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Structural characteristics of North Indian Ocean tropical cyclones during 1999–2017: a scatterometer observation-based analysis

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Abstract

The climatology and numerical modeling of tropical cyclones (TCs) both are the primary focus area of research over the North Indian Ocean (NIO); however, the size determination and variation are a less emphasized topic. Therefore, the present study deals with the determination of the size of TCs over NIO mainly by following the vorticity-based approach and using scatterometer data sets. It assesses the ability of this data set to determine the size of the NIO TCs. Different parameters such as Rmax, Rvor, R34 (i.e., the radius of 34 knots winds; 1 knot = 0.514444 ms−1), R50 (i.e., the radius of 50 knots winds), and R64 (i.e., the radius of 64 knots winds) are determined for the said purpose. The error between TC center determined by the vorticity-based approach and India Meteorological Department (IMD) data is in the range of 30–105 km (mean value is 76.34 km) and with Joint Typhoon Warning Centre (JTWC) is within 25–115 km (mean value 79.77 km) when all of the observations are considered. However, if the observations with the smallest time gap of < 30 min are considered, the corresponding mean error values become 30.12 km (± 20.83 km) and 36.47 km (± 20.21 km). The comparison of Rmax with JTWC observation is having a correlation value of ~ 0.75 with RMSE of ~ 7.6 km. The radial parameter Rmax varies in the range of 15–90 km, R64 varies within 15–60 km, R34 lies in the range of 80–300 km, R50 varies within 15–60 km, and Rvor is within 190–485 km for the NIO region that is comparable with the JTWC data. Larger size TCs are observed near the higher latitude of the NIO region, and the size of systems increases with an increase in latitude. The size of the pre-monsoon season TCs is found to be larger in comparison to post-monsoon season systems over NIO. An empirical relationship is also established based on the linear regression method to determine the size of TCs using SCATSAT-1 data. The results presented in this work indicate that scatterometer wind data with the spatial resolution of ~ 6 to 25 km would be handy enough to study the structural characteristics of NIO TCs.

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References

  • Anthes RA (1981) Tropical cyclones: structure, computer simulation models, and operational procedure. Contemp Phys 22:643–680

    Article  Google Scholar 

  • Carrasco CA, Landsea CW, Lin YL (2014) The influence of tropical cyclone size on its intensification. Weather Forecast 29(3):582–590

    Article  Google Scholar 

  • Chan KTF, Chan JCL (2012) Size and strength of tropical cyclones as inferred from QuikSCAT data. Mon Weather Rev 140:811–824

    Article  Google Scholar 

  • Chan KTF, Chan JCL (2014) Impacts of initial vortex size and planetary vorticity on tropical cyclone size. Q J R Meteorol Soc 140:2235–2248. https://doi.org/10.1002/qj.2292

    Article  Google Scholar 

  • Chan KT, Chan JC (2015) Global climatology of tropical cyclone size as inferred from QuikSCAT data. Int J Climatol 35(15):4843–4848

    Article  Google Scholar 

  • Chavas DR, Emanuel KA (2010) A QuikSCAT climatology of tropical cyclone size. Geophys Res Lett 37(18):L18816. https://doi.org/10.1029/2010

    Article  Google Scholar 

  • Chavas DR, Lin N, Dong W, Lin Y (2016) Observed tropical cyclone size revisited. J Clim 29(8):2923–2939

    Article  Google Scholar 

  • Chen JL, Penhune VB, Zatorre RJ (2008) Listening to musical rhythms recruits motor regions of the brain. Cereb Cortex 18(12):2844–2854

    Article  Google Scholar 

  • Cocks SB, Gray WM (2002) Variability of the outer wind profiles of western North Pacific typhoons: classifications and techniques for analysis and forecasting. Mon Weather Rev 130:1989–2005

    Article  Google Scholar 

  • Dean L, Emanuel KA, Chavas DR (2009) On the size distribution of Atlantic tropical cyclones. Geophys Res Lett 36(14):L14803. https://doi.org/10.1029/2009

    Article  Google Scholar 

  • Demuth JL, DeMaria M, Knaff JA (2006) Improvement of advanced microwave sounding unit tropical cyclone intensity and size estimation algorithms. J Appl Meteorol Climatol 45:1573–1581

    Article  Google Scholar 

  • Dvorak VF (1975) Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon Weather Rev 103:420–430

    Article  Google Scholar 

  • Dvorak VF (1984) Tropical cyclone intensity analysis using satellite data. NOAA TechRep NESDIS 11:1–47

    Google Scholar 

  • Gross JM, DeMaria M, Knaff JA, Sampson CR (2004) A new method for determining tropical cyclone wind forecast probabilities. Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 425–426

  • Harr PA, Kalafsky MS, Elsberry RL (1996) Environmental conditions prior to formation of a midget tropical cyclone during TCM-93. Mon Weather Rev 124:1693–1710

    Article  Google Scholar 

  • Holland GJ (1980) An analytical model of the wind and pressure profiles in hurricanes. Mon Weather Rev 108:1212–1218

    Article  Google Scholar 

  • Holland WR, Rhines PB (1980) An example of eddy-induced ocean circulation. J Phys Oceanogr 10(7):1010–1031

    Article  Google Scholar 

  • Hsu SA, Babin A (2005) Estimating the radius of maximum wind via satellite during Hurricane Lili (2002) over the Gulf of Mexico. Natl Weather Assoc Electron J 6(3):1–6

    Google Scholar 

  • Jaiswal N, Kishtawal CM (2016) Structural analysis of tropical cyclone using INSAT-3D observations. Remote Sens Atmos Clouds Precipitation VI 9876:987611. https://doi.org/10.1117/12.2223508

    Article  Google Scholar 

  • Jaiswal N, Kishtawal CM (2018) Prediction of tropical cyclogenesis using scatterometer data. IEEE Trans Geosci Remote Sens 49:12

    Google Scholar 

  • Jaiswal N, Kishtawal CM, Pal PK (2012) Cyclone intensity estimation using similarity of satellite IR images based on histogram matching approach. Atmos Res 118:215–221

    Article  Google Scholar 

  • Jaiswal N, Ha DTT, Kishtawal CM (2019) Estimation of size of tropical cyclones in the North Indian Ocean using Oceansat-2 scatterometer high-resolution wind products. Theor and Appl Clim 136(1-2):45–53

    Article  Google Scholar 

  • Knaff JA, Zehr RM (2004) A consensus forecast for tropical cyclone gale wind Radii. Weather Forecast 22:71–88

    Article  Google Scholar 

  • Knaff JA, Zehr RM (2007) Reexamination of tropical cyclone wind–pressure relationships. Weather Forecast 22(1):71–88

    Article  Google Scholar 

  • Knaff JA, Sampson CR, DeMaria M, Marchok TP, Gross JM, McAdie CJ (2007) Statistical tropical cyclone wind radii prediction using climatology and persistence. Weather Forecast 22(4):781–791

    Article  Google Scholar 

  • Kossin JP, Knapp KR, Vimont DJ, Murnane RJ, Harper BA (2007) A globally consistent reanalysis of hurricane variability and trends. Geophys Res Lett 34(4):L04815. https://doi.org/10.1029/2006GL028836

    Article  Google Scholar 

  • Lee CS, Cheung KK, Fang WT, Elsberry RL (2010) Initial maintenance of tropical cyclone size in the western North Pacific. Mon Weather Rev 138(8):3207–3223

    Article  Google Scholar 

  • Liu KS, Chan JC (1999) Size of tropical cyclones as inferred from ERS-1 and ERS-2 data. Mon Weather Rev 127(12):2992–3001

    Article  Google Scholar 

  • Merrill RT (1984) A comparison of large and small tropical cyclones. Mon Weather Rev 112(7):1408–1418

    Article  Google Scholar 

  • Miller BI (1958) On the maximum intensity of hurricanes. J Meteorol 15(2):184–195

    Article  Google Scholar 

  • Mohanty UC, Niyogi D, Potty KV (2012) Recent developments in tropical cyclone analysis using observations and high resolution models

  • Mohapatra M, Sharma M (2015) Characteristics of surface wind structure of tropical cyclones over the North Indian Ocean. J Earth Syst Sci 124(7):1573–1598

    Article  Google Scholar 

  • Mok DK, Chan JC, Chan KT (2018) A 31-year climatology of tropical cyclone size from the NCEP Climate Forecast System Reanalysis. Int J Climatol 38:e796–e806

    Article  Google Scholar 

  • Mueller KJ, DeMaria M, Knaff J, Kossin JP, Vonder Haar TH (2006) Objective estimation of tropical cyclone wind structure from infrared satellite data. Weather Forecast 21(6):990–1005

    Article  Google Scholar 

  • Panda J, Giri RK (2012) A comprehensive study of surface and upper-air characteristics over two stations on the west coast of India during the occurrence of a cyclonic storm. Nat Hazards 64(2):1055–1078

    Article  Google Scholar 

  • Panda J, Giri RK, Patel KH, Sharma AK, Sharma RK (2011) Impact of satellite derived winds and cumulus physics during the occurrence of the tropical cyclone Phyan. Indian J Sci Technol 4(8):859–875

    Article  Google Scholar 

  • Panda J, Singh H, Wang PK, Giri RK, Routray A (2015) A qualitative study of some meteorological features during tropical cyclone PHET using satellite observations and WRF modeling system. J Indian Soc Remote Sens 43(1):45–56

    Article  Google Scholar 

  • Singh K, Panda J, Osuri KK, Vissa NK (2016) Progress in tropical cyclone predictability and present status in the North Indian Ocean region. In: Lupo AR (ed) Recent developments in tropical cyclone dynamics, prediction, and detection, vol 193. InTech Publishers, Rijeka, p 1. https://doi.org/10.5772/64333

    Chapter  Google Scholar 

  • Singh K, Panda J, Rath SS (2019a) Variability in landfalling trends of cyclonic disturbances over North Indian Ocean region during current and pre-warming climate. Theor Appl Climatol 137(1-2):417–439. https://doi.org/10.1007/s00704-018-2605-3

    Article  Google Scholar 

  • Singh K, Panda J, Sahoo M, Mohapatra M (2019b) Variability in tropical cyclone climatology over North Indian Ocean during the period 1891 to 2015. Asia-Pac J Atmos Sci 55(2):269–287. https://doi.org/10.1007/s13143-018-0069-0

    Article  Google Scholar 

  • Stiles BW, Danielson RE, Poulsen WL, Brennan MJ, Hristova-Veleva S, Shen TP, Fore AG (2014) Optimized tropical cyclone winds from QuikSCAT: A neural network approach. IEEE Trans Geosci Remote Sens 52(11):7418–7434

    Article  Google Scholar 

  • Takagi H, Wu W (2016) Maximum wind radius estimated by the 50 kt radius: improvement of storm surge forecasting over the western North Pacific. Nat Hazards Earth Syst Sci 16(3):705–717

    Article  Google Scholar 

  • Takagi H, Li S, de Leon M, Esteban M, Mikami T, Matsumaru R, Shibayama T, Nakamura R (2016) Storm surge and evacuation in urban areas during the peak of a storm. Coast Eng 108:1–9

    Article  Google Scholar 

  • Urban K (2001) Wavelet bases in H (div) and H (curl). Math Comput 70(234):739–766

    Article  Google Scholar 

  • Weissmann M, Harnisch F, Wu CC, Lin PH, Ohta Y, Yamashita K, Kim YH, Jeon EH, Nakazawa T, Aberson S (2011) The influence of assimilating dropsonde data on typhoon track and midlatitude forecasts. Mon Weather Rev 139(3):908–920

    Article  Google Scholar 

  • Willoughby HE, Rahn ME (2004) Parametric representation of the primary hurricane vortex. Part I: observations evaluation of the Holland (1980) Model. Mon Weather Rev 132(12):3033–3048

    Article  Google Scholar 

  • Yuan J, Wang D, Wan Q, Liu C (2007) A 28-year climatological analysis of size parameters for Northwestern Pacific tropical cyclones. Adv Atmos Sci 24:24–34. https://doi.org/10.1007/s00376-007-0024-y

    Article  Google Scholar 

Download references

Acknowledgments

The authors are thankful to the Space Applications Centre (SAC), Indian Space Research Organization (ISRO), Ahmedabad, for providing funds through the SCATSAT-1 data utilization project with reference number SAC/EPSA/4.19/2016. Special thanks to Dr. Neeru Jaiswal (SAC, ISRO) for the scientific and technical support during the work. The authors acknowledge the Regional Specialized Meteorological Centre, India Meteorological Department (http://www.rsmcnewdelhi.imd.gov.in/index.php?lang=en), and Joint Typhoon Warning Center (https://www.metoc.navy.mil/jtwc/jtwc.html?north-indian-ocean) for providing the best track records of tropical cyclones. Acknowledgment goes to the JPL, NASA (https://www.podaac.jpl.nasa.gov), and MOSDAC (ISRO) (www.mosdac.gov.in) for providing the high-resolution scatterometer wind products used in this work.

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Kumar, S., Panda, J., Singh, K. et al. Structural characteristics of North Indian Ocean tropical cyclones during 1999–2017: a scatterometer observation-based analysis. Theor Appl Climatol 143, 227–240 (2021). https://doi.org/10.1007/s00704-020-03431-w

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