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"29b5916a-a76c-4e73-9657-3c8fd22e65e6" +ClimaAtmos = "b2c96348-7fb7-4fe0-8da9-78d88439e717" +ClimaCalibrate = "4347a170-ebd6-470c-89d3-5c705c0cacc2" +ClimaCore = "d414da3d-4745-48bb-8d80-42e94e092884" +Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f" +EnsembleKalmanProcesses = "aa8a2aa5-91d8-4396-bcef-d4f2ec43552d" +Insolation = "e98cc03f-d57e-4e3c-b70c-8d51efe9e0d8" +Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59" +JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819" +NCDatasets = "85f8d34a-cbdd-5861-8df4-14fed0d494ab" +NetCDF = "30363a11-5582-574a-97bb-aa9a979735b9" +TOML = "fa267f1f-6049-4f14-aa54-33bafae1ed76" +YAML = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6" diff --git a/calibration/experiments/gcm_driven_scm/experiment_config.yml b/calibration/experiments/gcm_driven_scm/experiment_config.yml new file mode 100644 index 00000000000..08ce1b4c676 --- /dev/null +++ b/calibration/experiments/gcm_driven_scm/experiment_config.yml @@ -0,0 +1,12 @@ +prior: prior.toml +ensemble_size: 10 +n_iterations: 2 +observations: obs_mean.jld2 +noise: obs_noise_cov.jld2 +output_dir: output/gcm_driven_scm +y_var_names: [thetaa, hus, clw] +dims : 180 # num vertical levels x num variables +y_t_start_sec : 475200.0 # 5.5 days (LES length = 6 days) +y_t_end_sec : 518400.0 # 6.0 days +g_t_start_sec : 216000.0 # 2.5 days(SCM length = 3 days) +g_t_end_sec : 259200.0 # 3 days \ No newline at end of file diff --git a/calibration/experiments/gcm_driven_scm/helper_funcs.jl b/calibration/experiments/gcm_driven_scm/helper_funcs.jl new file mode 100644 index 00000000000..1a32245384e --- /dev/null +++ b/calibration/experiments/gcm_driven_scm/helper_funcs.jl @@ -0,0 +1,644 @@ + +using NCDatasets +using Interpolations +using Statistics +import ClimaAtmos as CA + +"Optional vector" +const OptVec{T} = Union{Nothing, Vector{T}} + +"Optional real" +const OptReal = Union{Real, Nothing} + + +CLIMADIAGNOSTICS_LES_NAME_MAP = + Dict("thetaa" => "theta_mean", "hus" => "qt_mean", "clw" => "ql_mean") + + + +"""Get z coordinates of CA run, given config. """ +function get_z_grid(atmos_config) + params = CA.create_parameter_set(atmos_config) + spaces = + CA.get_spaces(atmos_config.parsed_args, params, atmos_config.comms_ctx) + coord = CA.Fields.coordinate_field(spaces.center_space) + return convert(Vector{Float64}, parent(coord.z)[:]) +end + + +""" + is_timeseries(filename::String, var_name::String) + +A `Bool` indicating whether the given variable is a timeseries. +""" +function is_timeseries(filename::String, var_name::String) + NCDataset(filename) do ds + if haskey(ds.group, "timeseries") + if haskey(ds.group["timeseries"], var_name) + return true + else + return false + end + else + return false + end + end +end + +""" + is_face_variable(filename::String, var_name::String) + +A `Bool` indicating whether the given variable is defined in a face, +or not (cell center). + +TurbulenceConvection data are consistent, meaning that variables at +cell faces (resp. centers) have as dim `zf` (resp., `zc`). + +PyCLES variables are inconsistent. All variables have as a dim `z`, +the cell face locations, but most of them (except for the statistical +moments of w) are actually defined at cell centers (`z_half`). +""" +function is_face_variable(filename::String, var_name::String) + # PyCLES cell face variables + pycles_face_vars = ["w_mean", "w_mean2", "w_mean3"] + + NCDataset(filename) do ds + for group_option in ["profiles", "reference"] + haskey(ds.group, group_option) || continue + if haskey(ds.group[group_option], var_name) + var_dims = dimnames(ds.group[group_option][var_name]) + if ("zc" in var_dims) | ("z_half" in var_dims) + return false + elseif ("zf" in var_dims) | (var_name in pycles_face_vars) + return true + elseif ("z" in var_dims) # "Inconsistent" PyCLES variables, defined at cell centers. + return false + else + throw( + ArgumentError( + "Variable $var_name does not contain a vertical coordinate.", + ), + ) + end + end + end + end +end + +""" + get_height(filename::String; get_faces::Bool = false) + +Returns the vertical cell centers or faces of the given configuration. + +Inputs: + - filename :: nc filename. + - get_faces :: If true, returns the coordinates of cell faces. Otherwise, + returns the coordinates of cell centers. +Output: + - z: Vertical level coordinates. +""" +function get_height(filename::String; get_faces::Bool = false) + return get_faces ? nc_fetch(filename, ("zf", "z")) : + nc_fetch(filename, ("zc", "z_half")) +end + +""" + nc_fetch(filename::String, var_names::NTuple{N, Tuple}) where {N} + nc_fetch(filename::String, var_name::String) + +Returns the data for a variable `var_name` (or +tuple of strings, `varnames`), looping through +all dataset groups. +""" +function nc_fetch(filename::String, var_names::Tuple) + NCDataset(filename) do ds + for var_name in var_names + if haskey(ds, var_name) + return Array(ds[var_name]) + else + for group_option in ["profiles", "reference", "timeseries"] + haskey(ds.group, group_option) || continue + if haskey(ds.group[group_option], var_name) + return Array(ds.group[group_option][var_name]) + end + end + end + end + error( + "Variables $var_names not found in the output netCDF file $filename.", + ) + end +end +nc_fetch(filename::String, var_name::String) = nc_fetch(filename, (var_name,)) + + + +""" + nc_fetch_interpolate(var_name::String, filename::String, z_scm::OptVec{<:Real}) + +Returns the netcdf variable `var_name`, possibly interpolated to heights `z_scm`. + +Inputs: + - `var_name` :: Name of variable in the netcdf dataset. + - `filename` :: nc filename + - `z_scm` :: Vertical coordinate vector onto which var_name is interpolated. +Output: + - The interpolated vector. +""" +function nc_fetch_interpolate( + var_name::String, + filename::String, + z_scm::OptVec{<:Real}, +) + if !is_timeseries(filename, var_name) && !isnothing(z_scm) + return vertical_interpolation(var_name, filename, z_scm) + else + return nc_fetch(filename, var_name) + end +end + +""" + fetch_interpolate_transform(var_name::String, filename::String, z_scm::OptVec{<:Real}) + +Returns the netcdf variable `var_name`, possibly interpolated to heights `z_scm`. If the +variable needs to be transformed to be equivalent to an SCM variable, applies the +transformation as well. + +Inputs: + - `var_name` :: Name of variable in the netcdf dataset. + - `filename` :: nc filename + - `z_scm` :: Vertical coordinate vector onto which var_name is interpolated. +Output: + - The interpolated and transformed vector. + +### PyCLES variables that require transformations: + +- PyCLES diagnostic vertical fluxes (defined in [AuxiliaryStatistics.pyx](https://github.com/CliMA/pycles/blob/master/AuxiliaryStatistics.pyx#L845)) are specific quantities, + not multiplied by density, and written at cell centers. These include all `resolved_z_flux_(...)` and + `sgs_z_flux_(...)` diagnostics. For instance the `resolved_z_flux_theta` is ``\\langle{w^*\\theta^*}\\rangle``. + In contrast, all `massflux_(...)`, `diffusive_flux_(...)` and `total_flux_(...)` outputs from + TC.jl are already multiplied by density and written at cell faces; e.g. `total_flux_h` is ``\\rho\\langle{w^*\\theta^*}\\rangle``. + The location mismatch is handled through `is_face_variable` and interpolation. Another difference + is that the `total_flux_(...)` in TC.jl simulations includes the full flux, whereas the PyCLES `resolved` + definitions only include the resolved flux. We must add the `sgs_z_flux_(...)` component here. + + +- PyCLES prognostic vertical fluxes (defined in [ScalarAdvection.pyx](https://github.com/CliMA/pycles/blob/master/ScalarAdvection.pyx#L136), + [ScalarDiffusion.pyx](https://github.com/CliMA/pycles/blob/master/ScalarDiffusion.pyx#L174), MomentumAdvection.pyx, + MomentumDiffusion.pyx) are defined at cell centers and have already been multiplied by density. They are computed + at cell faces in the low-level functions in [`scalar_advection.h`](https://github.com/CliMA/pycles/blob/master/Csrc/scalar_diffusion.h#L31) and `scalar_diffusion.h`, + and then [interpolated](https://github.com/CliMA/pycles/blob/master/ScalarDiffusion.pyx#L173) + in the `.pyx` files before they are written to file. These include all + `(...)_flux_z` and `(...)__sgs_flux_z` fluxes. In contrast, flux diagnostics from TC.jl are defined + at cell faces. This mismatch is handled through `is_face_variable`. Another difference is that the `total_flux_(...)` + in TC.jl simulations includes the full flux, whereas the PyCLES `(...)_flux_z` definition only includes the resolved + flux. We must add the `(...)__sgs_flux_z` component here. + PyCLES `sgs_z_flux` and `resolved` flux fields do not include a contribution from the surface flux, + so the bottom cell is set to the diagnosed surface flux. +""" +function fetch_interpolate_transform( + var_name::String, + filename::String, + z_scm::OptVec{<:Real}, +) + # Multiply by density, add sgs flux + if occursin("resolved_z_flux", var_name) + resolved_flux = nc_fetch_interpolate(var_name, filename, z_scm) + sgs_flux_name = + string("sgs_z_flux", last(split(var_name, "resolved_z_flux"))) + sgs_flux = nc_fetch_interpolate(sgs_flux_name, filename, z_scm) + rho_half = nc_fetch_interpolate("rho0_half", filename, z_scm) + total_flux = rho_half .* (resolved_flux .+ sgs_flux) + total_flux = rectify_surface_flux(total_flux, var_name, filename, z_scm) + var_ = total_flux + + # Add sgs flux + elseif occursin("_flux_z", var_name) + resolved_flux = nc_fetch_interpolate(var_name, filename, z_scm) + sgs_flux_name = string(first(split(var_name, "flux_z")), "sgs_flux_z") + sgs_flux = nc_fetch_interpolate(sgs_flux_name, filename, z_scm) + total_flux = resolved_flux .+ sgs_flux + total_flux = rectify_surface_flux(total_flux, var_name, filename, z_scm) + var_ = total_flux + + # Combine horizontal velocities + elseif var_name == "horizontal_vel" + u_ = nc_fetch_interpolate("u_mean", filename, z_scm) + v_ = nc_fetch_interpolate("v_mean", filename, z_scm) + var_ = sqrt.(u_ .^ 2 + v_ .^ 2) + + else + var_ = nc_fetch_interpolate(var_name, filename, z_scm) + end + return var_ +end + +""" + rectify_surface_flux(interpolated_var::Vector{FT}, var_name::String, filename::String, z_scm::OptVec{<:Real}) + +Sets bottom cell in interpolated flux profile equal to surface flux. This is needed for +LES profiles since neither the resolved nor the SGS fluxes include contributions +from the surface flux (otherwise flux goes to zero at the surface). + +Inputs: + - `interpolated_var` :: Interpolated variable vector. + - `var_name` :: Name of variable in the netcdf dataset. + - `filename` :: nc filename + - `z_scm` :: Vertical coordinate vector onto which var_name is interpolated. +Output: + - Flux profile with bottom cell set equal to surface flux. + """ + +function rectify_surface_flux( + interpolated_var::Array{FT}, + var_name::String, + filename::String, + z_scm::OptVec{<:Real}, +) where {FT} + if z_scm != nothing + min_z_index = argmin(z_scm) + else + min_z_index = 1 + end + + if occursin("qt", var_name) + lhf_surface = nc_fetch_interpolate("lhf_surface_mean", filename, z_scm) + t_profile = nc_fetch_interpolate("temperature_mean", filename, z_scm) + surf_qt_flux = + lhf_surface ./ + TD.latent_heat_vapor.(thermo_param_set, t_profile[min_z_index]) + interpolated_var[min_z_index, :] .= surf_qt_flux + return interpolated_var + elseif occursin("s", var_name) + surf_s_flux = + nc_fetch_interpolate("s_flux_surface_mean", filename, z_scm) + interpolated_var[min_z_index, :] .= surf_s_flux + return interpolated_var + else + @warn "Surface flux correcton not implemented for $(var_name). Check consistency of flux definitions." + end +end + + +""" + normalize_profile( + y::Array{FT}, + norm_vec::Array{FT}, + prof_dof::IT, + prof_indices::OptVec{Bool} = nothing, + ) where {FT <: Real, IT <: Integer} + +Perform normalization of the aggregate observation vector `y` using separate +normalization constants for each variable, contained in `norm_vec`. + +Inputs: + - `y` :: Aggregate observation vector. + - `norm_vec` :: Vector of squares of normalization factors. + - `prof_dof` :: Degrees of freedom of vertical profiles contained in `y`. + - `prof_indices` :: Vector of booleans specifying which variables are profiles, and which + are timeseries. +- `z_score_norm` :: z-score normalization +Output: + - The normalized aggregate observation vector. +""" +function normalize_profile( + y::Array{FT}, + y_names, + prof_dof::IT, + prof_indices::OptVec{Bool} = nothing; + z_score_norm::Bool = true, +) where {FT <: Real, IT <: Integer} + y_ = deepcopy(y) + n_vars = length(y_names) + prof_indices = + isnothing(prof_indices) ? repeat([true], n_vars) : prof_indices + norm_vec = Array{Float64}(undef, n_vars, 2) + loc_start = 1 + for i in 1:n_vars + loc_end = prof_indices[i] ? loc_start + prof_dof - 1 : loc_start + + if z_score_norm + y_i = y_[loc_start:loc_end] + y_μ, y_σ = mean(y_i), std(y_i) + y_[loc_start:loc_end] = (y_i .- y_μ) ./ y_σ + norm_vec[i, :] = [y_μ, y_σ] + else + y_[loc_start:loc_end] = y_[loc_start:loc_end] ./ sqrt(norm_vec[i]) + end + loc_start = loc_end + 1 + end + return y_, norm_vec +end + +""" + vertical_interpolation( + var_name::String, + filename::String, + z_scm::Vector{FT}; + ) where {FT <: AbstractFloat} + +Returns the netcdf variable `var_name` interpolated to heights `z_scm`. + +Inputs: + - `var_name` :: Name of variable in the netcdf dataset. + - `filename` :: nc filename + - `z_scm` :: Vertical coordinate vector onto which var_name is interpolated. +Output: + - The interpolated vector. +""" +function vertical_interpolation( + var_name::String, + filename::String, + z_scm::Vector{FT}; +) where {FT <: AbstractFloat} + z_ref = + get_height(filename, get_faces = is_face_variable(filename, var_name)) + var_ = nc_fetch(filename, var_name) + if ndims(var_) == 2 + # Create interpolant + nodes = (z_ref, 1:size(var_, 2)) + var_itp = extrapolate( + interpolate(nodes, var_, (Gridded(Linear()), NoInterp())), + Line(), + ) + # Return interpolated vector + return var_itp(z_scm, 1:size(var_, 2)) + elseif ndims(var_) == 1 + # Create interpolant + nodes = (z_ref,) + var_itp = LinearInterpolation(nodes, var_; extrapolation_bc = Line()) + # Return interpolated vector + return var_itp(z_scm) + end +end + + +""" + get_profile( + filename::String, + y_names::Vector{String}; + ti::Real = 0.0, + tf::OptReal = nothing, + z_scm::Union{Vector{T}, T} = nothing, + prof_ind::Bool = false, + ) where {T} + +Get time-averaged profiles for variables `y_names`, interpolated to +`z_scm` (if given), and concatenated into a single output vector. + +Inputs: + + - `filename` :: nc filename + - `y_names` :: Names of variables to be retrieved. + - `ti` :: Initial time of averaging window. + - `tf` :: Final time of averaging window. + - `z_scm` :: If given, interpolate LES observations to given levels. + - `m` :: ReferenceModel from which to fetch profiles, implicitly defines `ti` and `tf`. + - `prof_ind` :: Whether to return a boolean array indicating the variables that are profiles (i.e., not scalars). + +Outputs: + + - `y` :: Output vector used in the inverse problem, which concatenates the requested profiles. +""" +function get_profile( + filename::String, + y_names::Vector{String}; + ti::Real = 0.0, + tf::OptReal = nothing, + z_scm::OptVec{T} = nothing, + prof_ind::Bool = false, +) where {T} + + t = nc_fetch(filename, "t") + dt = length(t) > 1 ? mean(diff(t)) : 0.0 + y = zeros(0) + is_profile = Bool[] + + # Check that times are contained in simulation output + Δt_start, ti_index = findmin(broadcast(abs, t .- ti)) + # If simulation does not contain values for ti or tf, return high value (penalization) + if t[end] < ti + @warn string( + "Note: t_end < ti, which means that simulation stopped before reaching the requested t_start.", + "Requested t_start = $ti s. However, the last time available is $(t[end]) s.", + "Defaulting to penalized profiles...", + ) + for i in 1:length(y_names) + var_ = isnothing(z_scm) ? get_height(filename) : z_scm + append!(y, 1.0e5 * ones(length(var_[:]))) + end + return prof_ind ? (y, repeat([true], length(y_names))) : y + end + if !isnothing(tf) + Δt_end, tf_index = findmin(broadcast(abs, t .- tf)) + if t[end] < tf - dt + @warn string( + "Note: t_end < tf - dt, which means that simulation stopped before reaching the requested t_end.", + "Requested t_end = $tf s. However, the last time available is $(t[end]) s.", + "Defaulting to penalized profiles...", + ) + for i in 1:length(y_names) + var_ = isnothing(z_scm) ? get_height(filename) : z_scm + append!(y, 1.0e5 * ones(length(var_[:]))) + end + return prof_ind ? (y, repeat([true], length(y_names))) : y + end + end + + # Return time average for non-degenerate cases + for var_name in y_names + var_ = fetch_interpolate_transform(var_name, filename, z_scm) + if ndims(var_) == 2 + var_mean = + !isnothing(tf) ? mean(var_[:, ti_index:tf_index], dims = 2) : + var_[:, ti_index] + append!(is_profile, true) + elseif ndims(var_) == 1 + var_mean = + !isnothing(tf) ? mean(var_[ti_index:tf_index]) : var_[ti_index] + append!(is_profile, false) + end + append!(y, var_mean) + end + return prof_ind ? (y, is_profile) : y +end + + + +""" + get_obs(filename::String,, y_names, normalize; [z_scm]) + +Get observational mean `y` and empirical time covariance `Σ`. + +The keyword `normalize` specifies whether observations are to be normalized with respect to the +per-quantity pooled variance or not. See [`normalize_profile`](@ref) for details. +The normalization vector is returned along with `y` and `Σ`. + +If `z_scm` is given, interpolate observations to the given levels. + +# Arguments +- `m` :: A `ReferenceModel`](@ref) +- `y_names` :: Names of observed fields from the [`ReferenceModel`](@ref) `m`. + +# Keywords +- `z_scm` :: If given, interpolate LES observations to given array of vertical levels. +- `Σ_const` :: Constant diagonal noise +- `model_error` :: Model error per variable, added to the internal variability noise, and + normalized by the pooled variance of the variable. + +# Returns +- `y::Vector` :: Mean of observations `y`, possibly interpolated to `z_scm` levels. +- `Σ::Matrix` :: Observational covariance matrix `Σ`, possibly pool-normalized. +- `norm_vec::Matrix` :: Normalization mean & std, one row for each variable +""" +function get_obs( + filename::String, + y_names::Vector{String}, + z_scm::OptVec{FT} = nothing; + ti::FT = 5.5 * 3600 * 24, + tf::FT = 6.0 * 3600 * 24, + model_error::OptVec{FT} = nothing, + pooled_var_dict::Union{Dict, Nothing} = nothing, + Σ_const::FT = nothing, +) where {FT <: Real} + + # map to CA names to LES names + y_names = [CLIMADIAGNOSTICS_LES_NAME_MAP[var_i] for var_i in y_names] + if !isnothing(pooled_var_dict) + pooled_var_dict = Dict( + CLIMADIAGNOSTICS_LES_NAME_MAP[var_i] => value for + (var_i, value) in pooled_var_dict + ) + end + + # Get true observables + y, prof_indices = get_profile( + filename, + y_names, + z_scm = z_scm, + ti = ti, + tf = tf, + prof_ind = true, + ) + # normalize + y, norm_vec = normalize_profile(y, y_names, length(z_scm), prof_indices) + + + if !isnothing(Σ_const) + Σ = Diagonal(Σ_const * ones(length(y))) + else + # time covariance + Σ, pool_var = get_time_covariance( + filename, + y_names, + z_scm, + model_error = model_error, + pooled_var_dict = pooled_var_dict, + ) + end + + return y, Σ, norm_vec +end + + +""" + get_time_covariance( + y_names::Vector{String}, + z_scm::Vector{FT}; + normalize::Bool = true, + model_error::OptVec{FT} = nothing, + pooled_var_dict::Union{Dict, Nothing} = nothing, + ) where {FT <: Real} + +Obtain the covariance matrix of a group of profiles, where the covariance +is obtained in time. + +Inputs: + - `y_names` :: List of variable names to be included. + - `z_scm` :: If given, interpolates covariance matrix to this locations. + - `normalize` :: Whether to normalize the time series with the pooled variance + before computing the covariance, or not. +- `model_error` :: Model error per variable, added to the internal variability noise, and + normalized by the pooled variance of the variable. +- `pooled_var_dict` :: Dict of precomputed pooled variances +""" +function get_time_covariance( + filename::String, + y_names::Vector{String}, + z_scm::Vector{FT}; + ti::FT = 72000.0, + normalize::Bool = false, + model_error::OptVec{FT} = nothing, + pooled_var_dict::Union{Dict, Nothing} = nothing, +) where {FT <: Real} + t = nc_fetch(filename, "t") + # Find closest interval in data + ti_index = argmin(broadcast(abs, t .- ti)) + + tf_index = length(t) + N_samples = length(ti_index:tf_index) + ts_vec = zeros(0, N_samples) + num_outputs = length(y_names) + pool_var = zeros(num_outputs) + model_error_expanded = Vector{FT}[] + + for (i, var_name) in enumerate(y_names) + + var_ = fetch_interpolate_transform(var_name, filename, z_scm) + + if ndims(var_) == 2 + if !isnothing(pooled_var_dict) + if haskey(pooled_var_dict, var_name) + pool_var[i] = pooled_var_dict[var_name] + end + else + # Store pooled variance + pool_var[i] = + mean(var(var_[:, ti_index:tf_index], dims = 2)) + eps(FT) # vertically averaged time-variance of variable + end + + # Normalize timeseries + ts_var_i = + normalize ? var_[:, ti_index:tf_index] ./ sqrt(pool_var[i]) : + var_[:, ti_index:tf_index] # dims: (Nz, Nt) + elseif ndims(var_) == 1 + if !isnothing(pooled_var_dict) + if haskey(pooled_var_dict, var_name) & + !isnothing(pooled_var_dict) + pool_var[i] = pooled_var_dict[var_name] + end + else + # Store pooled variance + pool_var[i] = var(var_[ti_index:tf_index]) + eps(FT) # time-variance of variable + end + # Normalize timeseries + ts_var_i = + normalize ? + Array(var_[ti_index:tf_index]') ./ sqrt(pool_var[i]) : + Array(var_[ti_index:tf_index]') # dims: (1, Nt) + else + throw( + ArgumentError( + "Variable `$var_name` has more than 2 dimensions, 1 or 2 were expected.", + ), + ) + end + ts_vec = cat(ts_vec, ts_var_i, dims = 1) # final dims: (Nz*num_profiles + num_timeseries, Nt) + + # Add structural model error + if !isnothing(model_error) + var_model_error = + normalize ? model_error[i] : model_error[i] * pool_var[i] + model_error_expanded = cat( + model_error_expanded, + repeat([var_model_error], size(ts_var_i, 1)), + dims = 1, + ) + end + end + cov_mat = cov(ts_vec, dims = 2) # covariance, w/ samples across time dimension (t_inds). + cov_mat = + !isnothing(model_error) ? + cov_mat + Diagonal(FT.(model_error_expanded)) : cov_mat + return cov_mat, pool_var +end diff --git a/calibration/experiments/gcm_driven_scm/model_config.yml b/calibration/experiments/gcm_driven_scm/model_config.yml new file mode 100644 index 00000000000..b4886e0feac --- /dev/null +++ b/calibration/experiments/gcm_driven_scm/model_config.yml @@ -0,0 +1,35 @@ +initial_condition: "GCM" +external_forcing: "GCM" +external_forcing_file: "/groups/esm/zhaoyi/GCMForcedLES/cfsite/07/HadGEM2-A/amip/Output.cfsite23_HadGEM2-A_amip_2004-2008.07.4x/stats/Stats.cfsite23_HadGEM2-A_amip_2004-2008.07.nc" +surface_setup: "GCM" +turbconv: "prognostic_edmfx" +implicit_diffusion: true +implicit_sgs_advection: false +approximate_linear_solve_iters: 2 +max_newton_iters_ode: 1 +edmfx_upwinding: first_order +edmfx_entr_model: "Generalized" +edmfx_detr_model: "Generalized" +edmfx_sgs_mass_flux: true +edmfx_sgs_diffusive_flux: true +edmfx_nh_pressure: true +edmfx_filter: true +prognostic_tke: true +moist: "equil" +config: "column" +z_max: 4e3 +z_elem: 60 +z_stretch: false +perturb_initstate: false +dt: "10secs" +t_end: "72hours" +dt_save_to_disk: "10mins" +call_cloud_diagnostics_per_stage : true +output_dir: output/gcm_driven_scm +toml: [scm_tomls/prognostic_edmfx_bomex.toml] +netcdf_output_at_levels: true +netcdf_interpolation_num_points: [2, 2, 60] +# output_default_diagnostics: false +diagnostics: + - short_name: [hus, thetaa, clw, arup, tke, entr, detr, waup] + period: 10mins \ No newline at end of file diff --git a/calibration/experiments/gcm_driven_scm/observation_map.jl b/calibration/experiments/gcm_driven_scm/observation_map.jl new file mode 100644 index 00000000000..73aa76ca401 --- /dev/null +++ b/calibration/experiments/gcm_driven_scm/observation_map.jl @@ -0,0 +1,79 @@ +import EnsembleKalmanProcesses: TOMLInterface +import ClimaCalibrate: observation_map, ExperimentConfig +using ClimaAnalysis +using JLD2 + +config_dict = YAML.load_file("experiment_config.yml") + +function observation_map(iteration) + + G_ensemble = Array{Float64}( + undef, + config_dict["dims"]..., + config_dict["ensemble_size"], + ) + + f = JLD2.jldopen( + joinpath(config_dict["output_dir"], "norm_vec_obs.jld2"), + "r+", + ) + norm_vec_obs = f["norm_vec_obs"][:] + + for m in 1:config_dict["ensemble_size"] + member_path = TOMLInterface.path_to_ensemble_member( + config_dict["output_dir"], + iteration, + m, + ) + simdir = SimDir(joinpath(member_path, "output_active")) + try + G_ensemble[:, m] .= process_member_data( + simdir; + y_names = config_dict["y_var_names"], + t_start = config_dict["g_t_start_sec"], + t_end = config_dict["g_t_end_sec"], + norm_vec_obs = norm_vec_obs, + ) + catch err + @info "Error during observation map for ensemble member $m" err + G_ensemble[:, m] .= NaN + end + end + return G_ensemble +end + +function process_member_data( + simdir; + y_names, + reduction = "inst", + t_start, + t_end, + norm_vec_obs = [1.0], + normalize = true, +) + + g = Float64[] + + for (i, y_name) in enumerate(y_names) + var_i = get(simdir; short_name = y_name, reduction = reduction) + sim_t_end = var_i.dims["time"][end] + + if sim_t_end < 0.95 * t_end + throw(ErrorException("Simulation failed.")) + end + # take time-mean + var_i_ave = average_time( + window(var_i, "time", left = t_start, right = sim_t_end), + ) + + y_var_i = slice(var_i_ave, x = 1, y = 1).data + if normalize + y_μ, y_σ = norm_vec_obs[i, 1], norm_vec_obs[i, 2] + y_var_i = (y_var_i .- y_μ) ./ y_σ + end + + append!(g, y_var_i) + end + + return g +end diff --git a/calibration/experiments/gcm_driven_scm/prior.toml b/calibration/experiments/gcm_driven_scm/prior.toml new file mode 100644 index 00000000000..78fb24058ea --- /dev/null +++ b/calibration/experiments/gcm_driven_scm/prior.toml @@ -0,0 +1,19 @@ +[entr_coeff] +prior = "constrained_gaussian(entr_coeff, 0.3, 0.03, 0, Inf)" +type = "float" + +[entr_inv_tau] +prior = "constrained_gaussian(entr_inv_tau, 0.0001, 0.00001, 0, Inf)" +type = "float" + +[detr_massflux_vertdiv_coeff] +prior = "constrained_gaussian(detr_massflux_vertdiv_coeff, 1.15, 0.1, 0, Inf)" +type = "float" + +[mixing_length_eddy_viscosity_coefficient] +prior = "constrained_gaussian(mixing_length_eddy_viscosity_coefficient, 0.14, 0.01, 0, Inf)" +type = "float" + +[mixing_length_diss_coeff] +prior = "constrained_gaussian(mixing_length_diss_coeff, 0.22, 0.01, 0, Inf)" +type = "float" \ No newline at end of file diff --git a/calibration/experiments/gcm_driven_scm/run_calibration.jl b/calibration/experiments/gcm_driven_scm/run_calibration.jl new file mode 100644 index 00000000000..edda5d12ad2 --- /dev/null +++ b/calibration/experiments/gcm_driven_scm/run_calibration.jl @@ -0,0 +1,35 @@ +import ClimaCalibrate: calibrate, CaltechHPC, get_prior, kwargs +import ClimaAtmos as CA +import YAML +import JLD2 + +# helper functions for processing netcdf data +include("helper_funcs.jl") + +# config_dict = YAML.load_file("../../../config/model_configs/prognostic_edmfx_gcmdriven_column.yml") +config_dict = YAML.load_file("model_config.yml") + +# TODO: Run simulation for 1 timestep to get z grid for LES interpolation +# config = CA.AtmosConfig(config_dict) +# include("../examples/hybrid/driver.jl") + +# generate obs_mean.jl and obs_noise_cov.jl +# temperature_mean, thetali_mean +zc_model = collect(25.0:50.0:2975.0) +y_obs, Σ_obs, norm_vec_obs = get_obs(config_dict["external_forcing_file"], ["qt_mean"], ["qt_mean"], zc_model; normalize = false) + + +# jldsave("obs_mean.jl"; y_obs) +# jldsave("obs_mean_cov.jl"; ) + +JLD2.save_object("obs_mean.jld2", y_obs) +JLD2.save_object("obs_noise_cov.jld2", Σ_obs) +# plot(y_obs) +# heatmap(Σ_obs) + +experiment_dir = dirname(Base.active_project()) +include(joinpath(experiment_dir, "observation_map.jl")) +slurm_kwargs = kwargs(time = 90, mem="16G") +eki = calibrate(CaltechHPC, experiment_dir; slurm_kwargs, verbose=true) + + diff --git a/calibration/experiments/gcm_driven_scm/scm_tomls/prognostic_edmfx_bomex.toml b/calibration/experiments/gcm_driven_scm/scm_tomls/prognostic_edmfx_bomex.toml new file mode 100644 index 00000000000..c40a2d16ab6 --- /dev/null +++ b/calibration/experiments/gcm_driven_scm/scm_tomls/prognostic_edmfx_bomex.toml @@ -0,0 +1,29 @@ +[EDMF_surface_area] +value = 0.1 + +[EDMF_min_area] +value = 1.0e-5 + +[EDMF_max_area] +value = 0.7 + +[min_area_limiter_scale] +value = 0 + +[detr_inv_tau] +value = 0 + +[detr_coeff] +value = 0 + +[detr_buoy_coeff] +value = 0 + +[detr_vertdiv_coeff] +value = 0 + +[max_area_limiter_scale] +value = 0 + +[pressure_normalmode_drag_coeff] +value = 40.0 diff --git a/calibration/model_interface.jl b/calibration/model_interface.jl new file mode 100644 index 00000000000..92fde0c3173 --- /dev/null +++ b/calibration/model_interface.jl @@ -0,0 +1,55 @@ +import ClimaAtmos as CA +import YAML +import ClimaCalibrate: + set_up_forward_model, run_forward_model, path_to_ensemble_member + +""" + set_up_forward_model(member, iteration, experiment_dir::AbstractString) + +Returns an AtmosConfig object for the given member and iteration. +If given an experiment id string, it will load the config from the corresponding YAML file. +Turns off default diagnostics and sets the TOML parameter file to the member's path. +This assumes that the config dictionary has `output_dir` and `restart_file` keys. +""" +function set_up_forward_model( + member, + iteration, + experiment_dir::AbstractString, +) + config_dict = YAML.load_file(joinpath(experiment_dir, "model_config.yml")) + if haskey(config_dict, "restart_file") + config_dict["restart_file"] = + joinpath(experiment_dir, config_dict["restart_file"]) + end + output_dir = config_dict["output_dir"] + member_path = path_to_ensemble_member(output_dir, iteration, member) + config_dict["output_dir"] = member_path + parameter_path = joinpath(member_path, "parameters.toml") + if haskey(config_dict, "toml") + push!(config_dict["toml"], parameter_path) + else + config_dict["toml"] = [parameter_path] + end + + # Turn off default diagnostics + config_dict["output_default_diagnostics"] = false + + return CA.AtmosConfig(config_dict) +end + +""" + run_forward_model(atmos_config::CA.AtmosConfig) + +Runs the atmosphere model with the given an AtmosConfig object. +Currently only has basic error handling. +""" +function run_forward_model(atmos_config::CA.AtmosConfig) + simulation = CA.get_simulation(atmos_config) + sol_res = CA.solve_atmos!(simulation) + if sol_res.ret_code == :simulation_crashed + !isnothing(sol_res.sol) && sol_res.sol .= eltype(sol_res.sol)(NaN) + error( + "The ClimaAtmos simulation has crashed. See the stack trace for details.", + ) + end +end \ No newline at end of file