Observational relationship of climatologic beta drift with large‐scale environmental flows

Zhao, Haikun; Wu, Liguang; Zhou, Wei

doi:10.1029/2009gl040126

Cited by 22 publications

(20 citation statements)

References 52 publications

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“…Conversely, cyclonic shear (case CSH) resulted in a more westward but slower vortex motion. This conclusion has been verified by numerical calculations [ Williams and Chan , ; Wang and Li , ] and observational studies [ Zhao et al ., ].…”

Section: Numerical Resultsmentioning

confidence: 99%

Rossby wave energy dispersion from tropical cyclone in zonal basic flows

et al. 2016

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This study investigates tropical cyclone energy dispersion under horizontally sheared flows using a nonlinear barotropic model. In addition to common patterns, unusual features of Rossby wave trains are also found in flows with constant vorticity and vorticity gradients. In terms of the direction of the energy dispersion, the wave train can rotate clockwise and elongate southwestward under anticyclonic circulation (ASH), which contributes to the reenhancement of the tropical cyclone (TC). The wave train even splits into two obvious wavelike trains in flows with a southward vorticity gradient (WSH). Energy dispersed from TCs varies over time, and variations in the intensity of the wave train components typically occur in two stages. Wave-activity flux diagnosis and ray tracing calculations are extended to the frame that moves along with the TC to reveal the concrete progress of wave propagation. The direction of the wave-activity flux is primarily determined by the combination of the basic flow and the TC velocity. Along the flux, the distribution of pseudomomentum effectively illustrates the development of wave trains, particularly the rotation and split of wave propagation. Ray tracing involves the quantitative tracing of wave features along rays, which effectively coincide with the wave train regimes. Flows of a constant shear (parabolic meridional variation) produce linear (nonlinear) wave number variations. For the split wave trains, the real and complex wave number waves move along divergent trajectories and are responsible for different energy dispersion ducts.

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Section: Numerical Resultsmentioning

confidence: 99%

Rossby wave energy dispersion from tropical cyclone in zonal basic flows

et al. 2016

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“…Therefore, in the second experiment we choose

{boldV}_{boldβ}

as a function of the cosine of latitude (

ϕ

), with maxima of 1.0 and 2.5 m s −1 in zonal and meridional directions, i.e.,

{boldV}_{boldβ}

= (2.5

\cos ϕ

, 1.0

\cos ϕ

). The cosine function is used because the β ‐drift changes with Coriolis force (Zhao et al, ). We call this second experiment “betaLat.”…”

Section: Development Of Individual Model Componentsmentioning

confidence: 99%

An Environmentally Forced Tropical Cyclone Hazard Model

Lee

Tippett

Sobel

et al. 2018

J Adv Model Earth Syst

112

116

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A new statistical‐dynamical model is developed for estimating the long‐term hazard of rare, high impact tropical cyclones events globally. There are three components representing the complete storm lifetime: an environmental index‐based genesis model, a beta‐advection track model, and an autoregressive intensity model. All three components depend upon the local environmental conditions, including potential intensity, relative sea surface temperature, 850 and 250 hPa steering flow, deep‐layer mean vertical shear, 850 hPa vorticity, and midlevel relative humidity. The hazard model, using 400 realizations of a 32 year period (approximately 3,000 storms per realization), captures many aspects of tropical cyclone statistics, such as genesis and track density distribution. Of particular note, it simulates the observed number of rapidly intensifying storms, a challenging issue in tropical cyclone modeling and prediction. Using the return period curve of landfall intensity as a measure of local tropical cyclone hazard, the model reasonably simulates the hazard in the western north Pacific (coastal regions of the Philippines, China, Taiwan, and Japan) and the Caribbean islands. In other regions, the observed return period curve can be captured after a local landfall frequency adjustment that forces the total number of landfalls to be the same as that observed while allowing the model to freely simulate the distribution of intensities at landfall.

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“…The source regions of the slow solar wind are not so well established. Analysis of Ulysses and ACE data suggests that in different phases of the solar cycle the slow wind emanates from various local sources such as boundaries of PCH and equatorial coronal holes (ECH) (Neugebauer et al, 1998;Neugebauer et al, 2002;Liewer, Neugebauer and Zurbuchen, 2003), non-coronal hole sources (Zhao, Zurbuchen and Fisk, 2009), helmet streamers (Sheeley et al, 1997) and active regions (ARs) Liewer et al, 2001;Liewer, Neugebauer and Zurbuchen, 2004). These identifications were based on juxtaposition of the solar wind parameters measured near the Earth with different solar structures spatially distributed over the Sun under assumptions that propagation of the plasma flows in the inner corona can be described by the potential current-free magnetic field model and that the solar wind in the heliosphere moves along the Archimedian spiral with constant radial speed.…”

Section: Introductionmentioning

confidence: 99%