# Phillips model of Baroclinic Instability

This example can be viewed as a Jupyter notebook via .

A simulation of the growth of barolinic instability in the Phillips 2-layer model when we impose a vertical mean flow shear as a difference $\Delta U$ in the imposed, domain-averaged, zonal flow at each layer.

## Install dependencies

First let's make sure we have all required packages installed.

using Pkg
pkg"add GeophysicalFlows, Plots, Printf"

## Let's begin

Let's load GeophysicalFlows.jl and some other needed packages.

using GeophysicalFlows, Plots, Printf

using Random: seed!

## Choosing a device: CPU or GPU

dev = CPU()     # Device (CPU/GPU)

## Numerical parameters and time-stepping parameters

n = 128                  # 2D resolution = n²
stepper = "FilteredRK4"  # timestepper
dt = 5e-3           # timestep
nsteps = 10000          # total number of time-steps
nsubs  = 25             # number of time-steps for plotting (nsteps must be multiple of nsubs)

## Physical parameters

L = 2π                   # domain size
μ = 5e-2                 # bottom drag
β = 5                    # the y-gradient of planetary PV

nlayers = 2              # number of layers
f₀, g = 1, 1             # Coriolis parameter and gravitational constant
H = [0.2, 0.8]          # the rest depths of each layer
ρ = [4.0, 5.0]          # the density of each layer

U = zeros(nlayers) # the imposed mean zonal flow in each layer
U[1] = 1.0
U[2] = 0.0

## Problem setup

We initialize a Problem by providing a set of keyword arguments. In this example we don't do any dealiasing to our solution by providing aliased_fraction=0.

prob = MultiLayerQG.Problem(nlayers, dev;
nx=n, Lx=L, f₀=f₀, g=g, H=H, ρ=ρ, U=U, μ=μ, β=β,
dt=dt, stepper=stepper, aliased_fraction=0)

and define some shortcuts.

sol, clock, params, vars, grid = prob.sol, prob.clock, prob.params, prob.vars, prob.grid
x, y = grid.x, grid.y

## Setting initial conditions

Our initial condition is some small-amplitude random noise. We smooth our initial condidtion using the timestepper's high-wavenumber filter.

ArrayType() function returns the array type appropriate for the device, i.e., Array for dev = CPU() and CuArray for dev = GPU().

seed!(1234) # reset of the random number generator for reproducibility
q₀  = 1e-2 * ArrayType(dev)(randn((grid.nx, grid.ny, nlayers)))
q₀h = prob.timestepper.filter .* rfft(q₀, (1, 2)) # apply rfft  only in dims=1, 2
q₀  = irfft(q₀h, grid.nx, (1, 2))                 # apply irfft only in dims=1, 2

MultiLayerQG.set_q!(prob, q₀)

## Diagnostics

Create Diagnostics – energies function is imported at the top.

E = Diagnostic(MultiLayerQG.energies, prob; nsteps=nsteps)
diags = [E] # A list of Diagnostics types passed to "stepforward!" will  be updated every timestep.

## Output

We choose folder for outputing .jld2 files and snapshots (.png files).

filepath = "."
plotpath = "./plots_2layer"
plotname = "snapshots"
filename = joinpath(filepath, "2layer.jld2")

Do some basic file management

if isfile(filename); rm(filename); end
if !isdir(plotpath); mkdir(plotpath); end

And then create Output

get_sol(prob) = sol # extracts the Fourier-transformed solution
function get_u(prob)
sol, params, vars, grid = prob.sol, prob.params, prob.vars, prob.grid

@. vars.qh = sol
streamfunctionfrompv!(vars.ψh, vars.qh, params, grid)
@. vars.uh = -im * grid.l * vars.ψh
invtransform!(vars.u, vars.uh, params)

return vars.u
end

out = Output(prob, filename, (:sol, get_sol), (:u, get_u))

## Visualizing the simulation

We define a function that plots the potential vorticity field and the evolution of energy and enstrophy. Note that when plotting, we decorate the variable to be plotted with Array() to make sure it is brought back on the CPU when vars live on the GPU.

symlims(data) = maximum(abs.(extrema(data))) |> q -> (-q, q)

function plot_output(prob)
Lx, Ly = prob.grid.Lx, prob.grid.Ly

l = @layout Plots.grid(2, 3)
p = plot(layout=l, size = (1000, 600))

for m in 1:nlayers
heatmap!(p[(m-1) * 3 + 1], x, y, Array(vars.q[:, :, m]'),
aspectratio = 1,
legend = false,
c = :balance,
xlims = (-Lx/2, Lx/2),
ylims = (-Ly/2, Ly/2),
clims = symlims,
xticks = -3:3,
yticks = -3:3,
xlabel = "x",
ylabel = "y",
title = "q_"*string(m),
framestyle = :box)

contourf!(p[(m-1) * 3 + 2], x, y, Array(vars.ψ[:, :, m]'),
levels = 8,
aspectratio = 1,
legend = false,
c = :viridis,
xlims = (-Lx/2, Lx/2),
ylims = (-Ly/2, Ly/2),
clims = symlims,
xticks = -3:3,
yticks = -3:3,
xlabel = "x",
ylabel = "y",
title = "ψ_"*string(m),
framestyle = :box)
end

plot!(p[3], 2,
label = ["KE₁" "KE₂"],
legend = :bottomright,
linewidth = 2,
alpha = 0.7,
xlims = (-0.1, 2.35),
ylims = (1e-9, 1e0),
yscale = :log10,
yticks = 10.0.^(-9:0),
xlabel = "μt")

plot!(p[6], 1,
label = "PE",
legend = :bottomright,
linecolor = :red,
linewidth = 2,
alpha = 0.7,
xlims = (-0.1, 2.35),
ylims = (1e-9, 1e0),
yscale = :log10,
yticks = 10.0.^(-9:0),
xlabel = "μt")

end

## Time-stepping the Problem forward

Finally, we time-step the Problem forward in time.

p = plot_output(prob)

startwalltime = time()

anim = @animate for j = 0:round(Int, nsteps / nsubs)
if j % (1000 / nsubs) == 0
cfl = clock.dt * maximum([maximum(vars.u) / grid.dx, maximum(vars.v) / grid.dy])

log = @sprintf("step: %04d, t: %.1f, cfl: %.2f, KE₁: %.3e, KE₂: %.3e, PE: %.3e, walltime: %.2f min", clock.step, clock.t, cfl, E.data[E.i][1][1], E.data[E.i][1][2], E.data[E.i][2][1], (time()-startwalltime)/60)

println(log)
end

for m in 1:nlayers
p[(m-1) * 3 + 1][1][:z] = Array(vars.q[:, :, m])
p[(m-1) * 3 + 2][1][:z] = Array(vars.ψ[:, :, m])
end

push!(p[3][1], μ * E.t[E.i], E.data[E.i][1][1])
push!(p[3][2], μ * E.t[E.i], E.data[E.i][1][2])
push!(p[6][1], μ * E.t[E.i], E.data[E.i][2][1])

stepforward!(prob, diags, nsubs)
MultiLayerQG.updatevars!(prob)
end

mp4(anim, "multilayerqg_2layer.mp4", fps=18)