Skip to main content

Advertisement

Log in

Numerical modeling of tropical cyclone size over the Bay of Bengal: influence of microphysical processes and horizontal resolution

  • Original Paper
  • Published:
Meteorology and Atmospheric Physics Aims and scope Submit manuscript

Abstract

The destructiveness of tropical cyclones (TCs) is associated with uneven distribution of winds, TC-size, rainfall and storm-surge. The TCs in the Bay of Bengal (BoB) have shown a steady increase in size, as measured by the 34-knot wind radius (R34) over the past two-decades. TC-size information is essential in estimating areas to be evacuated to minimize the damage and loss of life. The study addresses the significance of microphysical (MP) processes and horizontal grid-resolution for improved TC-size. The Weather Research and Forecasting model is run at different grid-resolutions and various MP-schemes. Results show that TC movement is less sensitive to MP-schemes, while the size is more sensitive. The simple-ice (WSM3) scheme produced smaller TCs in R34 (228-km) due to less MP-heating caused by the evaporation of rainwater and lesser efficiency of freezing. Due to absence of ice-treatment and more rainwater, the warm-rain (Kessler) scheme produced larger TC-size (295-km). The size simulated from other schemes is more or less the same (266–284 km). Analyses indicate that higher MP-heating induces intense vertical-velocities, absolute angular momentum and thus increases the TC-size. Finer model resolution results in smaller TC-size. Though WSM3 performed better for size simulation, it somewhat underestimates at finer resolutions. For any particular resolution, the simulated size differs by 30–50 km among the MP schemes, while the size changes 5–15 km (2–4 km) between 6-km and 2-km (3-km and 2-km) grid-resolutions for any MP scheme. The study concludes that better TC-size can be achieved with appropriate MP-schemes at higher/cloud-resolving grid-resolution.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  • Bao JW, Gopalakrishnan SG, Michelson SA, Marks FD, Montgomery MT (2012) Impact of physics representations in the HWRFX on simulated hurricane structure and pressure–wind relationships. Mon Weather Rev 140(10):3278–3299

    Article  Google Scholar 

  • Beal LM, Vialard J, Roxy MK, Li J, Andres M, Annamalai H, Feng M, Han W, Hood R, Lee T, Lengaigne M (2020) A road map to IndOOS-2: better observations of the rapidly warming Indian Ocean. Bull Am Meteorol Soc 101(11):E1891–E1913

    Article  Google Scholar 

  • Busireddy NK, Nadimpalli R, Osuri KK, Ankur K, Mohanty UC, Niyogi D (2019) Impact of vortex size and Initialization on prediction of landfalling tropical cyclones over Bay of Bengal. Atmos Res 224:18–29

    Article  Google Scholar 

  • Chan KT, Chan JC (2013) Angular momentum transports and synoptic flow patterns associated with tropical cyclone size change. Mon Weather Rev 141(11):3985–4007

    Article  Google Scholar 

  • Chan KT, Chan JC (2016) Sensitivity of the simulation of tropical cyclone size to microphysics schemes. Adv Atom Sci 33(9):1024–1035

    Article  Google Scholar 

  • Chen H, Zhang DL, Carton J, Atlas R (2011) On the rapid intensification of Hurricane Wilma (2005) Part I: model prediction and structural changes. Weather Forecasting 26(6):885–901

    Article  Google Scholar 

  • Chen H, Zhang DL (2013) On the rapid intensification of Hurricane Wilma (2005) Part II: convective bursts and the upper-level warm core. J Atmos Sci 70(1):146–162

    Article  Google Scholar 

  • Deshpande M, Pattnaik S, Salvekar PS (2010) Impact of physical parameterization schemes on numerical simulation of super cyclone Gonu. Nat Hazard 55(2):211–231

    Article  Google Scholar 

  • Davis C, Wang W, Dudhia J, Torn R (2010) Does increased horizontal resolution improve hurricane wind forecasts? Weather Forecast 25(6):1826–1841

    Article  Google Scholar 

  • Douluri DL, Annapurnaiah K (2016) Impact of microphysics schemes in the simulation of cyclone hudhud using WRF-ARW model. Int J Oceans Oceanogr 10(1):49–59

    Google Scholar 

  • Dube SK, Jain I, Rao AD, Murty TS (2009) Storm surge modelling for the Bay of Bengal and Arabian Sea. Nat Hazard 51(1):3–27

    Article  Google Scholar 

  • Ferrier BS, Jin Y, Lin Y, Black T, Rogers E, DiMego G (2002) Implementation of a new grid-scale cloud and precipitation scheme in the NCEP Eta model. In: 19th conf on weather analysis and forecasting/15th conf on numerical weather prediction. Amer Meteor Soc , San Antonio TX, 10.1.

  • Fierro AO, Rogers RF, Marks FD, Nolan DS (2009) The impact of horizontal grid spacing on the microphysical and kinematic structures of strong tropical cyclones simulated with the WRF-ARW model. Mon Weather Rev 137(11):3717–3743

    Article  Google Scholar 

  • Fovell RG, Corbosiero KL, Kuo HC (2009) Cloud microphysics impact on hurricane track as revealed in idealized experiments. J Atmos Sci 66(6):1764–1778

    Article  Google Scholar 

  • Geetha B, Balachandran S (2016) Diabatic heating and convective asymmetries during rapid intensity changes of tropical cyclones over North Indian Ocean. Trop Cyclone Res Rev 5(1–2):32–46

    Google Scholar 

  • Heo KY, Lee JW, Ha KJ, Jun KC, Park KS, Kwon JI (2009) Simulation of atmospheric states for a storm surge on the west coast of Korea: model comparison between MM5, WRF and COAMPS. Nat Hazard 51(1):151–162

    Article  Google Scholar 

  • Hill KA, Lackmann GM (2009) Influence of environmental humidity on tropical cyclone size. Mon Weather Rev 137(10):3294–3315

    Article  Google Scholar 

  • Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132(1):103–120

    Article  Google Scholar 

  • Hong SY, Lim JO (2006) The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pac J Atmos Sci 42(2):129–151

    Google Scholar 

  • Jankov I, Gallus WA Jr, Segal M, Koch SE (2007) Influence of initial conditions on the WRF-ARW Model QPF response to physical parameterization changes. Weather Forecast 22(3):501–519

    Article  Google Scholar 

  • Kanase RD, Salvekar PS (2015) Impact of physical parameterization schemes on track and intensity of severe cyclonic storms in Bay of Bengal. Meteorol Atmos Phys 127(5):537–559

    Article  Google Scholar 

  • Kessler E (1969) On the distribution and continuity of water substance in atmospheric circulations. In: On the distribution and continuity of water substance in atmospheric circulations 1969 (pp. 1–84). American Meteorological Society, Boston, MA.

  • Kimball SK (2006) A modeling study of hurricane landfall in a dry environment. Mon Weather Rev 134(7):1901–1918

    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 

  • Knaff JA, Sampson CR, Chirokova G (2017) A global statistical–dynamical tropical cyclone wind radii forecast scheme. Weather Forecast 32(2):629–644

    Article  Google Scholar 

  • Krishnan R, Sanjay J, Gnanaseelan C, Mujumdar M, Kulkarni A, Chakraborty S (2020) Assessment of climate change over the Indian region: a report of the ministry of earth sciences (MOES), Government of India. Springer Nature.

  • Li X, Pu Z (2008) Sensitivity of numerical simulation of early rapid intensification of Hurricane Emily (2005) to cloud microphysical and planetary boundary layer parameterizations. Mon Weather Rev 136(12):4819–4838

    Article  Google Scholar 

  • Lin Y, Zhao M, Zhang M (2015) Tropical cyclone rainfall area controlled by relative sea surface temperature. Nat Commun 6(1):1–7

    Article  Google Scholar 

  • Lin YL, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. J Appl Meteorol Clim 22(6):1065–1092

    Article  Google Scholar 

  • Liu J, Zhang F, Pu Z (2017) Numerical simulation of the rapid intensification of Hurricane Katrina (2005): sensitivity to boundary layer parameterization schemes. Adv Atom Sci 34(4):482–496

    Article  Google Scholar 

  • Mohan PR, Srinivas CV, Yesubabu V, Baskaran R, Venkatraman B (2019) Tropical cyclone simulations over Bay of Bengal with ARW model: sensitivity to cloud microphysics schemes. Atmos Res 230:104651

    Article  Google Scholar 

  • 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 

  • Nadimpalli R, Osuri KK, Pattanayak S, Mohanty UC, Nageswararao MM, Prasad SK (2016) Real-time prediction of movement, intensity and storm surge of very severe cyclonic storm Hudhud over Bay of Bengal using high-resolution dynamical model. Nat Hazard 81(3):1771–1795

    Article  Google Scholar 

  • Nadimpalli R, Srivastava A, Prasad VS, Osuri KK, Das AK, Mohanty UC, Niyogi D (2020) Impact of INSAT-3D/3DR radiance data assimilation in predicting tropical cyclone Titli over the Bay of Bengal. IEEE Trans Geosci Remote Sens 58(10):6945–6957

    Article  Google Scholar 

  • Needham HF, Keim BD (2014) Correlating storm surge heights with tropical cyclone winds at and before landfall. Earth Interact 18(7):1–26

    Article  Google Scholar 

  • Osuri KK, Mohanty UC, Routray A, Makarand AK, Mohapatra M (2012a) Sensitivity of physical parameterization schemes of WRF model for the simulation of Indian seas tropical cyclones. Nat Hazard 63:1337–1359

    Article  Google Scholar 

  • Osuri KK, Mohanty UC, Routray A, Mohapatra M (2012b) The impact of satellite-derived wind data assimilation on track, intensity and structure of tropical cyclones over the North Indian Ocean. Int J Remote Sens 33(5):1627–1652

    Article  Google Scholar 

  • Osuri KK, Mohanty UC, Routray A, Mohapatra M, Niyogi D (2013) Real-time track prediction of tropical cyclones over the North Indian Ocean using the ARW model. J Appl Meteorol Clim 52(11):2476–2492

    Article  Google Scholar 

  • Osuri KK, Mohanty UC, Routray A, Niyogi D (2015) Improved prediction of Bay of Bengal tropical cyclones through assimilation of Doppler weather radar observations. Mon Weather Rev 143(11):4533–4560

    Article  Google Scholar 

  • Osuri KK, Nadimpalli R, Mohanty UC, Niyogi D (2017) Prediction of rapid intensification of tropical cyclone Phailin over the Bay of Bengal using the HWRF modelling system. Q J R Meteorol Soc 143(703):678–690

    Article  Google Scholar 

  • Pattnaik S, Krishnamurti TN (2007a) Impact of cloud microphysical processes on hurricane intensity, part 1: control run. Meteorol Atmos Phys 97(1):117–126

    Article  Google Scholar 

  • Pattnaik S, Krishnamurti TN (2007b) Impact of cloud microphysical processes on hurricane intensity, part 2: sensitivity experiments. Meteorol Atmos Phys 97(1):127–147

    Article  Google Scholar 

  • Rao AD, Murty PL, Jain I, Kankara RS, Dube SK, Murty TS (2013) Simulation of water levels and extent of coastal inundation due to a cyclonic storm along the east coast of India. Nat Hazard 66(3):1431–1441

    Article  Google Scholar 

  • Rogers R, Aberson S, Black M, Black P, Cione J, Dodge P, Dunion J, Gamache J, Kaplan J, Powell M, Shay N (2006) The intensity forecasting experiment: a NOAA multiyear field program for improving tropical cyclone intensity forecasts. Bull Am Meteorol Soc 87(11):1523–1538

    Article  Google Scholar 

  • Routray A, Lodh A, Dutta D, George JP (2020) Study of an extremely severe cyclonic storm “Fani” over Bay of Bengal using regional NCUM modeling system: a case study. J Hydrol 590:125357

    Article  Google Scholar 

  • Routray A, Mohanty UC, Osuri KK, Kar SC, Niyogi D (2016) Impact of satellite radiance data on simulations of Bay of Bengal tropical cyclones using the WRF-3DVAR modeling system. IEEE Trans Geosci Remote Sens 54(4):2285–2303

    Article  Google Scholar 

  • Sampson CR, Wittmann PA, Tolman HL (2010) Consistent tropical cyclone wind and wave forecasts for the US Navy. Weather Forecast 25(4):1293–1306

    Article  Google Scholar 

  • Sawada M, Iwasaki T (2007) Impacts of ice phase processes on tropical cyclone development. J Meteorol Soc Japan 285(4):479–494

    Article  Google Scholar 

  • Sawada M, Iwasaki T (2010) Impacts of evaporation from raindrops on tropical cyclones. Part I: evolution and axisymmetric structure. J Atmos Sci 67(1):71–83

    Article  Google Scholar 

  • Skamarock WC (2004) Evaluating mesoscale NWP models using kinetic energy spectra. Mon Weather Rev 132(12):3019–3032

    Article  Google Scholar 

  • Stovern DR, Ritchie EA (2016) Simulated sensitivity of tropical cyclone size and structure to the atmospheric temperature profile. J Atmos Sci 73(11):4553–4571

    Article  Google Scholar 

  • Sun Y, Zhong Z, Lu W (2015) Sensitivity of tropical cyclone feedback on the intensity of the western Pacific subtropical high to microphysics schemes. J Atmos Sci 72(4):1346–1368

    Article  Google Scholar 

  • Sun Y, Zhong Z, Li T, Yi L, Hu Y, Wan H, Chen H, Liao Q, Ma C, Li Q (2017) Impact of ocean warming on tropical cyclone size and its destructiveness. Sci Rep 7(1):10

    Google Scholar 

  • Tao WK, Shi JJ, Chen SS, Lang S, Lin PL, Hong SY, Peters-Lidard C, Hou A (2011) The impact of microphysical schemes on hurricane intensity and track. Asia-Pac J Atmos Sci 47(1):1–6

    Article  Google Scholar 

  • Thompson G, Field PR, Rasmussen RM, Hall WD (2008) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization. Mon Weather Rev 136(12):5095–5115

    Article  Google Scholar 

  • Wang Y (2009) How do outer spiral rainbands affect tropical cyclone structure and intensity? J Atmos Sci 66(5):1250–1273

    Article  Google Scholar 

  • Weatherford CL, Gray WM (1988) Typhoon structure as revealed by aircraft reconnaissance. Part I: data analysis and climatology. Mon Weather Rev 116(5):1032–1043

    Article  Google Scholar 

  • Xu J, Wang Y (2010) Sensitivity of the simulated tropical cyclone inner-core size to the initial vortex size. Mon Weather Rev 138(11):4135–4157

    Article  Google Scholar 

  • Xu J, Wang YA (2015) Statistical analysis on the dependence of tropical cyclone intensification rate on the storm intensity and size in the North Atlantic. Weather Forecast 30(3):692–701

    Article  Google Scholar 

  • Yu L, Wu S, Ma Z (2019) Evaluation of moist static energy in a simulated tropical cyclone. Atmosphere 10(6):319

    Article  Google Scholar 

Download references

Acknowledgements

The Council of Scientific and Industrial Research (CSIR) is gratefully acknowledged for providing financial support. The authors also gratefully acknowledge the computational support from the SERB project (ECR/2016/001637), Govt. of India and ESSO, Ministry of Earth Sciences (MoES/16/14/2014-RDEAS), Govt. of India. We thankfully acknowledged National Center for Atmospheric Research (NCAR) for WRF model and National Center for Environmental Prediction (NCEP) for providing real time data. We also acknowledge the India Meteorological Department and Joint Typhoon Warning Center for making TC-related data freely available.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Krishna K. Osuri.

Additional information

Responsible Editor: Emilia Kyung Jin.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 4088 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nekkali, Y.S., Osuri, K.K. & Das, A.K. Numerical modeling of tropical cyclone size over the Bay of Bengal: influence of microphysical processes and horizontal resolution. Meteorol Atmos Phys 134, 72 (2022). https://doi.org/10.1007/s00703-022-00915-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00703-022-00915-4

Navigation