عنوان مقاله [English]
Tropical cyclones (TC) have been investigated from different points of view. Development of forecast of TC intensity and its track is often the shared purpose of all previous researches. To this aim, various empirical indices and different frameworks, based on various parameters, have been defined to provide deep knowledge of TC dynamics and thermodynamics. In this research, using the thermodynamic parameter of entropy, entropy fluxes (including surface, lateral and vertical fluxes) have been calculated. A theoretical framework based on hypothesized mechanism, introduced by Tang and Emanuel (2010), has been used to calculate the vertical flux of entropy. This ideal framework used a set of rigid assumptions including steadiness, axisymmetry and slantwise neutrality to assess the effects of vertical entropy flux on TC intensity via the possible pathway of downdrafts outside the eyewall. The lateral entropy flux has been computed based on radial component of surface wind. Azimuthal average of lateral entropy flux has been calculated to analyze vertical extension and strength of inflow (in the lower part of boundary layer) and also outflow (in the upper part of troposphere). Here, Tropical Cyclone Gonu (TCG) has been focusedon. TCG, formed at 18:00 UTC 1 June 2007 and decayed on 7 June, passed intensity of Saffir-Simpson Category-5 and affected southern coast (Makran) of Iran. All above parameters have been computed and analyzed during TCG lifetime using (1) Era-Interim reanalysis data (from European Center for Medium Range Weather Forecast) with 0.125 degree horizontal resolution, 12 vertical levels from 1000 to 200 hPa and 6-hour time intervals, and (2) data produced by India Meteorological Department. The variables were used both at the surface and also at pressure levels, the surface values were temperature and humidity (both at 2 m height), wind vector (at 10 m height), mixing ratio and sea level pressure. Synoptic–scale analysis has been done using data in a circular region centered by TCG center with a radius of 500 km. Results of horizontal patterns and time series of radial and tangential components of wind vector indicated that the value of radial component was maximized simultaneously with maximum activity of TCG. At TCG peak activity time, the tangential component had a comparatively minimum value embedded between two relative maximum values. Time series analysis showed that the integrated values of all three parameters of surface, vertical and lateral entropy fluxes experienced their extremum values before TCG reached its maximum intensity. It is worthwhile to be noted that their lead time varied from 6 hours (for surface entropy flux), 18 hours (for lateral entropy flux) to 30 hours (for vertical entropy flux). A comparative analysis between the values of entropy fluxes during TCG and those for Haiyan Tropical Cyclone (TCH, the strongest TC formed over the Pacific Ocean), reported by Pegahfar and Gharaylou (2019), indicated that entropy surface flux and lateral entropy flux during TCG were respectively two and one order of magnitude larger than the related values during TCH. In contrast, TCG experienced entropy vertical flux with two orders of magnitude smaller than that during TCH. Hence it can be concluded that the accumulation of energy helped TCG to travel to the higher latitudes. Moreover, the strongest inflow and outflow occurred before and after TCG maximum intensity, respectively. In a period that TCG reaches category-5 intensity and more, vertical extension of inflow layer was minimized while outflow layer started from the lower levels, comparing with results from the beginning of TCG life cycle. Conclusively, findings of this research showed that surface, vertical and lateral entropy fluxes, even in synoptic scale, have the ability to be served as empirical indices and also need to be focused in theoretical, computational and practical frameworks, for all prognostic purposes of TC intensity.
ملکوتی، ح. و علی محمدی، م.، 1393، شبیه سازی طوفان حارهای گونو با استفاده از مدل Advanced Hurricane WRF: حساسیت به طراحی محدودهها، آشیانهسازی، تفکیک افقی و زمان شروع، م. علوم و فنون دریایی، 13، 101-110.
مزرعه فراهانی، م.، احمدی، م. و ثقفی، م.ع.، 1394، ارزیابی نیروهای مؤثر بر تشکیل و تقویت توفان حارهای گونو با استفاده از مدل تحلیلی کیو و بررسی عملکرد مدلهای عددی در تعیین شدت آن، م. فیزیک زمین و فضا، 41، 273-280.
Aberson, S. D. and Franklin, J. L., 1999, Impact on hurricane track and intensity forecasts of GPS dropwindsonde observations from the first-season flights of the NOAA Gulfstream-IV jet aircraft, Bull. Amer. Meteor. Soc., 80, 421–427.
Allahdadi, M. N., Chaichitehrani, N., Allahyar, M. and McGee, L., 2017, Wave Spectral Patterns during a Historical Cyclone: A Numerical Model for Cyclone Gonu in the Northern Oman Sea, Open Journal of Fluid Dynamics, 7(02), 131.
Anthes, R. A., 1974, The dynamics and energetics of mature tropical cyclones, Rev. Geophys. Space Phys., 12, 495–522.
Bister, M. and Emanuel, K., 1998, Dissipative heating and hurricane intensity, Meteor. Atmos. Phys., 65, 233–240.
Bryan, G. and Rotunno, R., 2009a, Evaluation of an analytical model for the maximum intensity of tropical cyclones, J. Atmos. Sci., 66, 3042–3060.
Bryan, G. H. and Rotunno, R., 2009b, The maximum intensity of tropical cyclones in axisymmetric numerical model simulations, Mon. Wea. Rev., 137, 1770–1789.
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), J. Atmos. Sci., 64, 1835–1856.
Cuxart, J., Conangla, L. and Jiménez, M. A., 2015, Evaluation of the surface energy budget equation with experimental data and the ECMWF model in the Ebro Valley. Journal of Geophysical Research: Atmospheres, 120(3), 1008-1022.
Davis, C., Wang, W., Chen, S. S., Chen, Y., Corbosiero, K., DeMaria, M., Dudhia, J., Holland, G., Klemp, J., Michalakes, J., Reeves, H., Rotunno, R., Snyder, C. and Xiao, Q., 2008, Prediction of land falling hurricanes with the advanced hurricane WRF model, Mon. Wea. Rev., 136, 1990–2005.
DeMaria, M., 2009, A simplified dynamical system for tropical cyclone intensity prediction, Mon. Wea. Rev., 137(1), 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(2), 219-233.
Ditchek, S. D., Molinari, J. and Vollaro, D., 2017, Tropical Cyclone Outflow-Layer Structure and Balanced Response to Eddy Forcings, J. Atmos. Sci., 74(1):133-149.
Drennan, W. M., Zhang, J. A., French, J. R., McCormick, C. and Black, P. G., 2007, Turbulent fluxes in the hurricane boundary layer. Part I: Latent heat flux. J. Atmos. Sci., 64:1103–1115.
Emanuel, K. A. and Nolan, D. S., 2004, Tropical cyclone activity and the global climate system, Preprints, 26th Conf. on Hurricanes and Tropical Meteorology.
Emanuel, K. A., 1986, An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance, J. Atmos. Sci., 43, 585–604.
Emanuel, K., 1991, The theory of hurricanes, Annu. Rev. Fluid Mech., 23, 179196.
Emanuel, K., 1995, Sensitivity of tropical cyclones to surface exchange coefficients
and a revised steady-state model incorporating eye dynamics, J. Atmos. Sci., 52, 3969–3976.
Farahani, M. M., Khansalari, S. and Azadi, M., 2017, Evaluation of helicity generation in the tropical storm Gonu, Meteorology and Atmospheric Physics, 129(3), 333-344.
Frank, W. M. and Ritchie, E. A., 2001, Effects of vertical wind shear on the intensity and structure of numerically simulated hurricanes, Monthly weather review, 129(9), 2249-2269.
Holland, G., 1997, The maximum potential intensity of tropical cyclones, J. Atmos. Sci., 54, 2519–2541.
Isaksen, L. and Stoffelen, A., 2000, ERS scatterometer wind data impact on ECMWF's tropical cyclone forecasts, IEEE transactions on geoscience and remote sensing, 38(4), 1885-1892.
Jewtoukoff, V., Hertzog, A., Plougonven, R., Cámara, A. D. L. and Lott, F., 2015, Comparison of gravity waves in the Southern Hemisphere derived from balloon observations and the ECMWF analyses. Journal of the Atmospheric Sciences, 72(9), 3449-3468.
Jones, S. C., 1995, The evolution of vortices in vertical shear. I: Initially barotropic vortices, Quart. J. Roy. Meteor. Soc., 121(524), 821-851.
Jung, T., Gulev, S. K., Rudeva, I. and Soloviov, V., 2006, Sensitivity of extratropical cyclone characteristics to horizontal resolution in the ECMWF model, Quart. J. Roy. Meteor. Soc., 132(619), 1839-1857.
Korty, R. L., Camargo, S. J. and Galewsky, J., 2012, Tropical cyclone genesis factors in simulations of the Last Glacial Maximum. Journal of Climate, 25(12), 4348-4365.
Kumar, P., Kishtawal, C. M., Pal, P. K., 2017, Impact of ECMWF, NCEP, and NCMRWF global model analysis on the WRF model forecast over Indian Region. Theoretical and applied climatology, 127(1-2), 143-151.
Li, J. and Li, T., 2014, Entropy Evolution Characteristics Associated with the Development of the South Asian Monsoon, J. Atmos. Sci., 71, 865-880.
López Carrillo, C. and Raymond D. J., 2005, Moisture tendency equations in a tropical atmosphere, J. Atmos. Sci., 62(5), 1601-1613.
Malkus, J. and Riehl, H., 1960, On the dynamics and energy transformations in steady-state hurricanes, Tellus, 12, 1–20.
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.
Osuri, K. K., Mohanty, U. C., Routray, A., Kulkarni, M. A. and 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, 1337–1359.
Pauluis, O. and Held, I. M., 2002, Entropy budget of an atmosphere in radiative-convective equilibrium, Part I: Maximum work and frictional dissipation, J. Atmos. Sci., 59, 125–139.
Pegahfar, N. and Gharaylou, M., 2019, Entropy Evolution Characteristics during an Intense Tropical Cyclone. Accepted in Meteorology and Applied Physics.
Persing, J. and Montgomery, M. T., 2003, Hurricane superintensity, J. Atmos. Sci., 60, 2349–2371.
Raymond, D. J., Esbensen, S. K., Paulson, C., Gregg, M., Bretherton, C. S., Petersen, W. A., Cifelli, R., Shay, L. K., Ohlmann, C. and Zuidema, P., 2004, EPIC2001 and the coupled ocean– atmosphere system of the tropical east Pacific, Bull. Amer. Meteor. Soc., 85, 1341–1354.
Raymond, D. J., Bretherton, C. S. and Molinari, J., 2006, Dynamics of the intertropical convergence zone of the east Pacific, J. Atmos. Sci., 63, 582–597.
Raymond, D. J., Sessions, S., Sobel, A. and Fuchs, Ž., 2009, The mechanics of gross moist stability, J. Adv. Model. Earth Syst., 1(3).
Raymond, D. J., Raga, G. B., Bretherton, C. S., Molinari, J., Lopez- Carillo, C. and Fuchs, Z., 2003, Convective forcing in the intertropical convergence zone of the eastern Pacific, J. Atmos. Sci., 60, 2064–2082.
Raziei, T. and Sotoudeh, F., 2017, investigation of the accuracy of the european center for medium range weather forecasts (ECMWF) in forecasting observed precipitation in different climates of iran, journal of the earth and space physics, 43(1), 133-147.
Reasor, P. D., Montgomery, M. T. and Grasso, L. D., 2004, A new look at the problem of tropical cyclones in vertical shear flow: Vortex resiliency, J. Atmos. Sci., 61(1), 3-22.
Riemer, M., Montgomery, M. T. and Nicholls, M. E., 2010, A new paradigm for intensity modification of tropical cyclones: Thermodynamic impact of vertical wind shear on the inflow layer, Atmos. Chem. Phys., 10, 3163–3188.
Schecter, D. A., Montgomery, M. T. and Reasor, P. D., 2002, A theory for the vertical alignment of a quasigeostrophic vortex, J. Atmos. Sci., 59(2), 150-168.
Simpson, R. and Riehl, R., 1958, Mid-tropospheric ventilation as a constraint on hurricane development and maintenance, Tech. Conf. on Hurricanes, Amer. Meteor. Soc., Miami Beach, FL, D4–1–D4–10.
Singh, R., Kishtawal, C. M., Pal, P. K. and Joshi, P. C., 2011, Assimilation of the multisatellite data into the WRF model for track and intensity simulation of the Indian Ocean tropical cyclones, Meteorology and atmospheric physics, 111(3-4), 103-119.
Singh, R., Pal, P. K., Kishtawal, C. M. and Joshi, P. C., 2008, The impact of variational assimilation of SSM/I and QuikSCAT satellite observations on the numerical simulation of Indian Ocean tropical cyclone, Weather Forecast 23,460–476.
Smith, R. K., Ulrich, W. and Sneddon, G., 2000, On the dynamics of hurricane‐like vortices in vertical‐shear flows, Quart. J. Roy. Meteor. Soc., 126(569), 2653-2670.
Tang, B. and Emanuel, K., 2010, Midlevel ventilation’s constraint on tropical cyclone intensity, J. Atmos. Sci., 67(6), 1817-1830.
Wang, Y. and Xu, J., 2010, Energy production, frictional dissipation, and maximum intensity of a numerically simulated tropical cyclone, J. Atmos. Sci., 67(1), 97-116.
Wong, M. L. and Chan, J. C., 2004, Tropical cyclone intensity in vertical wind shear, J. Atmos. Sci., 61(15), 1859-1876.
Zhang, X., Xiao, Q. and Patrick, F., 2007, The impact of multisatellite data on the initialization and simulation of Hurricane Lili’s (2002) rapid weakening phase, Mon. Weather Rev., 135, 526–548.