oemAveragingKernelCalc

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

Calculate the averaging kernel matrix.

This is done by describing the sensitivity of the oemCalc() retrieval with respect to the true state of the system. A prerequisite for the calculation of the averaging kernel matrix is a successful oemCalc() calculation in which the oem.measurement_jac and the gain matrix oem.measurement_gain_mat have been calculated.

The result is \(\mathbf{A}=\mathbf{G}\mathbf{J}\), using the gain and Jacobian in the retrieved coordinates; see Gain, averaging kernel, and retrieval uncertainty.

Author: Simon Pfreundschuh

Parameters:

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