How-to guide and cookbook

How do I reconstruct the observations as OutputVars?

If you are using ObservationRecipe, you can reconstruct an EKP.Observation with ObservationRecipe.reconstruct_vars.

import ClimaCalibrate: ObservationRecipe
import ClimaAnalysis

# obs is a `EKP.Observation`
# Return a vector of OutputVar
vars = ObservationRecipe.reconstruct_vars(obs)

# obs_series is a `EKP.ObservationSeries`
# Return a vector of OutputVars reconstructed from the first observation in the
# series
vars =
    obs_series |>
    EKP.get_observations |>
    first |>
    ObservationRecipe.reconstruct_vars

# ekp is an `EKP.EnsembleKalmanProcess` object
# Same result as the example above
vars =
    ekp |>
    EKP.get_observation_series |>
    EKP.get_observations |>
    first |>
    ObservationRecipe.reconstruct_vars

How do I reconstruct the G ensemble matrix as OutputVars?

If you are using ObservationRecipe, you can reconstruct the G ensemble matrix as OutputVars with ObservationRecipe.reconstruct_g, ObservationRecipe.reconstruct_g_mean, and ObservationRecipe.reconstruct_g_mean_final.

import ClimaCalibrate: ObservationRecipe
import ClimaAnalysis

# ekp is an `EKP.EnsembleKalmanProcess` object

# Return a matrix of `OutputVar`s reconstructed from the G ensemble matrix
# at the 2nd iteration
G_ens_mat_as_vars = ObservationRecipe.reconstruct_g(ekp, 2)

# Return a vector of `OutputVar` reconstructed from the mean forward map
# evaluation at the 3rd iteration
g_mean_as_vars = ObservationRecipe.reconstruct_g_mean(ekp, 3)

# Return a vector of `OutputVar` reconstructed from the final mean forward map
# evaluation at the final iteration
ObservationRecipe.reconstruct_g_mean_final(ekp)

I have a diagonal covariance matrix. How can I reconstruct this as a OutputVar?

If you created the diagonal covariance matrix with ObservationRecipe, you can reconstruct with ObservationRecipe.reconstruct_diag_cov where obs is a EKP.Observation.

import ClimaCalibrate: ObservationRecipe
import ClimaAnalysis

ObservationRecipe.reconstruct_diag_cov(obs)