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Prediction of rapid intensification for land-falling extremely severe cyclonic storms in the Bay of Bengal

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Abstract

During the past 30 years (1990–2020), all extremely severe cyclonic storms (ESCS) that formed over the Bay of Bengal (BoB) region had undergone rapid intensification (RI) attributed due to climate change. A detailed evaluation is therefore necessary to forecast and ascertain the rapid changes in intensity especially the RI over BoB that has direct socio-economic consequences in coastal regions. This study performed a detailed evaluation on the intensity and RI of ESCS over the BoB region using Weather Research and Forecasting (WRF) model. Observations were assimilated using conventional and satellite radiances to improve the initial conditions of four ESCSs (Mala, 2006; Sidr, 2007; Phailin, 2013 and Hudhud, 2014). Numerical experiments used a double-nested domain of 27 and 9 km horizontal resolution with 73 vertical levels. The forecasted intensity, structure, and storm tracks were in good agreement with the available observations. Mean Absolute Error (MAE) of Maximum Surface Winds (MSW) were 5 m s−1, 6 m s−1, 5 m s−1, and 5 m s−1 from Day 1 to Day 4, respectively, with mean initial intensity error of about 4.5 m s−1. Results indicate that RI of ESCS was well apprehended in WRF model showing significant differences in the forecast for different cases. In most of the cases, the RI was under-predicted. The overall performance of WRF model was reasonably good in forecasting RI, and the estimated statistics for Probability of Detection (POD) and False Alarm Rate (FAR) was about 68% and 21%, respectively, based on the four cases considered in this study. Forecasted reflectivity and vertical profiles of heating rate, divergence, hydrometeors, vertical winds, and temperature anomaly are also presented in this study. Mean track error of storms varied between 75 and 135 km, at 24 to 96 h, respectively, while the mean landfall time and positional errors were about 3 h and 69 km, respectively. This research has also highlighted the need for a re-evaluation of the WRF model’s performance in forecasting the track, structure, and intensity of ESCS over BOB during the RI.

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source Akter and Tsuboki 2012; left hand figure), and model prediction (right hand figure) for ESCS Sidr at 11 UTC on 15 November 2007

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Availability of data and material

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

Code availability

The code used in this study is an open source and available from the WRF model.

References

  • Albert J, Bhaskaran PK (2020a) Evaluation of track length, residence time and translational speed for tropical cyclones in the North Indian Ocean. ISH J Hydraulic Eng. https://doi.org/10.1080/09715010.2020.1825124

    Article  Google Scholar 

  • Albert J, Bhaskaran PK (2020b) Ocean heat content and its role in tropical cyclogenesis for the Bay of Bengal basin. Clim Dyn. https://doi.org/10.1007/s00382-020-05450-9

    Article  Google Scholar 

  • Albert J, Bhaskaran PK (2020c) Optimal grid resolution for the detection lead time of cyclogenesis in the North Indian ocean. J Atmos Solar Terr Phys. https://doi.org/10.1016/j.jastp.2020.105289

    Article  Google Scholar 

  • Albert J, Krishnan A, Bhaskaran PK, Singh KS (2021a) Role and influence of key atmospheric parameters in large-scale environmental flow associated with tropical cyclogenesis and ENSO in the North Indian Ocean basin. Clim Dyn. https://doi.org/10.1007/s00382-021-05885-8

    Article  Google Scholar 

  • Albert J, Sahoo B, Bhaskaran PK (2021b) Tropical cyclogenesis identification using eddy detection technique for the Bay of Bengal Basin. Atmos Res. https://doi.org/10.1016/j.atmosres.2021.105670

    Article  Google Scholar 

  • Aligo EA, Gallus WA Jr, Segal M (2009) On the impact of WRF model vertical grid resolution on Midwest summer rainfall forecasts. Weather Forecast 24(2):575–594

    Google Scholar 

  • Baker NL, Hogan TF, Campbell WF, Pauley RL, Swadley SD (2005) The impact of AMSU-A radiance assimilation in the US Navy's Operational Global Atmospheric Prediction System (NOGAPS) (No. NRL/MR/7530–05–8836). Naval Research Lab Monterey CA Marine Meteorology Div.

  • Barker DM, Huang W, Guo YR, Bourgeois AJ, Xiao QN (2004) A three-dimensional variational data assimilation system for MM5: implementation and initial results. Mon Weather Rev 132(4):897–914

    Google Scholar 

  • Bhaskaran PK, Stone GW (2007) Numerical simulation of typhoon wind forcing in the Korean seas using a spectral wave model. J Coastal Res 23(2):362–373. https://doi.org/10.2112/04-0173.1

    Article  Google Scholar 

  • Bhaskaran PK, Kalra R, Dube SK, Sinha PC, Rao AD, Kumar R, Sarkar A (2000) Extreme wave conditions over the Bay of Bengal during a severe cyclone — simulation experiment with two spectral wave models. Mar Geodesy 23(2):91–102. https://doi.org/10.1080/01490410050030661

    Article  Google Scholar 

  • Bhaskaran PK, Gayathri R, Murty PLN, Subba Reddy B, Sen D (2013a) A numerical study of coastal inundation and its validation for Thane Cyclone in the Bay of Bengal. Coast Eng 83:108–118. https://doi.org/10.1016/j.coastaleng.2013.10.005

    Article  Google Scholar 

  • Bhaskaran PK, Nayak S, Bonthu SR, Murty PLN, Sen D (2013b) Performance and validation of a coupled parallel ADCIRC-SWAN model for THANE cyclone in the Bay of Bengal. Environ Fluid Mech 13:601–623. https://doi.org/10.1007/s10652-013-9284-5

    Article  Google Scholar 

  • Chen SH, Vandenberghe F, Petty GW, Bresch JF (2004) Application of SSM/I satellite data to a hurricane simulation. Q J R Meteorol Soc 130(598):801–825

    Google Scholar 

  • Chou CB, Huang HP (2011) The impact of assimilating atmospheric infrared sounder observation on the forecast of typhoon tracks. Adv Meteorol. https://doi.org/10.1155/2011/803593

    Article  Google Scholar 

  • Dhana Lakshmi D, Murty PLN, Bhaskaran PK, Sahoo B, Srinivasa Kumar T, Shenoi SSC, Srikanth AS (2017) Performance of WRF-ARW winds on computed storm surge using hydrodynamic model for Phailin and Hudhud cyclones. Ocean Eng 131:135–148. https://doi.org/10.1016/j.oceaneng.2017.01.005

    Article  Google Scholar 

  • Donelan MA, Haus BK, Reul N, Plant WJ, Stiassnie M, Graber HC, Brown OB, Saltzman ES (2004) On the limiting aerodynamic roughness of the ocean in very strong winds. Geophys Res Lett 31(18):L18306

    Google Scholar 

  • Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46(20):3077–3107

    Google Scholar 

  • Gayathri R, Murty PLN, Bhaskaran PK, Srinivasa KT (2016) A numerical study of hypothetical storm surge and coastal inundation for AILA cyclone in the Bay of Bengal. Environ Fluid Mech 16(2):429–452. https://doi.org/10.1007/s10652-015-9434-z

    Article  Google Scholar 

  • Gayathri R, Bhaskaran PK, Jose F (2017) Coastal inundation research: an overview of the processes. Curr Sci 112(2):267–278

    Google Scholar 

  • Gopalakrishnan SG, Goldenberg S, Quirino T, Zhang X, Marks F Jr, Yeh KS, Atlas R, Tallapragada V (2012) Toward improving high-resolution numerical hurricane forecasting: influence of model horizontal grid resolution, initialization, and physics. Weather Forecast 27(3):647–666

    Google Scholar 

  • Gopalakrishnan SG, Marks F Jr, Zhang JA, Zhang X, Bao JW, Tallapragada V (2013) A study of the impacts of vertical diffusion on the structure and intensity of the tropical cyclones using the high-resolution HWRF system. J Atmos Sci 70(2):524–541

    Google Scholar 

  • Greeshma M, Srinivas CV, Hari Prasad KB, Baskaran R, Venkatraman B (2019) Sensitivity of tropical cyclone predictions in the coupled atmosphere-ocean model WRF-3DPWP to surface roughness schemes. Meteorol Appl 26(2):324–346

    Google Scholar 

  • Han Y, Weng F, Liu Q, van Delst P (2007) A fast radiative transfer model for SSMIS upper atmosphere sounding channels. J Geophys Res Atmos 112(D11)

  • Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134(9):2318–2341

    Google Scholar 

  • Inness PM, Slingo JM, Woolnough SJ, Neale RB, Pope VD (2001) Organization of tropical convection in a GCM with varying vertical resolution; implications for the simulation of the Madden-Julian Oscillation. Clim Dyn 17(10):777–793

    Google Scholar 

  • Islam AS, Bala SK, Hussain MA, Hossain MA, Rahman MM (2011) Performance of coastal structures during Cyclone Sidr. Nat Hazard Rev 12(3):111–116

    Google Scholar 

  • Kaplan J, DeMaria M, Knaff JA (2010) A revised tropical cyclone rapid intensification index for the Atlantic and eastern North Pacific basins. Wea Forecasting 25:220–224

    Google Scholar 

  • Kaplan J, Rozoff CM, DeMaria M, Sampson CR, Kossin JP, Velden CS, Cione JJ, Dunion JP, Knaff JA, Zhang JA, Dostalek JF (2015) Evaluating environmental impacts on tropical cyclone rapid intensification predictability utilizing statistical models. Weather Forecast 30(5):1374–1396

    Google Scholar 

  • Kieper M, Jiang H (2012) Predicting tropical cyclone rapid intensification using the 37 GHz ring pattern identified from passive microwave measurements. Geophys Res Lett 39(L1):3804. https://doi.org/10.1029/2012GL052115

    Article  Google Scholar 

  • Kieu CQ, Tallapragada V, Hogsett W (2014) Vertical structure of tropical cyclones at onset of the rapid intensification in the HWRF model. Geophys Res Lett 41:3298–3306

    Google Scholar 

  • Kimball SK, Dougherty FC (2006) The sensitivity of idealized hurricane structure and development to the distribution of vertical levels in MM5. Mon Weather Rev 134(7):1987–2008

    Google Scholar 

  • Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin JP, Srivastava AK, Sugi M (2010) Tropical cyclones and climate change. Nat Geosci 3(3):157–163

    Google Scholar 

  • Kumari KV, Yesubabu V, Dasari HP, Langodan S, Vissa NK, Reddy SS, Rao SVB (2021) Impact of assimilation of SCATSAT-1 data on coupled ocean-atmospheric simulations of tropical cyclones over Bay of Bengal. Atmos Res 105733

  • Lee P, Kang D, McQueen J, Tsidulko M, Hart M, DiMego G, Seaman N, Davidson P (2008) Impact of domain size on modeled ozone forecast for the northeastern United States. J Appl Meteorol Climatol 47(2):443–461

    Google Scholar 

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

    Google Scholar 

  • Lorenc AC, Ballard SP, Bell RS, Ingleby NB, Andrews PLF, Barker DM, Bray JR, Clayton AM, Dalby T, Li D, Payne TJ (2000) The Met. Office global three-dimensional variational data assimilation scheme. Q J R Meteorol Soc 126(570):2991–3012

    Google Scholar 

  • Mandal M, Singh KS, Balaji M, Mohapatra M (2016) Performance of WRF-ARW model in real-time prediction of Bay of Bengal cyclone ‘Phailin.’ Pure Appl Geophys 173(5):1783–1801

    Google Scholar 

  • Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res Atmos 102(D14):16663–16682

    Google Scholar 

  • Molinari J, Vollaro D (2010) Rapid intensification of a sheared tropical storm. Mon Wea Rev 138:3869–3885

    Google Scholar 

  • Mukhopadhyay P, Taraphdar S, Goswami BN (2011) Influence of moist processes on track and intensity forecast of cyclones over the north Indian Ocean. J Geophys Res Atmos 116(D5)

  • Murakami H, Vecchi GA, Underwood S (2017) Increasing frequency of extremely severe cyclonic storms over the Arabian Sea. Nat Clim Chang 7(12):885–889

    Google Scholar 

  • Murty PLN, Sandhya KG, Bhaskaran PK, Jose F, Gayathri R, Balakrishnan Nair TM, Srinivasa Rao T, Shenoi SSC (2014) A coupled hydrodynamic modeling system for PHAILIN cyclone in the Bay of Bengal. Coast Eng 93:71–81. https://doi.org/10.1016/j.coastaleng.2014.08.006

    Article  Google Scholar 

  • Murty PLN, Bhaskaran PK, Gayathri R, Sahoo B, Srinivasa Kumar T, Subba Reddy B (2016) Numerical study of coastal hydrodynamics using a coupled model for Hudhud cyclone in the Bay of Bengal. Estuar Coast Shelf Sci 183:13–27. https://doi.org/10.1016/j.ecss.2016.10.013

    Article  Google Scholar 

  • Murty PLN, Siva Srinivas K, Rama Rao EP, Bhaskaran PK, Shenoi SSC, Padmanabham J (2020) Improved cyclonic wind fields over the Bay of Bengal and their application in storm surge and wave computations. Appl Ocean Res. https://doi.org/10.1016/j.apor.2019.102048

    Article  Google Scholar 

  • Nayak S, Bhaskaran PK (2014) Coastal vulnerability due to extreme waves at Kalpakkam based on historical tropical cyclones in the Bay of Bengal. Int J Climatol 34:1460–1471. https://doi.org/10.1002/joc.3776

    Article  Google Scholar 

  • Nellipudi NR, Viswanadhapalli Y, Challa VS, Vissa NK, Langodan S (2021) Impact of surface roughness parameterizations on tropical cyclone simulations over the Bay of Bengal using WRF-OML model. Atmos Res 262:105779

    Google Scholar 

  • Niu GY, Yang ZL, Mitchell KE, Chen F, Ek MB, Barlage M, Kumar A, Manning K, Niyogi D, Rosero E, Tewari M (2011) The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. J Geophys Res Atmos 116(D12)

  • Pan HL, Wu WS (1995) Implementing a mass flux convection parameterization package for the NMC medium-range forecast model. Note 409, 40 http://wwwt.emc.ncep.noaa.gov/officenotes

  • Parrish DF, Derber JC (1992) The National Meteorological Center’s spectral statistical interpolation analysis system. Mon Weather Rev 120:1747–1763

    Google Scholar 

  • Pattanayak S, Mohanty UC, Gopalakrishnan SG (2012) Simulation of very severe cyclone Mala over Bay of Bengal with HWRF modeling system. Nat Hazards 63(3):1413–1437

    Google Scholar 

  • Pope VD, Pamment JA, Jackson DR, Slingo A (2001) The representation of water vapor and its dependence on vertical resolution in the Hadley Centre Climate Model. J Clim 14(14):3065–3085

    Google Scholar 

  • Poulose J, Rao AD, Bhaskaran PK (2017) Role of continental shelf on non-linear interaction of storm surges, tides and wind waves: an idealized study representing the west coast of India. Estuar Coast Shelf Sci. https://doi.org/10.1016/j.ecss.2017.06.007

    Article  Google Scholar 

  • Powell MD, Vickery PJ, Reinhold TA (2003) Reduced drag coefficient for high wind speeds in tropical cyclones. Nature 422(6929):279–283

    Google Scholar 

  • Pu Z, Li X, Zipser EJ (2009) Diagnosis of the initial and forecast errors in the numerical simulation of the rapid intensification of Hurricane Emily (2005). Weather Forecast 24:1236–1251

    Google Scholar 

  • Rappaport EN, Franklin JL, Avila LA, Baig SR, Beven JL, Blake ES, Burr CA, Jiing JG, Juckins CA, Knabb RD, Landsea CW, Mainelli M, Mayfield M, McAdie CJ, Pasch RJ, Sisko C, Stewart SR, Tribble AN (2009) Advances and challenges at the National Hurricane Center. Weather Forecast 24:395–419. https://doi.org/10.1175/2008WAF2222128.1

    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

    Google Scholar 

  • Ruti PM, Di Rocco D, Gualdi S (2006) Impact of increased vertical resolution on simulation of tropical climate. Theoret Appl Climatol 85(1–2):61–80

    Google Scholar 

  • Sahoo B, Bhaskaran PK (2015) Assessment on historical cyclone tracks in the Bay of Bengal, east coast of India. Int J Climatol 36(1):95–109. https://doi.org/10.1002/joc.4331

    Article  Google Scholar 

  • Sahoo B, Bhaskaran PK (2017) Multi-hazard risk assessment of coastal vulnerability from tropical cyclones — a GIS based approach for the Odisha coast. J Environ Manag. https://doi.org/10.1016/j.jenvman.2017.10.075

    Article  Google Scholar 

  • Sahoo B, Bhaskaran PK (2019a) Prediction of storm surge and coastal inundation using Artificial Neural Network — a case study for 1999 Odisha Super Cyclone. Weather Clim Extremes. https://doi.org/10.1016/j.wace.2019.100196

    Article  Google Scholar 

  • Sahoo B, Bhaskaran PK (2019b) Prediction of storm surge and inundation using climatological datasets for the Indian coast using soft computing techniques. Soft Comput. https://doi.org/10.1007/s00500-019-03775-0(0123456789(),-volV)(0123456789,-().volV)

    Article  Google Scholar 

  • Sahoo B, Bhaskaran PK, Pradhan A (2018) Application of weather forecasting model WRF for operational electric power network management — a case study for Phailin cyclone. Theoret Appl Climatol. https://doi.org/10.1007/s00704-018-2639-6

    Article  Google Scholar 

  • Sahoo B, Bhaskaran PK (2018a) A comprehensive data set for tropical cyclone storm surge-induced inundation for the east coast of India. Int J Climatol. https://doi.org/10.1002/joc.5184

  • Singh KS, Bhaskaran PK (2017) Impact of PBL and convection parameterization schemes for prediction of severe land-falling Bay of Bengal cyclones using WRF-ARW model. J Atmos Solar Terr Phys 165–166:10–24. https://doi.org/10.1016/j.jastp.2017.11.004

    Article  Google Scholar 

  • Singh KS, Bhaskaran PK (2018) Impact of lateral boundary and initial conditions in the prediction of Bay of Bengal cyclones using WRF model and its 3D-VAR data assimilation system. J Atmos Solar Terr Phys. https://doi.org/10.1016/j.jastp.2018.05.007

    Article  Google Scholar 

  • Singh KS, Tyagi B (2019) Impact of data assimilation and air-sea flux parameterization schemes on the prediction of cyclone Phailin over the Bay of Bengal using the WRF-ARW model. Meteorol Appl 26(1):36–48

    Google Scholar 

  • Singh KS, Mandal M, Bhaskaran PK (2019) Impact of radiance data assimilation on the prediction performance of cyclonic storm SIDR using WRF-3DVAR modelling system. Meteorol Atmos Phys 131(1):11–28

    Google Scholar 

  • Singh KS, Alam P, Albert J, Bhaskaran PK (2020) Numerical simulation of an extremely severe cyclonic storm Hudhud over the North Indian Ocean in a medium range scale: influence of cloud microphysical schemes. Pure Appl Geophys. https://doi.org/10.1007/s00024-020-02596-9

    Article  Google Scholar 

  • Singh KS, Albert J, Bhaskaran PK, Alam P (2021a) Assessment of extremely severe cyclonic storms over Bay of Bengal and performance evaluation of ARW model in the prediction of track and intensity. Theoret Appl Climatol. https://doi.org/10.1007/s00704-020-03510-y

    Article  Google Scholar 

  • Singh KS, Albert J, Bhaskaran PK, Alam P (2021b) Numerical simulation of an extremely severe cyclonic storm over the Bay of Bengal using WRF modelling system: influence of model initial condition. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-01069-1

    Article  Google Scholar 

  • Singh KS, Subbareddy B, Bhaskaran PK, Purvaja R, Ramesh R (2021c) Impact of time step size on different cumulus parameterization schemes in the numerical simulation of a heavy rainfall event over Tamil Nadu. Pure and Applied Geophysics, India. https://doi.org/10.1007/s00024-021-02896-8

    Book  Google Scholar 

  • Singh KS, Bhaskaran PK (2019a) Prediction of landfalling Bay of Bengal cyclones during 2013 using the high resolution WRF model. Meteorol Appl 27(1). https://doi.org/10.1002/met.1850

  • Singh KS, Mandal M (2015) Impact of initial and boundary conditions on mesoscale simulation of Bay of Bengal cyclones using WRF-ARW model. In High-Impact Weather Events over the SAARC Region 179–189 Springer Cham

  • Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the Advanced Research WRF version 2. NCAR Tech. Note NCAR/TN-4681STR, pp 88

  • Song IS, Byun UY, Hong J, Park SH (2018) Domain-size and top-height dependence in regional predictions for the Northeast Asia in spring. Atmos Sci Lett 19(1):799. https://doi.org/10.1002/asl.799

    Article  Google Scholar 

  • Srinivas CV, Yesubabu V, Venkatesan R, Ramakrishna SSVS (2010) Impact of assimilation of conventional and satellite meteorological observations on the numerical simulation of a Bay of Bengal Tropical Cyclone of November 2008 near Tamilnadu using WRF model. Meteorol Atmos Phys 110(1–2):19–44

    Google Scholar 

  • Srinivas CV, Yesubabu V, Hariprasad KBRR, Ramakrishna SSV, Venkatraman B (2013) Real-time prediction of a severe cyclone ‘Jal’ over Bay of Bengal using a high-resolution mesoscale model WRF (ARW). Nat Hazards 65(1):331–357

    Google Scholar 

  • Sudha Rani NNV, Satyanarayana ANV, Bhaskaran PK (2015) Coastal vulnerability assessment studies over India: a review. Nat Hazards 77:405–428. https://doi.org/10.1007/s11069-015-1597-x

    Article  Google Scholar 

  • Vissa NK, Satyanarayana ANV, Kumar BP (2012) Response of Upper Ocean during passage of MALA cyclone utilizing ARGO data. Int J Appl Earth Obs Geoinf 14(1):149–159

    Google Scholar 

  • Vissa NK, Satyanarayana ANV, Bhaskaran PK (2013a) Response of oceanic cyclogenesis metrics for NARGIS cyclone: a case study. Atmos Sci Lett 14:7–13. https://doi.org/10.1002/asl2.407

    Article  Google Scholar 

  • Vissa NK, Satyanarayana ANV, Bhaskaran PK (2013b) Intensity of tropical cyclones during pre- and post-monsoon seasons in relation to accumulated tropical cyclone heat potential over Bay of Bengal. Nat Hazards 68:351–371. https://doi.org/10.1007/s11069-013-0625-y

    Article  Google Scholar 

  • Vissa NK, Satyanarayana ANV, Bhaskaran PK (2013c) Impact of South China Sea cold surges and typhoon Peipah on initiating cyclone Sidr in the Bay of Bengal. Pure Appl Geophys 170:2369–2381. https://doi.org/10.1007/s00024-013-0671-0

    Article  Google Scholar 

  • Vissa NK, Satyanarayana ANV, Bhaskaran PK (2013d) Response of Upper Ocean and impact of barrier layer on SIDR cyclone induced sea surface cooling. Ocean Sci J 48(3):279–288. https://doi.org/10.1007/s12601-013-0026-x

    Article  Google Scholar 

  • Wahiduzzaman M, Luo CK, Bhaskaran PK, Tang S, Yuan C (2021) Impact assessment of Indian Ocean Dipole on the North Indian Ocean tropical cyclone prediction using a statistical model. Clim Dyn. https://doi.org/10.1007/s00382-021-05960-0

    Article  Google Scholar 

  • Wang X, Jiang H (2019) A 13-year global climatology of tropical cyclone warm-core structures from AIRS data. Mon Weather Rev 147(3):773–790

    Google Scholar 

  • Weng F (2007) Advances in radiative transfer modeling in support of satellite data assimilation. J Atmos Sci 64(11):3799–3807

    Google Scholar 

  • Xie Y, Xing J, Shi J, Dou Y, Lei Y (2016) Impacts of radiance data assimilation on the Beijing 7.21 heavy rainfall. Atmos Res 169:318–330

    Google Scholar 

  • Xu D, Liu Z, Huang X-Y, Min J, Wang H (2013) Impact of assimilating IASI radiance observations on forecasts of two tropical cyclones. Meteorol Atmos Phys 122:1–18

    Google Scholar 

  • Yesubabu V, Srinivas CV, Hariprasad KBRR, Baskaran R (2013) A study on the impact of observation assimilation on the numerical simulation of tropical cyclones Jal and Thane using 3-d-var. Pure Appl Geophys 171:2023–2042. https://doi.org/10.1007/s00024-013-0741-3

    Article  Google Scholar 

  • Yesubabu V, Kattamanchi VK, Vissa NK, Dasari HP, Sarangam VBR (2020) Impact of ocean mixed-layer depth initialization on the simulation of tropical cyclones over the Bay of Bengal using the WRF-ARW model. Meteorol Appl 27(1):e1862

    Google Scholar 

  • Zhang DL, Wang X (2003) Dependence of hurricane intensity and structures on vertical resolution and time-step size. Adv Atmos Sci 20(5):711. https://doi.org/10.1007/BF02915397

    Article  Google Scholar 

  • Zhang X, Xiao Q, Patrick F (2007) The impact of multi-satellite data on the initialization and simulation of Hurricane Lili’s (2002). Mon Weather Rev 135:526–548

    Google Scholar 

  • Zhang C, Wang Y, Hamilton K (2011a) Improved representation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme. Mon Weather Rev 139(11):3489–3513

    Google Scholar 

  • Zhang F, Weng Y, Gamache JF, Marks FD (2011) Performance of convection‐permitting hurricane initialization and prediction during 2008–2010 with ensemble data assimilation of inner‐core airborne Doppler radar observations. Geophys Res Lett 38(15)

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Acknowledgements

The authors sincerely acknowledge the India Meteorological Department (IMD) for providing the observations, NCEP for providing the analysis data sets, NCAR for the WRF model and its 3DVAR data assimilation system.

Funding

Ambily Thankachan is thankful to the Vellore Institute of Technology for providing the research funding. The author acknowledges the Department of Science and Technology—Science and Engineering Research Board (DST-SERB) Government of India, for funding the research project (File Sanction No.—ECR/2018/001185).

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Authors and Affiliations

  1. Centre for Disaster Mitigation and Management, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

    Kuvar Satya Singh

  2. Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India

    Kuvar Satya Singh, Ambily Thankachan, K. Thatiparthi & M. S. Reshma

  3. Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India

    Jiya Albert & Prasad K. Bhaskaran

  4. National Centre for Sustainable Coastal Management, Ministry of Environment, Forest and Climate Change, Anna University Campus, Chennai, 600025, Tamil Nadu, India

    Subbareddy Bonthu

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Kuvar Satya Singh, Ambily Thankachan, Thatiparthi K, Reshma M S]. The first draft of the manuscript was written by [Kuvar Satya Singh, Ambily Thankachan, Jiya Albert, Prasad K Bhaskaran] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Conceptualization: [Kuvar Satya Singh, Prasad K Bhaskaran]; Methodology: [Kuvar Satya Singh, Ambily Thankachan, Thatiparthi K, Reshma M S, Jiya Albert, Subba Reddy B, Prasad K Bhaskaran]; Formal analysis and investigation: [Kuvar Satya Singh, Ambily Thankachan, Thatiparthi K, Reshma M S, Jiya Albert, Prasad K Bhaskaran]; Writing—original draft preparation: [Kuvar Satya Singh, Prasad K Bhaskaran]; Writing—review and editing: [Kuvar Satya Singh, Jiya Albert, Prasad K Bhaskaran]; Supervision: [Kuvar Satya Singh, Prasad K Bhaskaran].

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Correspondence to Prasad K. Bhaskaran.

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The submitted work is original and not published elsewhere in any form or language (partially or in full). All authors made substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data; or the creation of new software used in the work.

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Singh, K.S., Thankachan, A., Thatiparthi, K. et al. Prediction of rapid intensification for land-falling extremely severe cyclonic storms in the Bay of Bengal. Theor Appl Climatol 147, 1359–1377 (2022). https://doi.org/10.1007/s00704-022-03923-x

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