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Aerosol activation emulators: GP, ET, and ARG-informed #349

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merged 2 commits into from
Mar 13, 2024
Merged

Aerosol activation emulators: GP, ET, and ARG-informed #349

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69186ac
adding the remaining features from Mikhal's work: GP, ET trainings an…
sajjadazimi Mar 10, 2024
edc4021
avoid hardcoding FT and parameters in ext/Common.jl; add a small docu…
sajjadazimi Mar 13, 2024
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64 changes: 64 additions & 0 deletions ext/Common.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,9 @@
import DataFrames as DF
using DataFramesMeta

import CloudMicrophysics.AerosolModel as AM
import CloudMicrophysics.AerosolActivation as AA

function get_num_modes(df::DataFrame)
i = 1
while true
Expand Down Expand Up @@ -68,6 +71,67 @@
return X
end

function get_ARG_act_frac(data_row::NamedTuple)

FT = Float32
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Would it be possible to pass in the FT and the parameter structs as arguments, instead of defining them here?

aip = CMP.AirProperties(FT)
tps = TD.Parameters.ThermodynamicsParameters(FT)
ap = CMP.AerosolActivationParameters(FT)

num_modes = get_num_modes(data_row)
@assert num_modes > 0
mode_Ns = []
mode_means = []
mode_stdevs = []
mode_kappas = []
w = data_row.velocity
T = data_row.initial_temperature
p = data_row.initial_pressure
for i in 1:num_modes
push!(mode_Ns, data_row[Symbol("mode_$(i)_N")])
push!(mode_means, data_row[Symbol("mode_$(i)_mean")])
push!(mode_stdevs, data_row[Symbol("mode_$(i)_stdev")])
push!(mode_kappas, data_row[Symbol("mode_$(i)_kappa")])
end
ad = AM.AerosolDistribution(
Tuple(
AM.Mode_κ(
mode_means[i],
mode_stdevs[i],
mode_Ns[i],
FT(1),
FT(1),
FT(0),
FT(mode_kappas[i]),
1,
) for i in 1:num_modes
),
)
pv0 = TD.saturation_vapor_pressure(tps, FT(T), TD.Liquid())
vapor_mix_ratio = pv0 / TD.Parameters.molmass_ratio(tps) / (p - pv0)
q_vap = vapor_mix_ratio / (vapor_mix_ratio + 1)
q = TD.PhasePartition(FT(q_vap), FT(0), FT(0))
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Are we sure there is never any cloud condensate in this data?


return collect(
AA.N_activated_per_mode(ap, ad, aip, tps, FT(T), FT(p), FT(w), q),
) ./ mode_Ns
end

function get_ARG_act_frac(X::DataFrame)
return transpose(hcat(get_ARG_act_frac.(NamedTuple.(eachrow(X)))...))

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end

function preprocess_aerosol_data_with_ARG_act_frac(X::DataFrame)
num_modes = get_num_modes(X)
X = DF.transform(
X,
AsTable(All()) =>
ByRow(x -> get_ARG_act_frac(x)) =>
[Symbol("mode_$(i)_ARG_act_frac") for i in 1:num_modes],
)
return preprocess_aerosol_data(X)
end

function target_transform(act_frac)
return @. atanh(2.0 * 0.99 * (act_frac - 0.5))
end
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3 changes: 3 additions & 0 deletions test/Project.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -8,13 +8,16 @@ ClimaParams = "5c42b081-d73a-476f-9059-fd94b934656c"
CloudMicrophysics = "6a9e3e04-43cd-43ba-94b9-e8782df3c71b"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
EvoTrees = "f6006082-12f8-11e9-0c9c-0d5d367ab1e5"
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
GaussianProcesses = "891a1506-143c-57d2-908e-e1f8e92e6de9"
KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
MLJ = "add582a8-e3ab-11e8-2d5e-e98b27df1bc7"
MLJFlux = "094fc8d1-fd35-5302-93ea-dabda2abf845"
MLJModels = "d491faf4-2d78-11e9-2867-c94bc002c0b7"
RootSolvers = "7181ea78-2dcb-4de3-ab41-2b8ab5a31e74"
SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
Thermodynamics = "b60c26fb-14c3-4610-9d3e-2d17fe7ff00c"

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