar 

  43. Zhang Y, Wang HJ, Sun JQ, Drange H (2010) Changes in the tropical cyclone genesis potential index over the Western North Pacific in the SRES A2 scenario. Adv Atmosp Sci 27(6):1246–1258

    Article  Google Scholar 

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Acknowledgements

Authors would like to sincerely thank Dr. Savin Chand (Senior Lecturer, Federation University, Australia); Dr. Stephanie Fielder (Editor, Meteorology and Atmospheric Physics) and two anonymous reviewers for their helpful discussion and constructive review.

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Affiliations

  1. Institute for Climate and Application Research (ICAR), Nanjing University of Information Science and Technology (NUIST), Nanjing, China

    Md Wahiduzzaman

  2. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, TAS, Australia

    Md Wahiduzzaman

  3. The Satellite Positioning for Atmosphere, Climate and Environment (SPACE) Research Centre, RMIT University, Melbourne, Australia

    Alea Yeasmin

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Wahiduzzaman, M., Yeasmin, A. A kernel density estimation approach of North Indian Ocean tropical cyclone formation and the association with convective available potential energy and equivalent potential temperature. Meteorol Atmos Phys 132, 603–612 (2020). https://doi.org/10.1007/s00703-019-00711-7

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