Diagnostics

Diagnostics are a set of general utilities that can be called on-demand during time-stepping to compute quantities of interest you may want to save to disk, such as the horizontal average of the temperature, the maximum velocity, or to produce a time series of salinity. They also include utilities for diagnosing model health, such as the CFL number or to check for NaNs.

Diagnostics are stored as a list of diagnostics in simulation.diagnostics. Diagnostics can be specified at model creation time or be specified at any later time and appended (or assigned with a key value pair) to simulation.diagnostics.

Most diagnostics can be run at specified frequencies (e.g. every 25 time steps) or specified intervals (e.g. every 15 minutes of simulation time). If you'd like to run a diagnostic on demand then do not specify a frequency or interval (and do not add it to simulation.diagnostics).

We describe the HorizontalAverage diagnostic in detail below but see the API documentation for other diagnostics such as TimeSeries, FieldMaximum, CFL, and NaNChecker.

Horizontal averages

You can create a HorizontalAverage diagnostic by passing a field to the constructor, e.g.

model = Model(grid=RegularCartesianGrid(size=(16, 16, 16), length=(1, 1, 1)))
simulation = Simulation(model, Δt=6, stop_iteration=10)
T_avg = HorizontalAverage(model.tracers.T)
push!(simulation.diagnostics, T_avg)

which can then be called on-demand via T_avg(model) to return the horizontally averaged temperature. When running on the GPU you may want it to return an Array instead of a CuArray in case you want to save the horizontal average to disk in which case you'd want to construct it like

model = Model(grid=RegularCartesianGrid(size=(16, 16, 16), length=(1, 1, 1)))
simulation = Simulation(model, Δt=6, stop_iteration=10)
T_avg = HorizontalAverage(model.tracers.T, return_type=Array)
push!(simulation.diagnostics, T_avg)

You can also use pass an abstract operator to take the horizontal average of any diagnosed quantity. For example, to compute the horizontal average of the vertical component of vorticity:

model = Model(grid=RegularCartesianGrid(size=(16, 16, 16), length=(1, 1, 1)))
simulation = Simulation(model, Δt=6, stop_iteration=10)
u, v, w = model.velocities
ζ = ∂x(v) - ∂y(u)
ζ_avg = HorizontalAverage(ζ)
simulation.diagnostics[:vorticity_profile] = ζ_avg

See HorizontalAverage for more details and options.