RainGauge

RainMaker.jl exports the callback RainGauge, a rain gauge that you can place inside a SpeedyWeather simulation to measures precipitation at a given location. The following explains how to use this RainGauge callback.

With a SpectralGrid from SpeedyWeather.jl (see here) you can create RainGauge (it needs to know the spectral grid to interpolate from gridded fields to a given location). In most cases you will probably want to specify the rain gauge's location with lond (0...360˚E, -180...180˚E also work) and latd (-90...90˚N)`

using SpeedyWeather, RainMaker

spectral_grid = SpectralGrid()  # default resolution
rain_gauge = RainGauge(spectral_grid, lond=358.75, latd=51.75)

RainGauge{Float32, AnvilInterpolator{Float32, OctahedralGaussianGrid}} <: AbstractCallback
├ latd::Float64 = 51.75˚N
├ lond::Float64 = 358.75˚E
├ measurement_counter:Int = 0 (uninitialized)
├ tstart::DateTime = 2000-01-01T00:00:00
├ Δt::Second 1800 seconds
├ max_measurements::Int = 100000 (measuring for up to ~5.7 years, 0% recorded)
├ accumulated_rain_large_scale::Vector{Float32}, maximum: 0.0 mm
├ accumulated_rain_convection::Vector{Float32}, maximum: 0.0 mm
└ accumulated total precipitation: 0.000 mm

(also see ?RainGauge for more information). The measurement_counter starts at 0 and just counts up the number of measurements the rain gauge has made, one per timestep – zero also means that the gauge is not initialized. In order to reconstruct the time axis the fields tstart and Δt are used but they will be automatically set when initialized given the time step in the model. At any time you can always reset!(rain_gauge) in order to reset the counter, time and all rainfall measurements. But a RainGauge is also mutable, meaning you can do this by hand too, e.g. rain_gauge.accumulated_rain_large_scale .= 0.

RainGauge has two vectors accumulated_rain_large_scale and accumulated_rain_convection where every entry is one measurement of the given precipitation type at the specified location. One measurement is taken after every time step of the model simulation. In order to preallocate these vectors we use max_measurements as length, meaning those are the maximum number of measurements that will be taken. An info is thrown when this point is reached and also an instance of RainGauge printed to the terminal shows you how many years of measurements you can take and how much of that measurement "memory" is already used, see above. If you want to measure for longer periods you may need to increase max_measurements by setting it as a keyword argument, e.g. RainGauge(spectral_grid, max_measurements=1_000_000)

Adding RainGauge as callback

The RainGauge is implemented as a <: SpeedyWeather.AbstractCallback (<: means "subtype of"). A Callback is an object (technically a struct introducing a new type that belongs to the supertype AbstractCallback) with methods defined that are executed after every time step of the model. A callback therefore allows you to inject any piece of code into a simulation. Many callbacks are "diagnostic" meaning they just read out variables but you could also define "intrusive" callbacks that change the model or the simulation while it is running (not covered here but see Intrusive callbacks).

A RainGauge can be added to a model with add!

model = PrimitiveWetModel(spectral_grid)
add!(model, rain_gauge)

Note that you can create many different (or same) RainGauges and add them all to your model in the same way. This way you can place several "weather stations" across the globe and measure simultaneously. Note that you will need to create several independent RainGauges for that, adding the same RainGauge several times to the model is unlikely what you will want to do (it will measure several times the same precipitation after each time step).

You can also delete! a RainGauge (or any callback) again, but you need to know its key for that which is printed to screen when added or just inspect

model.callbacks

Dict{Symbol, SpeedyWeather.AbstractCallback} with 1 entry:
  :callback_RFtl => RainGauge{Float32, AnvilInterpolator{Float32, OctahedralGau…

then with delete!(model.callbacks, :callback_????) where :callback_???? is a Symbol (an immutable string) identifying the callback you want to delete.

Continuous measurements across simulations

While you can reset!(::RainGauge) your rain gauge manually every time, this will not happen automatically for a new simulation if the rain gauge has already measured. This is so that you can run one simulation, look at the rain gauge measurements and then continue the simulation. Let's try this by running two 10-day simulations.

simulation = initialize!(model)
run!(simulation, period=Day(10))
rain_gauge

RainGauge{Float32, AnvilInterpolator{Float32, OctahedralGaussianGrid}} <: AbstractCallback
├ latd::Float64 = 51.75˚N
├ lond::Float64 = 358.75˚E
├ measurement_counter:Int = 480 (now: 2000-01-11 00:00:00)
├ tstart::DateTime = 2000-01-01T00:00:00
├ Δt::Second 1800 seconds
├ max_measurements::Int = 100000 (measuring for up to ~5.7 years, 0% recorded)
├ accumulated_rain_large_scale::Vector{Float32}, maximum: 5.9743648 mm
├ accumulated_rain_convection::Vector{Float32}, maximum: 4.111715 mm
└ accumulated total precipitation: 10.086 mm

Yay, the rain gauge has measured precipitation, now let us continue

run!(simulation, period=Day(10))
rain_gauge

RainGauge{Float32, AnvilInterpolator{Float32, OctahedralGaussianGrid}} <: AbstractCallback
├ latd::Float64 = 51.75˚N
├ lond::Float64 = 358.75˚E
├ measurement_counter:Int = 960 (now: 2000-01-21 00:00:00)
├ tstart::DateTime = 2000-01-01T00:00:00
├ Δt::Second 1800 seconds
├ max_measurements::Int = 100000 (measuring for up to ~5.7 years, 1% recorded)
├ accumulated_rain_large_scale::Vector{Float32}, maximum: 6.1975307 mm
├ accumulated_rain_convection::Vector{Float32}, maximum: 24.93277 mm
└ accumulated total precipitation: 31.130 mm

which adds another 10 days of measurements as if we had simulated directly 20 days.

Visualising RainGauge measurements

While you always see a summary of a RainGauge printed to the REPL, we can also visualise all measurements nicely with Makie.jl. Because RainMaker.jl has this package as a dependency (particulary the CairoMakie backend) the only thing to do is calling the RainMaker.plot function. We do not export plot as it easily conflicts with other packages exporting a plot function.

RainMaker.plot(rain_gauge)

The RainMaker.plot functions takes as optional keyword argument rate_Δt::Period so that you can change the binwidth of the precipitation rate. Above we have one bin every 6 hours (the default), showing the average rate over the previous 6 hours. You can visualise the hourly precipitation rate with

RainMaker.plot(rain_gauge, rate_Δt=Hour(1))

which just gives you a more fine-grained picture. Arguments can be Hour(::Real), Minute(::Real), Day(::Real) but note that the default model time step at default resolution is 30min, so you do not get any more information when going lower than that.

Visualising accumulated rainfall globally

SpeedyWeather simulations diagnose the accumulated rainfall internally. Which is actually what a RainGauge reads out on every time step at the specified location (but see details in Discarding spin-up). This means you can also visualise a map of the accumulated rainfall since the beginning of the simulation to better understand regional rainfall patterns. SpeedyWeather uses largely SI units internally, but RainGauge converts meters to millimeters because that is the more common unit for rainfall. If we read out SpeedyWeather's fields manually we therefore have to do this conversion manually too. Total precipitation is the sum of convective and large-scale precipitation which we can calculate and visualise like this

# (; a, b) = struct unpacks the fields a, b in struct identified by name, equivalent to
# a = struct.a and b = struct.b
(; precip_large_scale, precip_convection) = simulation.diagnostic_variables.physics
total_precipitation = precip_large_scale + precip_convection
total_precipitation *= 1000    # convert m to mm

using CairoMakie
heatmap(total_precipitation, title="Total precipitation [mm], accumulated")

You can also visualise these fields individually. The accumulation starts when the model is initialized with simulation = initialize!(model) which constructs variables, initialized with zeros, too. This means the accumulation will continue across several run! calls unless you manually set it back via

simulation.diagnostic_variables.physics.precip_large_scale .= 0
simulation.diagnostic_variables.physics.precip_convection .= 0

The . here is important to specify the broadcasting of the scalar 0 on the right to the array on the left. This was not needed in *= 1000 above as scalar times vector/matrix is mathematicall already well defined.

Discarding spin-up

A RainGauge starts measuring accumulated rainfall relative to the first run!(simulation) call after it has been added to the model with add!, see Adding RainGauge as callback. This means that you can run a simulation without a RainGauge and then only start measuring precipitation after some time has passed. You can use this to discard any spin-up ( = adjustment after initial conditions) of a simulation. Let us illustrate this

model = PrimitiveWetModel(spectral_grid)

# add one rain gauge the measures the whole simulation
rain_gauge_from_beginning  = RainGauge(spectral_grid, lond=-1.25, latd=51.75)
add!(model, rain_gauge_from_beginning)

simulation = initialize!(model)
run!(simulation, period=Week(1))

# add another rain gauge that only starts measuring
# after that week we already simulated
rain_gauge_after_spinup  = RainGauge(spectral_grid, lond=-1.25, latd=51.75)
add!(model, rain_gauge_after_spinup)
run!(simulation, period=Day(10))

# now compare them, from the beginning
rain_gauge_from_beginning

RainGauge{Float32, AnvilInterpolator{Float32, OctahedralGaussianGrid}} <: AbstractCallback
├ latd::Float64 = 51.75˚N
├ lond::Float64 = -1.25˚E
├ measurement_counter:Int = 816 (now: 2000-01-18 00:00:00)
├ tstart::DateTime = 2000-01-01T00:00:00
├ Δt::Second 1800 seconds
├ max_measurements::Int = 100000 (measuring for up to ~5.7 years, 1% recorded)
├ accumulated_rain_large_scale::Vector{Float32}, maximum: 6.0231752 mm
├ accumulated_rain_convection::Vector{Float32}, maximum: 20.557806 mm
└ accumulated total precipitation: 26.581 mm

versus

# rain gauge after a 1-week spinup
rain_gauge_after_spinup

RainGauge{Float32, AnvilInterpolator{Float32, OctahedralGaussianGrid}} <: AbstractCallback
├ latd::Float64 = 51.75˚N
├ lond::Float64 = -1.25˚E
├ measurement_counter:Int = 480 (now: 2000-01-18 00:00:00)
├ tstart::DateTime = 2000-01-08T00:00:00
├ Δt::Second 1800 seconds
├ max_measurements::Int = 100000 (measuring for up to ~5.7 years, 0% recorded)
├ accumulated_rain_large_scale::Vector{Float32}, maximum: 1.27823 mm
├ accumulated_rain_convection::Vector{Float32}, maximum: 19.789408 mm
└ accumulated total precipitation: 21.068 mm

As you can see their clocks differ and so does the measured precipitation!

Functions and types

• RainMaker.RainGauge
• RainMaker.plot
• RainMaker.reset!
• RainMaker.reset!
• SpeedyWeather.callback!
• SpeedyWeather.initialize!

plot(gauge::RainGauge; rate_Δt) -> Figure

reset!(
    gauge::RainGauge,
    progn::PrognosticVariables,
    diagn::DiagnosticVariables,
    model::AbstractModel
)

reset!(gauge::RainGauge) -> RainGauge

callback!(
    gauge::RainGauge,
    progn::PrognosticVariables,
    diagn::DiagnosticVariables,
    model::AbstractModel
)

initialize!(
    gauge::RainGauge,
    args...
) -> Union{Nothing, RainGauge}