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Merge pull request #192 from OceanBioME/main
New Box Model merge
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| Original file line number | Diff line number | Diff line change |
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| # # Calibrating a biogeochemical model with `EnsembleKalmanProcesses` | ||
| # | ||
| # In this example we calibrate some of the parameters for the [NPZD](@ref NPZD) model | ||
| # in a simple box model setup using a data assimilation package [EnsembleKalmanProcesses](https://github.com/CliMA/EnsembleKalmanProcesses.jl). | ||
| # First we setup the model and generate synthetic data with "true" parameters. We then | ||
| # define priors and setup an EKP to solve. | ||
| # | ||
| # While this is a very simple situation it illustrates the ease of integration with | ||
| # data assimilation tools. Examples given in the EnsembleKalmanProcesses docs illustrate | ||
| # how the package can be used to solve more complex forward models. | ||
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| # ## Install dependencies | ||
| # First we ensure we have the required dependencies installed | ||
| # ```julia | ||
| # using Pkg | ||
| # pkg "add OceanBioME, Oceananigans, CairoMakie, EnsembleKalmanProcesses, Distributions" | ||
| # ``` | ||
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| using OceanBioME, Oceananigans.Units, Oceananigans, BenchmarkTools | ||
| using Oceananigans.Fields: FunctionField | ||
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| const year = years = 365day | ||
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| # Setup the forward model | ||
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| @inline PAR⁰(t) = 60 * (1 - cos((t + 15days) * 2π / year)) * (1 / (1 + 0.2 * exp(-((mod(t, year) - 200days) / 50days)^2))) + 2 | ||
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| z = -10 # nominal depth of the box for the PAR profile | ||
| @inline PAR_func(t)::Float64 = PAR⁰(t) * exp(0.2z) # Modify the PAR based on the nominal depth and exponential decay | ||
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| function run_box_simulation() | ||
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| clock = Clock(time = Float64(0)) | ||
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| grid = BoxModelGrid() | ||
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| PAR = FunctionField{Center, Center, Center}(PAR_func, grid; clock) | ||
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| biogeochemistry = NutrientPhytoplanktonZooplanktonDetritus(; grid, | ||
| light_attenuation_model = PrescribedPhotosyntheticallyActiveRadiation(PAR)) | ||
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| model = BoxModel(; biogeochemistry, clock) | ||
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| set!(model, N = 10.0, P = 0.1, Z = 0.01) | ||
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| simulation = Simulation(model; Δt = 20minutes, stop_iteration = 1000, verbose = false) | ||
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| #simulation.output_writers[:fields] = JLD2OutputWriter(model, model.fields; filename = "box_benchmarking.jld2", schedule = IterationInterval(20), overwrite_existing = true) | ||
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| fast_output = SpeedyOutput("box_benchmarking.jld2") | ||
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| simulation.callbacks[:output] = Callback(fast_output, IterationInterval(20)) | ||
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| @info "Running the model..." | ||
| run!(simulation) | ||
| end | ||
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| ##### | ||
| ##### results | ||
| ##### | ||
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| # Config: 1000 iterations with output every 8 hours and 20min steps | ||
| # origional @btime 317.5ms (1483607 allocations: 243.03 MiB) | ||
| # removing kernel launching from store tendencies: 291.546 ms (1293607 allocations: 187.95 MiB) | ||
| # removed kernel launching from rk3 substepping: 265.823 ms (1000607 allocations: 79.11 MiB) | ||
| # removed broadcasting in update state: 120.605 ms (619379 allocations: 63.98 MiB) | ||
| # no outputs: 23.523 ms (370344 allocations: 30.31 MiB) | ||
| # outputting every 20 steps: 1.181s | ||
| # outputting every 20 steps with `SpeedyOutput`: 34ms |
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