Abstract
Gauging appropriate physical parameterization schemes for any numerical weather prediction model is indispensable for obtaining high accuracy in tropical cyclone forecasting. In this study, combinations of five microphysics, three cumulus convection, and two planetary boundary layer (PBL) schemes are investigated with respect to track, intensity, and time of landfall to determine an optimal combination of physical schemes of the weather research and forecasting (WRF) model (version 4.0) with advanced research WRF (ARW) core. All sensitivity experiments are carried out by taking the initial and boundary conditionsfrom the National Centers for Environmental Prediction Global Forecast System (NCEP-GFS). The simulated track, intensity, and landfall time are compared with the Indian Meteorological Department (IMD) observations.
The sensitivity experiments reveal that the KF cumulus is performing better with YSU PBL along with WSM6, Ferrier (new eta), and Thompson microphysics for the track (position and time), and intensity with the least errors. Furthermore, we examined the performance of the model with the above combination of schemes on four severe landfalling cyclones (Bulbul, Hudhud, Aila, and Sidr). The root mean square error (RMSE) for central pressure gives the least value in the range of 0.4 to 8 hPa and 0.2 to 3.7 ms−1 for maximum surface wind (MSW) during landfall with YSU-KF- Ferrier combination. The equivalent potential temperature shows strong vertical mixing up to 500 hPa in the case of YSU-KF-Ferrier, which enhances the formation of warm-core, which further explains the intensity of cyclones. Overall, the track, intensity, and rainfall forecasts for the extreme cyclones considered in this study are consistent with IMD observations using YSU PBL, KF cumulus convection, and Ferrier microphysics.
Similar content being viewed by others
Data availability
Not applicable.
Code availability
The authors have used WRF model for cyclone simulation which is available for public.
Change history
01 June 2022
The article title was duplicate in the article.
References
Betts AK, Miller MJ (1986) A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets. Q.J.R. Meteorol Soc 112:693–709. https://doi.org/10.1002/qj.49711247308
Bhaskar Rao DV, Prasad DH, Srinivas D, Anjaneyulu Y (2010) Role of vertical resolution in numerical models towards the intensification, structure and track of tropical cyclones. Mar Geodesy 33(4):338–355
Chen YJ, Xie Q, Meng WG, Yuan JN, Wang DX (2010) A numerical study of the influence of sea surface temperatures with different temporal resolutions on typhoon Dujuan over the South China Sea. J Trop Meteorol 16(2):195
Choudhury D, Das S (2017) The sensitivity to the microphysical schemes on the skill of forecasting the track and intensity of tropical cyclones using WRF-ARW model. J Earth Syst Sci 126(4):57. https://doi.org/10.1007/s12040-017-0830-2
Davis C, Bosart LF (2002). Numerical simulations of the genesis of Hurricane Diana (1984). Part II: sensitivity of track and intensity prediction. Monthly weather review. 130(5):1100-1124. https://doi.org/10.1175/1520-0493(2002)130<1100:NSOTGO>2.0.CO;2
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
Elsner JB, Kossin JP, Jagger TH (2008) The increasing intensity of the strongest tropical cyclones. Nature 455(7209):92–95. https://doi.org/10.1038/nature07234
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. Conf Weather Anal Forecasting AMS 19:280–283
Fovell RG, Su H (2007). Impact of cloud microphysics on hurricane track forecasts. Geophysical Research Letters. 34(24). https://doi.org/10.1029/2007GL03172
Grell GA, Dévényi D (2002). A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophysical Research Letters. 29(14). https://doi.org/10.1029/2002GL015311
Hong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pac J Atmos Sci 42(2):129–151
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. https://doi.org/10.1175/MWR3199.1
Islam T, Srivastava PK, Rico-Ramirez MA, Dai Q, Gupta M, Singh SK (2015) Tracking a tropical cyclone through WRF–ARW simulation and sensitivity of model physics. Nat Hazards 76(3):1473–1495
Janjic ZL (1990) The step-mountain coordinate physical package. Mon Weather Rev 118(7):1429–1443
Janjic ZI (2002) Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP meso model. NCEP Office Note. 437:61
Janjić ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon Weather Rev 122(5):927–945. https://doi.org/10.1175/1520-0493(1994)122%3c0927:TSMECM%3e2.0.CO;2
Janjić ZI (2000) Comments on development and evaluation of a convection scheme for use in climate models. J Atmos Sci 57(21):3686–3686. https://doi.org/10.1175/1520-0469(2000)057%3c3686:CODAEO%3e2.0.CO;2
Janjic ZI (1996). The surface layer in the NCEP Eta model. Eleventh conference on numerical weather prediction. Am Meteorol Soc, Boston, Norfolk, pp. 354–355.
Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl Meteorol 43(1):170–181. https://doi.org/10.1175/1520-0450(2004)043%3c0170:TKCPAU%3e2.0.CO;2
Kanase RD, Salvekar PS (2015a) Effect of physical parameterization schemes on track and intensity of cyclone LAILA using WRF model. Asia-Pac J Atmos Sci 51(3):205–227. https://doi.org/10.1007/s13143-015-0071-8
Kanase RD, Salvekar PS (2015b) Impact of physical parameterization schemes on track and intensity of severe cyclonic storms in Bay of Bengal. Meteorol Atmos Phys 127(5):537–559. https://doi.org/10.1007/s00703-015-0381-5
Kanase RD, Mukhopadhyay P, Salvekar PS (2015) Understanding the role of cloud and convective processes in simulating the weaker tropical cyclones over Indian seas. Pure Appl Geophys 172(6):1751–1779. https://doi.org/10.1007/s00024-014-0996-3
Karyampudi VM, Lai GS, Manobianco J (1998) Impact of initial conditions, rainfall assimilation, and cumulus parameterization on simulations of Hurricane Florence (1988). Mon Weather Rev 126(12):3077–3101
Kessler E (1969) On the distribution and continuity of water substance in atmospheric circulations. On the distribution and continuity of water substance in atmospheric circulations. American Meteorological Society, Boston, MA, pp 1–84
Kossin JP, Emanuel KA, Vecchi GA (2014) The poleward migration of the location of tropical cyclone maximum intensity. Nature 509(7500):349–352. https://doi.org/10.1038/nature13278
Kumari KV, Sagar SK, Viswanadhapalli Y, Dasari HP, Rao SVB (2019) Role of planetary boundary layer processes in the simulation of tropical cyclones over the Bay of Bengal. Pure Appl Geophys 176(2):951–977. https://doi.org/10.1007/s00024-018-2017-4
Li X, Pu Z (2009) Sensitivity of numerical simulations of the early rapid intensification of Hurricane Emily to cumulus parameterization schemes in different model horizontal resolutions. J Meteorol Society Japan Ser II 87(3):403–421. https://doi.org/10.2151/jmsj.87.403
Li WW, Wang C, Wang D, Yang L, Deng Y (2012) Modulation of low-latitude west wind on abnormal track and intensity of tropical cyclone Nargis (2008) in the Bay of Bengal. Adv Atmos Sci 29(2):407–421. https://doi.org/10.1007/s00376-011-0229-y
Lin YL, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. J Climate Appl Meteorol 22(6):1065–1092
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). Geophysical Research Letters. 36(3). https://doi.org/10.1029/2008GL035815
Ma Z, Fei J, Huang X, Cheng X (2012) Sensitivity of tropical cyclone intensity and structure to vertical resolution in WRF. Asia-Pac J Atmos Sci 48(1):67–81. https://doi.org/10.1007/s13143-012-0007-5
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. https://doi.org/10.1007/s00024-015-1206-7
McFarquhar GM, Zhang H, Heymsfield G, Halverson JB, Hood R, Dudhia J, Marks F Jr (2006) Factors affecting the evolution of Hurricane Erin (2001) and the distributions of hydrometeors: role of microphysical processes. J Atmos Sci 63(1):127–150. https://doi.org/10.1175/JAS3590.1
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 102(D14):16663–16682. https://doi.org/10.1029/97JD00237
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. https://doi.org/10.1016/j.atmosres.2019.104651
Mohanty UC, Osuri KK, Tallapragada V, Marks FD, Pattanayak S, Mohapatra M, Rathore LS, Gopalakrishnan SG, Niyogi D (2015) A great escape from the Bay of Bengal “Super Sapphire–Phailin” tropical cyclone: a case of improved weather forecast and societal response for disaster mitigation. Earth Interact 19(17):1–11. https://doi.org/10.1175/EI-D-14-0032.1
Mohapatra M, Nayak DP, Sharma RP, Bandyopadhyay BK (2013) Evaluation of official tropical cyclone track forecast over north Indian Ocean issued by India Meteorological Department. J Earth Syst Sci 122(3):589–601. https://doi.org/10.1007/s12040-013-0291-1
Mohapatra M, Bandyopadhyay BK, Tyagi A (2014). Status and plans for operational tropical cyclone forecasting and warning systems in the North Indian Ocean region. Monitoring and prediction of tropical cyclones in the Indian Ocean and climate change. Springer, Dordrecht, pp. 149–168. https://doi.org/10.1007/978-94-007-7720-0_14
Mukhopadhyay P, Taraphdar S, Goswami BN (2011). Influence of moist processes on track and intensity forecast of cyclones over the north Indian Ocean. Journal of Geophysical Research: Atmospheres. 116(D05116). https://doi.org/10.1029/2010JD014700
Osuri KK, Mohanty UC, Routray A, Kulkarni MA, Mohapatra M (2012) Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean. Nat Hazards 63(3):1337–1359. https://doi.org/10.1007/s11069-011-9862-0
Raghavan S, Sen Sarma AK (2000). Tropical cyclone impacts in India and neighbourhood. In Roger & P. Roger (Eds.), Storms, London, Routledge, pp. 339–356.
Raju PVS, Potty J, Mohanty UC (2011) Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model. Meteorol Atmos Phys 113(3–4):125–137. https://doi.org/10.1007/s00703-011-0151-y
Raju PVS, Potty J, Mohanty UC (2012) Simulations of tropical cyclone Nargis over Bay of Bengal using RIMES operational system. Pure and Appl Geophysics 169(10):1909–1920
Rambabu S, Gayatri Vani D, Ramakrishna SSVS et al (2013) Sensitivity of movement and intensity of severe cyclone AILA to the physical processes. J Earth Syst Sci 122:979–990. https://doi.org/10.1007/s12040-013-0319-6
Rao GV, Reddy KV, Navatha Y (2020). Assessment of microphysical parameterization schemes on the track and intensity of titli cyclone using ARW model. Numerical Optimization in Engineering and Sciences. Advances in Intelligent Systems and Computing. Springer, Singapore, pp. 35–42. https://doi.org/10.1007/978-981-15-3215-3_4
Sahoo B, Bhaskaran PK (2016) 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
Saikumar PJ, Ramashri T (2017) Impact of physics parameterization schemes in the simulation of Laila cyclone using the advanced mesoscale weather research and forecasting model. Int J Appl Eng Res 12(22):12645–12651
Sateesh M, Srinivas CV, Raju PVS (2017) Numerical simulation of tropical cyclone Thane: role of boundary layer and surface drag parameterization schemes. Nat Hazards 89(3):1255–1271
Sing KS, Mandal M (2014). Sensitivity of mesoscale simulation of Aila Cyclone to the parameterization of physical processes using WRF Model. In Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change, Springer, Dordrecht, pp. 300–308. https://doi.org/10.1007/978-94-007-7720-0_26
Singh KS, Tyagi B, Verma VK, Maity S (2019) Assessing the performance evaluation of different convective parameterization schemes in simulating the intensity of severe cyclonic storms over the Bay of Bengal region. Meteorol Appl 26(4):597–609. https://doi.org/10.1002/met.1787
Skamarock WC, Klemp JB, Dudhia J, Gill DO, Liu Z, Berner J, Wang W, Powers JG, Duda MG, Barker DM, Huang XY (2019). A description of the advanced research WRF version 4. NCAR Tech. Note NCAR/TN-556+STR, 145 pp. https://doi.org/10.5065/1dfh-6p97.
Srinivas CV, Yesubabu V, Hariprasad KRR, 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. https://doi.org/10.1007/s11069-012-0364-5
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. Monthly Weather Rev 136(12):5095–5115. https://doi.org/10.1175/2008MWR2387.1
Yesubabu V, Srinivas CV, Hariprasad KBRR, Baskaran R (2014) A study on the impact of observation assimilation on the numerical simulation of tropical cyclones JAL and THANE using3DVAR. Pure Appl Geophys 171(8):2023–2042. https://doi.org/10.1007/s00024-013-0741-3
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. Geophysical Research Letters. 38(L15810). https://doi.org/10.1029/2011GL048469
Acknowledgements
The authors sincerely acknowledge the India Meteorological Department (IMD) for providing the best track of the cyclone and the National Centers for Environmental Prediction (NCEP) for providing the analysis and forecast data used to initialize the model. We sincerely thank the anonymous reviewers for their constructive comments and suggestions, which further improved the manuscript.
Funding
The present study is supported by the Department of Science & Technology (DST) under the SPLICE: Climate Change Program [DST/CCP/NCC & CV/138/2017(G)].
Author information
Authors and Affiliations
Contributions
Meenakshi Shenoy: initializing WRF ARW model, data collection, analysis and, preparation of the initial draft of the manuscript. P. V. S. Raju: conceptualization and finalization of the manuscript. V. S. Prasad and K. B. R. R. H. Prasad: reviewing the manuscript.
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Shenoy, M., Raju, P.V.S., Prasad, V.S. et al. Sensitivity of physical schemes on simulation of severe cyclones over Bay of Bengal using WRF-ARW model. Theor Appl Climatol 149, 993–1007 (2022). https://doi.org/10.1007/s00704-022-04102-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-022-04102-8