| meteoEVT-package {meteoEVT} | R Documentation |
Introduction
Description
Energy-Vorticity theory (EVT) is the fundamental theory to describe processes in the atmosphere by combining conserved quantities from hydrodynamics and thermodynamics. The package 'meteoEVT' provides functions to calculate many energetic and vortical quantities, like potential vorticity, Bernoulli function and dynamic state index (DSI) (Weber and Nevir, 2008), for given gridded data, like ERA5 reanalyses. These quantities can be studied directly or can be used for many applications in meteorology, e.g., the objective identification of atmospheric fronts. For this purpose, separate function are provided that allow the detection of fronts based on the thermic front parameter (Hewson, 1998), the F diagnostic (Parfitt et al., 2017) and the DSI (Mack et al., 2022).
Details
Phenomenons in the Earth's atmosphere, like tropical hurricanes or extratropical cyclones, can adequately be chararcterized by a combination of energetic and vortical quantities. These quantities can also be used for a consistent theoretical description of these phenomenons. This package provides functions to calculate Bernoulli function, vorticity, enstrophy, helicity, Lamb vector and potential vorticity based on given gridded data sets. Addiotionally, by using energy-vortex theory an adiabatic, stationary and invisicid basic state of the Earth's atmosphere can be derived, which is itself a solution of the primitive equations. The derivation from this basic state is given by the dynamic state index (DSI), which can be used for the study of, e.g., cyclones and fronts. Recently, the DSI was used to identify atmospheric fronts objectively from reanalysis data and thereby provides an alternative way for front detection. For this purpose, this package provides funtions to calculate the DSI and use it to identify atmospheric fronts. This method can be compared with state-of-the-art front identification methods based on the thermic front parameter or the F diagnostic.
References
Weber, T. and Névir, P. (2008). Storm tracks and cyclone development using the theoretical concept of the Dynamic State Index (DSI). Tellus A, 60(1):1–10, doi:10.1111/j.1600-0870.2007.00272.x.
Parfitt, R., Czaja, A., and Seo, H. (2017). A simple diagnostic for the detection of atmospheric fronts. Geophys. Res. Lett., 44:4351–4358, doi:10.1002/2017GL073662.
Hewson, T. D. (1998). Objective fronts. Meteorol. Appl., 5:37–65, doi:10.1017/S1350482798000553.
Mack, L., Rudolph, A. and Névir, P. (2022). Identifying atmospheric fronts based on diabatic processes using the dynamic state index (DSI), arXiv:2208.11438.