Tropical cyclone energetics and structure

Emanuel, Kerry

doi:10.1017/cbo9780511735035.010

Cited by 153 publications

(167 citation statements)

References 27 publications

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“…In addition to these factors, vertical shear of the environmental flow is considered an important factor that can limit both the intensification and intensity of a TC (Wu and Cheng, 1999;Emanuel, 2004;Wang and Wu, 2004). Tropical cyclones are influenced by environmental, vertical wind shear at all stages of their life cycle (Gray, 1968;McBride and Zehr, 1981;Tuleya and Kurihara, 1981;Merrill, 1988).…”

Section: Sensitivity To Cumulus Parameterization Schemes (Cps)mentioning

confidence: 99%

Impact of cloud parameterization on the numerical simulation of a super cyclone

2012

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Abstract. This study examines the role of parameterization of convection and explicit moisture processes on the simulated track, intensity and inner core structure of Orissa super cyclone (1999) in Bay of Bengal (north Indian Ocean). Sensitivity experiments are carried out to examine the impact of cumulus parameterization schemes (CPS) using MM5 model (Version 3.7) in a two-way nested domain (D1 and D2) configuration at horizontal resolutions (45-15 km). Three different cumulus parameterization schemes, namely Grell (Gr), Betts-Miller (BM) and updated Kain Fritsch (KF2), are tested. It is noted that track and intensity both are very sensitive to CPS and comparatively, KF2 predicts them reasonably well. Particularly, the rapid intensification phase of the super cyclone is best simulated by KF2 compared to other CPS. To examine the effect of the cumulus parameterization scheme at high resolution (5 km), the three-domain configuration (45-15-5 km resolution) is utilized. Based on initial results, KF2 scheme is used for both the domains (D1 and D2). Two experiments are conducted: one in which KF2 is used as CPS and another in which no CPS is used in the third domain. The intensity is well predicted when no CPS is used in the innermost domain. The sensitivity experiments are also carried out to examine the impact from microphysics parameterization schemes (MPS). Four cloud microphysics parameterization schemes, namely mixed phase (MP), Goddard microphysics with Graupel (GG), Reisner Graupel (RG) and Schultz (Sc), are tested in these experiments. It is noted that the tropical cyclone tracks and intensity variation have considerable sensitivity to the varying cloud microphysical parameterization schemes. The MPS of MP and Sc could very well capture the rapid intensification phase. The final intensity is well predicted by MP, which is overestimated by Sc. The MPS of GG and RG underestimates the intensity.

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Section: Sensitivity To Cumulus Parameterization Schemes (Cps)mentioning

confidence: 99%

Impact of cloud parameterization on the numerical simulation of a super cyclone

2012

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“…Other profiles also exist (e.g., Fujita 1952;Smith et al 1990;Emanuel 2004;Willoughby et al 2006), including a recently revised version of H80 (Holland et al 2010). This study extends our previous work (Fovell and Su 2007;Wong 2008;Fovell et al , 2010 on the influence of model physics, especially cloud microphysics and cloudradiative feedback, on TC structure and track.…”

Section: Introductionmentioning

confidence: 99%

Tropical Cyclone Track and Structure Sensitivity to Initialization in Idealized Simulations: A Preliminary Study

Yang¹,

Fovell²,

Corbosiero³

2011

Terr. Atmos. Ocean. Sci.

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In the absence of environmental steering, tropical cyclone (TC) motion largely reflects "beta drift" owing to differential planetary vorticity advection by the storm's outer circulation. It is known that model physics choices (especially those relating to convection) can significantly alter these outer winds and thus the storm track. Here, semi-idealized simulations are used to explore the influence of the initialization on subsequent vortex evolution and motion. Specifically, TCs bred from a buoyant "bubble" are compared to bogussed vortices having a wide variety of parameterized shapes and sizes matching observations.As expected, the bogussed storms commencing with the strongest outer winds propagated fastest and, as a result, huge structure-dependent position differences quickly appeared. However, the forward speed variation among the initially bogussed TCs subsequently declined as a progressive homogenization harmonized the initially supplied structural differences. The homogenization likely involved model physics such as microphysics. This result casts doubt on the ability of models to retain and propagate forward information supplied at the initialization by advanced data assimilation techniques or parameterized vortex wind profiles.Asymmetries in near-core convective heating emerged as an important structural aspect that survived the homogenization tendency. The bubble and bogussed TCs developed markedly different heating patterns, which appear to help explain why the artificially-established storms tended to move about three times faster than their bubble counterparts. The reasons for this are not presently understood fully.

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“…Once initiated, storm displacements are calculated using the 'beta and advection model' in which 850 and 250 mb environmental steering flows vary randomly but in accordance with the monthly mean, variance and covariances of the reanalysis data or the climate model prediction. Along each simulated track, the Coupled Hurricane Intensity Prediction System (CHIPS), a deterministic ocean-atmospheric coupled model, is used to simulate the intensity evolution of the storm (Emanuel et al 2004). TC risk for a certain area is estimated by examining the frequency and characteristics of storms passing by the area (e.g., crossing a boundary of the area or passing within a certain radius of the center of the area).…”

Section: Tc Risk Assessmentmentioning

confidence: 99%

“…A boundary layer model (e.g., Thompson and Cardone, 1996;Vickery et al, 2009;Kepert, 2010) may be applied to more accurately calculate the surface wind from the gradient condition, but it is nonparametric and more computationally demanding. A number of gradient wind profiles (e.g., Holland, 1980;Jelesnianski et al, 1992;Emanuel, 2004;Emanuel and Rotunno, 2011) have been used in wind and surge analysis; it may be difficult at this point to identify the "best" wind profile, as each profile has its own strengths and limitations. Lin and Chavas (2012) investigated the sensitivities of simulated wind and associated surge fields to these gradient wind profiles and the other above-mentioned factors, which may then also be used to quantify the uncertainties in parametric wind modeling.…”

Section: Wind Modelingmentioning

confidence: 99%