On thinning methods for data assimilation of satellite observations

Abstract

Thinning of observational data sets is an essential task in assimilation of satellite data for numerical weather forecast. In this work we modify and improve the scheme of so-called estimation error analysis (EEA). EEA is an adaptive thinning method that iteratively removes observations from a given data set, guided by a special approximation error measure evaluated at all original observation points. We propose EEA variants that differ in methodological and performance aspects, such as the Grid-EEA method, where errors are evaluated on a regular grid on the globe. Moreover, in the Top-Down EEA, we propose to construct the thinnings by an iterative point insertion strategy, which leads to an improved performance since the number of insertion steps is typically much smaller than the number of corresponding removal operations in EEA. We also provide an efficient implementation of the proposed methods yielding a significant acceleration of the standard EEA approach.