Impact of observation error structure on satellite soil moisture assimilation into a rainfall-runoff model

Abstract

In the Ensemble Kalman Filter (EnKF) - based data assimilation, the background prediction of a model is updated using observations and relative weights based on the model prediction and observation uncertainties. In practice, both model and observation uncertainties are difficult to quantify thus have been often assumed to be spatially and temporally independent Gaussian random variables. Nevertheless, it has been shown that incorrect assumptions regarding the structure of these errors can degrade the performance of the stochastic data assimilation. This work investigates the autocorrelation structure of the microwave satellite soil moisture retrievals and explores how assumed observation er..