meteoEVT: Computation and Visualization of Energetic and Vortical Atmospheric Quantities

<!-- badges: end -->

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, e.g., potential vorticity, Bernoulli function and dynamic state index (DSI) [e.g. Weber and Nevir, 2008, doi:10.1111/j.1600-0870.2007.00272.x], for given gridded data, e.g., ERA5 reanalyses. These quantities can be studied directly or can be used for many applications in meteorology, e.g., the objective identification of vortices or atmospheric fronts. For this purpose, several functions are provided which allow the detection of fronts based on the thermic front parameter [Hewson, 1998, doi:10.1017/S1350482798000553], the F diagnostic [Parfitt et al., 2017, doi:10.1002/2017GL073662] and the DSI [Mack et al., 2022, arXiv:2208.11438]

Installation

Installing the current version of meteoEVT from CRAN: r install.packages("meteoEVT")

Installing the current (development) version of meteoEVT from github: r devtools::install_git("https://github.com/noctiluc3nt/meteoEVT")

Functionality

The package provides functions to calculate: - calc_evt.R: potential temperature, Bernoulli function, vorticity, enstrophy, helicity, Lamb vector, potential vorticity, dynamic state index - calc_tensors.R: vorticity tensor, strain-rate tensor, Q-invariant, kinematic vorticity number, hydrodynamic charge - calc_fronts.R: front identification based on thermic front parameter, F-diagnostic and dynamic state index

Provided auxiliary functions: - diff_operators.R: collection of differential operators - readin.R: handling ERA5 data - plot_functions.R: customized plotting functions

Examples

Here is an example showing the analysis of winter storm ZEYNEP/EUNICE over Germany on 18.02.2022:

``` r

step 1: read in the data

data = readinera5('meteoEVT/inst/extdata/era5storm-zeynep.nc')

step 2: custom plot layout (optional)

par(mfrow=c(2,3)) par(cex.main=1.8,cex.lab=1.8,cex.axis=1.8) par(mar=c(4, 5, 4, 2) + 0.1) nl=length(data$lat) library('latex2exp')

step 3: calculation and visualization

calculate potential temperature

theta=calctheta(data$temp,data$lev) fillhoriz(data$lon,rev(data$lat),theta[,nl:1,3], main='Potential Temperature @ 600 hPa\nStorm ZEYNEP 18.02.2022 18 UTC', levels=c(29,seq(29.5,30.8,0.1),32)*10^1, legendloc='bottomleft', legendtitle=TeX('$\theta$ \[K\]'),xlab='lon',ylab='lat')

calculate Bernoulli function

bernoulli=calcbernoulli(data$temp,data$u,data$v,data$w,data$z) fillhoriz(data$lon,rev(data$lat),bernoulli[,nl:1,3], main='Bernoulli function @ 600 hPa\nStorm ZEYNEP 18.02.2022 18 UTC', levels=c(29,seq(29.4,31,0.1),32)*10^4, legendloc='bottomleft', legendtitle=TeX('B \[$m^2/s^2$\]'),xlab='lon',ylab='lat')

calculate vorticity

xi=calcvorticity(data$u,data$v,data$w,data$lev,lat=data$lat) fillhoriz(data$lon,rev(data$lat),xi[,nl:1,3,3], main='Vorticity @ 600 hPa\nStorm ZEYNEP 18.02.2022 18 UTC', levels=c(-10,seq(-4,4,0.1),10)*10^-4, legendloc='bottomleft', legendtitle=TeX('vorticity \[$s^{-1}$\]'),xlab='lon',ylab='lat')

calculate helicity density

hel=calchelicity(data$u,data$v,data$w,data$lev,lat=data$lat) fillhoriz(data$lon,rev(data$lat),hel[,nl:1,3], main='Helicity @ 600 hPa\nStorm ZEYNEP 18.02.2022 18 UTC', levels=c(-10,seq(-3,3,0.1),10)*10^-2,legendloc='bottomleft', legendtitle=TeX('helicity \[$m/s^2$\]'),xlab='lon',ylab='lat')

calculate PV

pv=calcpv(data$temp,data$u,data$v,data$w,data$lev,lat=data$lat) fillhoriz(data$lon,rev(data$lat),pv[,nl:1,3]*10^6, main='PV @ 600 hPa\nStorm ZEYNEP 18.02.2022 18 UTC', levels=c(-10,seq(0,4,0.1),10), legendloc='bottomleft', legendtitle=TeX('PV \[PVU\]'),xlab='lon',ylab='lat')

calculate DSI

dsi=calcdsi(data$temp,data$u,data$v,data$w,data$z,levp=data$lev,lat=data$lat) fillhoriz(data$lon,rev(data$lat),dsi[,nl:1,3]10^12, main='DSI @ 600 hPa\nStorm ZEYNEP 18.02.2022 18 UTC', levels=c(-10,seq(-1,1,0.1),10)10^-3, legendloc='bottomleft', legend_title=TeX('DSI \[PVU^2/s\]'),xlab='lon',ylab='lat') ```

The output figure looks:

Using the function frontid you can detect atmospheric fronts and analyze their properties: ```r

front identification using the thermic front parameter (example without front statistic)

tfpfronts=frontid(data$temp,levp=data$lev,lat=data$lat,fronts_only=TRUE)

front identification using F diagnostic (example with front statistic)

ffronts=frontid(data$temp,data$u,data$v,data$w,data$z,levp=data$lev,lat=data$lat, method='f',threshold=2,fronts_only=FALSE)

front identification using the dynamic state index (example with statistic)

dsifronts=frontid(data$temp,data$u,data$v,data$w,data$z,levp=data$lev,lat=data$lat, method='dsi',threshold=4*10^-16,fronts_only=FALSE) ``` For details and plots generated with this function, see Mack et al., 2022.

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.
• Parfitt, R., Czaja, A., and Seo, H. (2017). A simple diagnostic for the detection of atmospheric fronts. Geophys. Res. Lett., 44:4351–4358.
• Hewson, T. D. (1998). Objective fronts. Meteorol. Appl., 5:37–65.
• Mack, L., Rudolph, A. and Névir, P. (2022). Identifying atmospheric fronts based on diabatic processes using the dynamic state index (DSI), arXiv:2208.11438.