Convective, Moist, and Dynamic Vorticity Vectors
As one of the most important dynamic/thermodynamic parameters, potential vorticity (PV) has been studied to enhance the understanding of the genesis and development of weather systems for more than six decades since it was first introduced by Ertel (1942). PV is conserved in a frictionless, adiabatic flow in a dry atmosphere. Later, moist PV was introduced by replacing potential temperature with the equivalent potential temperature. Moist PV is conserved in frictionless, moist adiabatic processes. Many studies have contributed to understanding the dry and moist PV associated with dynamic and thermodynamic processes in the genesis and development of weather systems (e.g., Bennetts and Hoskins 1979; Emanuel 1979; Danielsen and Hipskind 1980; Thorpe 1985; Hoskins and Berrisford 1988; Xu 1992; Montgomery and Farrell 1993; Cao and Cho 1995; Cho and Cao 1998; Gao et al. 2002). Helicity, as an important dynamic concept, has been applied to the study of convective storms in recent decades (e.g., Lilly 1986; Droegemeier et al. 1993; Tan and Wu 1994) since it was introduced by Betchov (1961). However, PV and helicity cannot be applied to the analysis in the 2D framework.
Convective (CVV), moist (MVV), and dynamic (DVV) vorticity vectors are introduced in the 2D framework by Gao et al. (2004, 2005) and are further studied in the 3D framework by Gao et al. (2007) and Gao (2007). How well do these vorticity vectors represent convective signals? What dominant physical processes determine the variations of these vorticity vectors? What are the differences between the 2D and 3D vorticity vectors? What are the differences between CVV and moist PV and between DVV and helicity in the study of 3D convection? These questions will be discussed in this chapter based on Gao et al. (2004, 2005, 2007) and Gao (2007).
KeywordsVortex Convection Covariance Radar Vorticity
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