oemSmoothingErrorCalc

Workspace.oemSmoothingErrorCalc(self, oem: OptimalEstimationData = self.oem) → None

Calculates the covariance matrix describing the error due to smoothing.

The calculation of model_state_covmat_smoothing_error also requires the averaging kernel matrix oem.measurement_averaging_kernel to be computed after a successful oemCalc calculation.

The result is \(\mathbf{S}_{\rm smooth}=(\mathbf{I}-\mathbf{A})\mathbf{S}_a(\mathbf{I}-\mathbf{A})^{\top}\); see Gain, averaging kernel, and retrieval uncertainty for its relation to posterior covariance.

Author: Simon Pfreundschuh

Parameters:

oem (~pyarts3.arts.OptimalEstimationData, optional) – Numerical problem and results for oemCalc() and oemCalcReduced(). Defaults to self.oem. [INOUT]