Verification of potential intensity relations for the northwest Indian Ocean tropical cyclones during 1990-2019

Document Type : Research Article


Assistant Professor, Atmospheric Science Center, Iranian National Institute for Oceanography and Atmospheric Science, Tehran, Iran


Prediction of tropical cyclone (TC) intensity has been considered in numerous research studies, due to TC destructive effects. Hence, various parameters were combined in potential intensity relations to show the maximum probable intensity that a TC can achieve. The relations of potential intensity are different, since each relation has been suggested based on various factors affecting TC intensity. In this research, the validity of five potential intensity relations, defined by other researchers for the other basins, was verified for all TCs formed over the northwest of the Indian Ocean from 1990 to 2019. In this duration, sixteen cyclonic storms, nine sever cyclonic storms, ten very sever cyclonic storms and ten extremely severe cyclonic storms occurred. In this research, two sets of data reported by India Meteorological Department (IMD) and reanalysis data from the fifth generation of the European Center for Medium Range Weather Forecast (ECMWF, ERA5) with the horizontal resolution of 0.25 degrees were used. The IMD data included position (latitude/longitude) of the TC’s eye and maximum wind speed. The reanalysis data consisted of meteorological parameters from sea level to the tropopause level, including relative and specific humidity, temperature, pressure, dew point temperature and horizontal wind vector. The first relation for the potential intensity is based on the difference between convective available potential energy values at the radius of maximum wind using saturated and unsaturated air mass. The second one considers the difference between saturated entropy at sea level and environmental value of entropy. The third relation consists of the ratio of difference between upper-level and lower-level temperature to the outflow temperature and also the discrepancy between saturated and unsaturated enthalpy. The fourth relation includs difference of saturated and unsaturated values of equivalent potential temperature at the radius of maximum wind. The last relation not only uses the ratio of temperature of inflow and outflow and discrepancy between surface and boundary layer entropy, but also emphasizs on surface temperature. The ratio of the enthalpy and drag coefficients is used in the all relations, while thermodynamic efficiency is included in some recent relations. The potential intensity values achieved using the empirical relations, were evaluated using the maximum wind speed reported by IMD. The comparison was done based on some statistical indexes and the Taylor diagrams. The statistical indices include (I) index of agreement (IOA), (II) standard deviation, (III) root mean square deviation and (IV) correlation coefficient. For the intensity of depression and deep depression states, the minimum value of IOA was achieved using the first relation, while the other relations produced the close values of around 0.7. For the CS-Category intensity, the first two relations produced the lowest IOA values. For the SCS- Category the last two relations did the best performance, while for VSCS- and ESCS-Categories, the second relation produced the most consistent results. The results from IOA showed that the fifth relation produced the highest agreement with the IMD data. This showed that the discrepancy between sea surface temperature and tropopause temperature and the difference between environmental entropy and inner-core entropy played the most important role in intensification for the first four categories of intensity. However, for the last two categories of intensity the discrepancy between the saturated entropy at surface and entropy of boundary layer produced IOA of 0.73 and 0.75, respectively. It is notable that the difference between saturated equivalent potential temperature and potential temperature of boundary layer, and also difference between temperature of inflow and outflow produced the same results for the beginning state. The other statistical indices were analyzed based on the Taylor diagram focusing on all considered tropical cyclones that were intensified to the various intensities. Conclusions demonstrated that the last and the second potential intensity relations produced the best performance in the all categories for the TCs formed over the northwest of the Indian Ocean during 2019-1990. 


Main Subjects

پگاه فر، ن.، 1400، ارزیابی عملکرد طرحواره‌های همرفت کومه‌ای در مدل HWRF در پیش‌بینی مشخصه‌های توفان حاره‌ای، مطالعه موردی توفان حارهای گونو. م. فیزیک زمین و فضا، 47(1)، 145-174.
doi: 10.22059/jesphys.2021.310820.1007250
علی محمدی، م.، ملکوتی، ح.، راهبانی، م. و آزادی، م.، 1398، ارزیابی داده‌گواری وردشی سه‌بعدی در کاهش حساسیت شبیه‌سازی طوفان حاره‌ای گونو به محدوده‌های انتخابی، مجله ژئوفیزیک ایران، 13(2)، 19-35.
Allahdadi, M. N., Chaichitehrani, N., Jose, F., Nasrollahi, A., Afshar, A. and Allahyar, M., 2018, Cyclone-generated Storm Surge in the Northern Gulf of Oman: A Field Data Analysis during Cyclone Gonu, American Journal of Fluid Dynamics, 8, 10-18.
Bister, M. and Emanuel, K. A., 1998, Dissipative heating and hurricane intensity, Meteorology and Atmospheric Physics, 65, 233-240.
Bister, M. and Emanuel, K. A., 2002, Low frequency variability of tropical cyclone potential intensity. 1. Interannual to interdecadal variability, J. Geophys. Res., 107, ACL-26.
Bryan, G. H., 2008, On the computation of pseudoadiabatic entropy and equivalent potential temperature, Monthly Weather Review, 136, 5239-5245.
Chauvin, F., Royer, J. F. and Déqué, M., 2006, Response of hurricane-type vortices to global warming as simulated by ARPEGE-Climat at high resolution, Climate Dynamics, 27, 377-399.
Cram, T. A., Persing, J., Montgomery, M. T. and Braun, S. A., 2007, A Lagrangian trajectory view on transport and mixing processes between the eye, eyewall, and environment using a high-resolution simulation of Hurricane Bonnie (1998). Journal of the atmospheric sciences, 64(6), 1835-1856.
DeMaria, M., 2009, A simplified dynamical system for tropical cyclone intensity prediction, Monthly Weather Review, 137, 68-82.
DeMaria, M., Knaff, J. A. and Connell, B . H., 2001, A tropical cyclone genesis parameter for the tropical Atlantic, Weather and Forecasting, 16, 219-233.
Emanuel, K. A. and Nolan, D. S., 2004, Tropical cyclone activity and the global climate system. In Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc. A., 240-241.
Emanuel, K. A., 1986, An air-sea interaction theory for tropical cyclones. Part I: Steady-state maintenance, Journal of the Atmospheric Sciences, 43, 585-605.
Emanuel, K. A., 1987, The dependence of hurricane intensity on climate, Nature, 326, 483– 485.
Emanuel, K. A., 1988, The maximum intensity of hurricanes, J. Atmos. Sci., 45, 1143–1155.
Emanuel, K.A., 1994, Atmospheric convection. Oxford University Press on Demand.
Emanuel, K. A., 1995, Sensitivity of tropical cyclones to surface exchange coefficients and a revised steady-state model incorporating eye dynamics, J. Atmos. Sci., 52, 3969– 3976.
Emanuel, K. A., 1997, Some aspects of hurricane inner-core dynamics and energetic, J. Atmos. Sci., 54, 1014–1026.
Emanuel, K. A., 2000, A statistical analysis of tropical cyclone intensity, Mon. Weather Rev., 128, 1139– 1152.
Emanuel, K. A., 2005, Increasing destructiveness of tropical cyclones over the past 30 years, Nature, 436, 686-688.
Goni, G., DeMaria, M., Knaff, J., Sampson, C., Ginis, I., Bringas, F., Mavume, A., Lauer, C., Lin, I.I., Ali, M.M. and Sandery, P., 2009, Applications of satellite-derived ocean measurements to tropical cyclone intensity forecasting, National Oceanic And Atmospheric Administration Rockville Md, 22, 190-197.
Henderson-Sellers, A., Zhang, H., Berz, G., Emanuel, K., Gray, W., Landsea, C., Holland, G., Lighthill, J., Shieh, S.L., Webster, P. and McGuffie, K., 1998, Tropical cyclones and global climate change: A post-IPCC assessment, Bull. Am. Meteorol. Soc., 79, 19–38.
Hill, K. A. and Lackmann, G. M., 2009, Influence of environmental humidity on tropical cyclone size, Monthly Weather Review, 137, 3294-3315.
Holland, G. J., 1997, The maximum potential intensity of tropical cyclones, Journal of the atmospheric sciences, 54, 2519-2541.
Holton, J. R. and Hakim, G. J., 2012, An introduction to dynamic meteorology (vol. 88). Academic press.intensity of tropical cyclones, J. Atmos. Sci., 66, 3042–3060.
Knaff, J. A., Sampson, C. R., DeMaria, M., Marchok, T. P., Gross, J. M. and McAdie, C. J., 2007, Statistical tropical cyclone wind radii prediction using climatology and persistence, Weather and Forecasting, 22, 781-791.
Knutson, T. R., Tuleya, R. E. and Kurihara, Y., 1998, Simulated increase of hurricane intensities in a CO2-warmed climate, Science, 279, 1018-1021.
Lawrence, M. G., 2005, The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications, Bulletin of the American Meteorological Society, 86, 225-233.
Lee, W. C. and Wurman, J., 2005, Diagnosed three-dimensional axisymmetric structure of the Mulhall tornado on 3 May 1999, J. Atmos. Sci., 62, 2373-2393.
Marin, J., Raymond, D. and Raga, G., 2009, Intensification of tropical cyclones in the GFS model, Atmos. Chem. Phys., 9, 1407-1417.
Matyas, C. J., 2010, Associations between the size of hurricane rain fields at landfall and their surrounding environments, Meteorology and atmospheric physics, 106, 135-148.
Moore, T. W. and Dixon, R. W., 2011a, Climatology Of Tornadoes Associated With Gulf Coast-Landfalling Hurricanes, Geographical Review, 101, 371-395.
Moore, T. W. and Dixon, R. W., 2011b, Tropical Cyclone Tornado Casualties, Natural Hazards. 61, 621−634.
Nolan, D. S. and McGauley, M. G., 2012, Tropical cyclogenesis in wind shear: Climatological relationships and physical processes, Cyclones: Formation, Triggers, and Control, 1-36.
Oouchi, K., Yoshimura, J., Yoshimura, H., Mizuta, R., Kusunoki, S. and Noda, A., 2006, Tropical cyclone climatology in a global-warming climate as simulated in a 20 km-mesh global atmospheric model: Frequency and wind intensity analyses, J. Meteor. Soc. Japan, 84, 259-276
Peduzzi, P., Chatenoux, B., Dao, H., De Bono, A., Herold, C., Kossin, J., Mouton, F. and Nordbeck, O., 2012, Global trends in tropical cyclone risk, Nature Clim. Change, 2, 289–294.
Pegahfar, N. and Gharaylou, M., 2018, Sensitivity of an Axi-Symmetric Tropical Cyclone Model to Two External Parameters, International Journal of Coastal and Offshore Engineering, 2, 41-51.
Pegahfar, N. and Gharaylou, M., 2020, Entropy evolution characteristics during an intense tropical cyclone, Meteorology and Atmospheric Physics, 132, 461-482.
Pegahfar, N., 2019, A synoptic-scale investigation of entropy fluxes during Tropical Cyclone Gonu, Journal of the Earth and Space Physics, 45, 459-472.
Pegahfar, N., 2020, Climatic analysis of tropopause during the northwestern Indian Ocean tropical cyclones, Dynamics of Atmospheres and Oceans, accepted in Dynamics of Atmosphere and Oceans, 101195.
Rappaport, E. N., Jiing, J. G., Landsea, C. W., Murillo, S. T. and Franklin, J. L., 2012, The joint hurricane testbed-Its first decade of tropical cyclone research-to-operations activities revisited, Bull. Am. Meteor. Soc., 93, 371–380.
Schwerdt, R., Ho, F. and Watkins, R., 1979, Meteorological criteria for standard project hurricane and probable maximum hurricane windfields: Gulf and Atlantic Coasts of the United States, NOAA Tech. Rep. NWS23. 320 pp.
Tang, B. and Emanuel, K., 2010, Midlevel Ventilation's Constraint on Tropical Cyclone Intensity, Journal of the atmospheric sciences, 67, 1817-1830.
Tang, B. H. A. and Emanuel, K., 2012, A ventilation index for tropical cyclones, Bulletin of the American Meteorological Society, 93, 1901-1912.
Taylor, K. E., Stouffer, R. J. and Meehl, G.A., 2012, An overview of CMIP5 and the experiment design, Bulletin of the American Meteorological Society, 93, 485-498.
Vecchi, G. A. and Soden, B. J., 2007, Effect of remote sea surface temperature change on tropical cyclone potential intensity, Nature, 450, 1066–1070.
Vishnu, S., Sanjay, J. and Krishnan, R., 2019, Assessment of climatological tropical cyclone activity over the north Indian Ocean in the CORDEX-South Asia regional climate models. Climate Dynamics, 53(7), 5101-5118.
Wang, Y. and Wu, C. C., 2004, Current understanding of tropical cyclone structure and intensity changes–a review, Meteorology and Atmospheric Physics, 87, 257-278.
Willmott, C. J., 1981, On the validation of models, Physical geography, 2, 184-194.
Xu, J. and Wang, Y., 2010, Sensitivity of tropical cyclone inner-core size and intensity to the radial distribution of surface entropy flux. Journal of the Atmospheric Sciences, 67(6),1831-1852.