SmootherExps

The SmootherExps.jl sub-module contains methods to configure filter twin experiments, using a stored time series as generated by GenerateTimeSeries as the underlying observation generating process. The frequency of observations in continuous time is defined by the frequency of data saved in the time series and is inferred by the experiment when reading in the data.

Observation analysis forecast cycle over multiple data assimilation windows

Smoother experiment configurations are generated by supplying a NamedTuple with the required fields as specified in the experiment method. Conventions for these arguments are the same as with the FilterExps, with the additional options that configure the data assimilation window (DAW) and how this is shifted in time:

lag - the number of past observation / analysis times to reanalyze in a DAW, corresponding to $L$ in the figure above;
shift- the number of observation / analysis times to shift the DAW, corresponding to $S$ in the figure above;
mda - (Multiple Data Assimilation), type Bool, determines whether the technique of multiple data assimilation is used (only compatible with single_iteration and iterative smoothers.

Currently debugged and validated smoother experiment configurations include

classic_state - classic ETKS style state estimation
classic_param - classic ETKS style state-parameter estimation
single_iteration_state - SIEnKS state estimation
single_iteration_param - SIEnKS state-parameter estimation
iterative_state - IEnKS Gauss-Newton style state estimation
iterative_param - IEnKS Gauss-Newton style state-parameter estimation

Note, the single-iteration and fully iterative Gauss-Newton style smoothers are only defined for MDA compatible values of lag and shift where the lag is an integer multiple of the shift.

Smoother Experiment Methods

DataAssimilationBenchmarks.SmootherExps.classic_ensemble_param — Method

classic_ensemble_param((time_series::String, method::String, seed::Int64, nanl::Int64,
                        lag::Int64, shift::Int64, obs_un::Float64, obs_dim::Int64,
                        γ::Float64, p_err::Float64, p_wlk::Float64, N_ens::Int64,
                        s_infl::Float64, s_infl::Float64})::NamedTuple)

Classic ensemble Kalman smoother joint state-parameter estimation twin experiment.

NOTE: the classic scheme does not use multiple data assimilation and we hard code mda=false in the function for consistency with other methods.

Output from the experiment is saved in a dictionary of the form,

data = Dict{String,Any}(
                        "fore_rmse" => fore_rmse,
                        "filt_rmse" => filt_rmse,
                        "post_rmse" => post_rmse,
                        "param_rmse" => para_rmse,
                        "fore_spread" => fore_spread,
                        "filt_spread" => filt_spread,
                        "post_spread" => post_spread,
                        "param_spread" => para_spread,
                        "method" => method,
                        "seed" => seed,
                        "diffusion" => diffusion,
                        "dx_params" => dx_params,
                        "param_truth" => param_truth,
                        "sys_dim" => sys_dim,
                        "state_dim" => state_dim,
                        "obs_dim" => obs_dim,
                        "obs_un" => obs_un,
                        "γ" => γ,
                        "p_err" => p_err,
                        "p_wlk" => p_wlk,
                        "nanl" => nanl,
                        "tanl" => tanl,
                        "lag" => lag,
                        "shift" => shift,
                        "mda" => mda,
                        "h" => h,
                        "N_ens" => N_ens,
                        "s_infl" => round(s_infl, digits=2),
                        "p_infl"  => round(p_infl, digits=2)
                       )

if haskey(ts, "diff_mat")
    data["diff_mat"] = ts["diff_mat"]
end

Experiment output is written to a directory defined by

path = pkgdir(DataAssimilationBenchmarks) * "/src/data/" * method * "-classic/"

where the file name is written dynamically according to the selected parameters as follows:

method * "-classic_" * model *
         "_state_seed_" * lpad(seed, 4, "0") *
         "_diff_" * rpad(diffusion, 5, "0") *
         "_sysD_" * lpad(sys_dim, 2, "0") *
         "_obsD_" * lpad(obs_dim, 2, "0") *
         "_obsU_" * rpad(obs_un, 4, "0") *
         "_gamma_" * lpad(γ, 5, "0") *
         "_nanl_" * lpad(nanl, 5, "0") *
         "_tanl_" * rpad(tanl, 4, "0") *
         "_h_" * rpad(h, 4, "0") *
         "_lag_" * lpad(lag, 3, "0") *
         "_shift_" * lpad(shift, 3, "0") *
         "_mda_" * string(mda) *
         "_nens_" * lpad(N_ens, 3,"0") *
         "_stateInfl_" * rpad(round(s_infl, digits=2), 4, "0") *
         ".jld2"

source

DataAssimilationBenchmarks.SmootherExps.classic_ensemble_state — Method

classic_ensemble_state((time_series::String, method::String, seed::Int64, nanl::Int64,
                        lag::Int64, shift::Int64, obs_un::Float64, obs_dim::Int64,
                        γ::Float64, N_ens::Int64, s_infl::Float64)::NamedTuple)

Classic ensemble Kalman smoother state estimation twin experiment.

NOTE: the classic scheme does not use multiple data assimilation and we hard code mda=false in the function for consistency with the API of other methods.

Output from the experiment is saved in a dictionary of the form,

data = Dict{String,Any}(
                        "fore_rmse" => fore_rmse,
                        "filt_rmse" => filt_rmse,
                        "post_rmse" => post_rmse,
                        "fore_spread" => fore_spread,
                        "filt_spread" => filt_spread,
                        "post_spread" => post_spread,
                        "method" => method,
                        "seed"  => seed,
                        "diffusion" => diffusion,
                        "dx_params" => dx_params,
                        "sys_dim" => sys_dim,
                        "obs_dim" => obs_dim,
                        "obs_un" => obs_un,
                        "γ" => γ,
                        "nanl" => nanl,
                        "tanl" => tanl,
                        "lag" => lag,
                        "shift" => shift,
                        "h" => h,
                        "N_ens" => N_ens,
                        "mda"  => mda,
                        "s_infl" => round(s_infl, digits=2)
                       )

if haskey(ts, "diff_mat")
    data["diff_mat"] = ts["diff_mat"]
end

Experiment output is written to a directory defined by

path = pkgdir(DataAssimilationBenchmarks) * "/src/data/" * method * "-classic/"

where the file name is written dynamically according to the selected parameters as follows:

method * "-classic_" * model *
         "_state_seed_" * lpad(seed, 4, "0") *
         "_diff_" * rpad(diffusion, 5, "0") *
         "_sysD_" * lpad(sys_dim, 2, "0") *
         "_obsD_" * lpad(obs_dim, 2, "0") *
         "_obsU_" * rpad(obs_un, 4, "0") *
         "_gamma_" * lpad(γ, 5, "0") *
         "_nanl_" * lpad(nanl, 5, "0") *
         "_tanl_" * rpad(tanl, 4, "0") *
         "_h_" * rpad(h, 4, "0") *
         "_lag_" * lpad(lag, 3, "0") *
         "_shift_" * lpad(shift, 3, "0") *
         "_mda_" * string(mda) *
         "_nens_" * lpad(N_ens, 3,"0") *
         "_stateInfl_" * rpad(round(s_infl, digits=2), 4, "0") *
         ".jld2"

source

DataAssimilationBenchmarks.SmootherExps.iterative_ensemble_param — Method

iterative_ensemble_param((time_series:String, method:String, seed::Int64, nanl::Int64,
                          lag::Int64, shift::Int64, mda::Bool, obs_un::Float64,
                          obs_dim::Int64, γ::Float64, p_err::Float64, p_wlk::Float64,
                          N_ens::Int64, s_infl::Float64, p_infl::Float64)::NamedTuple)

4DEnVAR joint state-parameter estimation twin experiment using the IEnKS formalism.

Output from the experiment is saved in a dictionary of the form,

data = Dict{String,Any}(
                        "fore_rmse" => fore_rmse,
                        "filt_rmse" => filt_rmse,
                        "post_rmse" => post_rmse,
                        "param_rmse" => para_rmse,
                        "fore_spread" => fore_spread,
                        "filt_spread" => filt_spread,
                        "post_spread" => post_spread,
                        "param_spread" => para_spread,
                        "iteration_sequence" => iteration_sequence,
                        "method" => method,
                        "seed" => seed,
                        "diffusion" => diffusion,
                        "dx_params" => dx_params,
                        "sys_dim" => sys_dim,
                        "obs_dim" => obs_dim,
                        "obs_un" => obs_un,
                        "γ" => γ,
                        "p_wlk" => p_wlk,
                        "p_infl" => p_infl,
                        "nanl" => nanl,
                        "tanl" => tanl,
                        "lag" => lag,
                        "shift" => shift,
                        "mda" => mda,
                        "h" => h,
                        "N_ens" => N_ens,
                        "s_infl" => round(s_infl, digits=2),
                        "p_infl" => round(p_infl, digits=2)
                       )

if haskey(ts, "diff_mat")
    data["diff_mat"] = ts["diff_mat"]
end

Experiment output is written to a directory defined by

path = pkgdir(DataAssimilationBenchmarks) * "/src/data/" * method * "/"

where the file name is written dynamically according to the selected parameters as follows:

method * "_" * model *
         "_param_seed_" * lpad(seed, 4, "0") *
         "_diff_" * rpad(diffusion, 5, "0") *
         "_sysD_" * lpad(sys_dim, 2, "0") *
         "_obsD_" * lpad(obs_dim, 2, "0") *
         "_obsU_" * rpad(obs_un, 4, "0") *
         "_gamma_" * lpad(γ, 5, "0") *
         "_paramE_" * rpad(p_err, 4, "0") *
         "_paramW_" * rpad(p_wlk, 6, "0") *
         "_nanl_" * lpad(nanl, 5, "0") *
         "_tanl_" * rpad(tanl, 4, "0") *
         "_h_" * rpad(h, 4, "0") *
         "_lag_" * lpad(lag, 3, "0") *
         "_shift_" * lpad(shift, 3, "0") *
         "_mda_" * string(mda) *
         "_nens_" * lpad(N_ens, 3,"0") *
         "_stateInfl_" * rpad(round(s_infl, digits=2), 4, "0") *
         "_paramInfl_" * rpad(round(p_infl, digits=2), 4, "0") *
         ".jld2"

source

DataAssimilationBenchmarks.SmootherExps.iterative_ensemble_state — Method

iterative_ensemble_state((time_series::String, method::String, seed::Int64, nanl::Int64,
                          lag::Int64, shift::Int64, mda::Bool, obs_un::Float64,
                          obs_dim::Int64, γ::Float64, N_ens::Int64,
                          s_infl::Float64)::NamedTuple)

4DEnVAR state estimation twin experiment using the IEnKS formalism.

Output from the experiment is saved in a dictionary of the form,

data = Dict{String,Any}(
                        "fore_rmse" => fore_rmse,
                        "filt_rmse" => filt_rmse,
                        "post_rmse" => post_rmse,
                        "fore_spread" => fore_spread,
                        "filt_spread" => filt_spread,
                        "post_spread" => post_spread,
                        "iteration_sequence" => iteration_sequence,
                        "method" => method,
                        "seed" => seed,
                        "diffusion" => diffusion,
                        "dx_params" => dx_params,
                        "sys_dim" => sys_dim,
                        "obs_dim" => obs_dim,
                        "obs_un" => obs_un,
                        "γ" => γ,
                        "nanl" => nanl,
                        "tanl" => tanl,
                        "lag" => lag,
                        "shift" => shift,
                        "mda" => mda,
                        "h" => h,
                        "N_ens" => N_ens,
                        "s_infl" => round(s_infl, digits=2)
                       )

if haskey(ts, "diff_mat")
    data["diff_mat"] = ts["diff_mat"]
end

Experiment output is written to a directory defined by

path = pkgdir(DataAssimilationBenchmarks) * "/src/data/" * method * "/"

where the file name is written dynamically according to the selected parameters as follows:

method * "_" * model *
         "_state_seed_" * lpad(seed, 4, "0") *
         "_diff_" * rpad(diffusion, 5, "0") *
         "_sysD_" * lpad(sys_dim, 2, "0") *
         "_obsD_" * lpad(obs_dim, 2, "0") *
         "_obsU_" * rpad(obs_un, 4, "0") *
         "_gamma_" * lpad(γ, 5, "0") *
         "_nanl_" * lpad(nanl, 5, "0") *
         "_tanl_" * rpad(tanl, 4, "0") *
         "_h_" * rpad(h, 4, "0") *
         "_lag_" * lpad(lag, 3, "0") *
         "_shift_" * lpad(shift, 3, "0") *
         "_mda_" * string(mda) *
         "_nens_" * lpad(N_ens, 3,"0") *
         "_stateInfl_" * rpad(round(s_infl, digits=2), 4, "0") *
         ".jld2"

source

DataAssimilationBenchmarks.SmootherExps.single_iteration_ensemble_param — Method

single_iteration_ensemble_param((time_series::String, method::String, seed:Int64,
                                 nanl::Int64, lag::Int64, shift::Int64, mda::Bool,
                                 obs_un::Float64, obs_dim::Int64, γ::Float64,
                                 p_err::Float64, p_wlk::Float64, N_ens::Int64,
                                 s_infl::Float64, p_infl::Float64)::NamedTuple)

SIEnKS joint state-parameter estimation twin experiment.

Output from the experiment is saved in a dictionary of the form,

data = Dict{String,Any}(
                        "fore_rmse" => fore_rmse,
                        "filt_rmse" => filt_rmse,
                        "post_rmse" => post_rmse,
                        "param_rmse" => para_rmse,
                        "fore_spread" => fore_spread,
                        "filt_spread" => filt_spread,
                        "post_spread" => post_spread,
                        "param_spread" => para_spread,
                        "method" => method,
                        "seed" => seed,
                        "diffusion" => diffusion,
                        "dx_params" => dx_params,
                        "param_truth" => param_truth,
                        "sys_dim" => sys_dim,
                        "obs_dim" => obs_dim,
                        "obs_un" => obs_un,
                        "γ" => γ,
                        "p_wlk" => p_wlk,
                        "p_infl" => p_infl,
                        "nanl" => nanl,
                        "tanl" => tanl,
                        "lag" => lag,
                        "shift" => shift,
                        "mda" => mda,
                        "h" => h,
                        "N_ens" => N_ens,
                        "s_infl" => round(s_infl, digits=2),
                        "p_infl" => round(p_infl, digits=2)
                       )

if haskey(ts, "diff_mat")
    data["diff_mat"] = ts["diff_mat"]
end

Experiment output is written to a directory defined by

path = pkgdir(DataAssimilationBenchmarks) * "/src/data/" * method * "-single-iteration/"

where the file name is written dynamically according to the selected parameters as follows:

method * "-single-iteration_" * model *
         "_param_seed_" * lpad(seed, 4, "0") *
         "_diff_" * rpad(diffusion, 5, "0") *
         "_sysD_" * lpad(sys_dim, 2, "0") *
         "_obsD_" * lpad(obs_dim, 2, "0") *
         "_obsU_" * rpad(obs_un, 4, "0") *
         "_gamma_" * lpad(γ, 5, "0") *
         "_paramE_" * rpad(p_err, 4, "0") *
         "_paramW_" * rpad(p_wlk, 6, "0") *
         "_nanl_" * lpad(nanl, 5, "0") *
         "_tanl_" * rpad(tanl, 4, "0") *
         "_h_" * rpad(h, 4, "0") *
         "_lag_" * lpad(lag, 3, "0") *
         "_shift_" * lpad(shift, 3, "0") *
         "_mda_" * string(mda) *
         "_nens_" * lpad(N_ens, 3,"0") *
         "_stateInfl_" * rpad(round(s_infl, digits=2), 4, "0") *
         "_paramInfl_" * rpad(round(p_infl, digits=2), 4, "0") *
         ".jld2"

source

DataAssimilationBenchmarks.SmootherExps.single_iteration_ensemble_state — Method

single_iteration_ensemble_state((time_series::String, method::String, seed::Int64,
                                 nanl::Int64, lag::Int64, shift::Int64, mda::Bool,
                                 obs_un::Float64, obs_dim::Int64, γ::Float64,
                                 N_ens::Int64, s_infl::Float64})::NamedTuple)

SIEnKS state estimation twin experiment.

Output from the experiment is saved in a dictionary of the form,

data = Dict{String,Any}(
                        "fore_rmse" => fore_rmse,
                        "filt_rmse" => filt_rmse,
                        "post_rmse" => post_rmse,
                        "fore_spread" => fore_spread,
                        "filt_spread" => filt_spread,
                        "post_spread" => post_spread,
                        "method" => method,
                        "seed" => seed,
                        "diffusion" => diffusion,
                        "dx_params" => dx_params,
                        "sys_dim" => sys_dim,
                        "obs_dim" => obs_dim,
                        "obs_un" => obs_un,
                        "γ" => γ,
                        "nanl" => nanl,
                        "tanl" => tanl,
                        "lag" => lag,
                        "shift" => shift,
                        "mda" => mda,
                        "h" => h,
                        "N_ens" => N_ens,
                        "s_infl" => round(s_infl, digits=2)
                       )

if haskey(ts, "diff_mat")
    data["diff_mat"] = ts["diff_mat"]
end

Experiment output is written to a directory defined by

path = pkgdir(DataAssimilationBenchmarks) * "/src/data/" * method * "-single-iteration/"

where the file name is written dynamically according to the selected parameters as follows:

method * "-single-iteration_" * model *
         "_state_seed_" * lpad(seed, 4, "0") *
         "_diff_" * rpad(diffusion, 5, "0") *
         "_sysD_" * lpad(sys_dim, 2, "0") *
         "_obsD_" * lpad(obs_dim, 2, "0") *
         "_obsU_" * rpad(obs_un, 4, "0") *
         "_gamma_" * lpad(γ, 5, "0") *
         "_nanl_" * lpad(nanl, 5, "0") *
         "_tanl_" * rpad(tanl, 4, "0") *
         "_h_" * rpad(h, 4, "0") *
         "_lag_" * lpad(lag, 3, "0") *
         "_shift_" * lpad(shift, 3, "0") *
         "_mda_" * string(mda) *
         "_nens_" * lpad(N_ens, 3,"0") *
         "_stateInfl_" * rpad(round(s_infl, digits=2), 4, "0") *
         ".jld2"

source