On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation

Abstract

The ensemble Kalman filter (EnKF) has been proved as a useful algorithm to merge coarse‐resolution Gravity Recovery and Climate Experiment (GRACE) data with hydrologic model results. However, in order for the EnKF to perform optimally, a correct forecast error covariance is needed. The EnKF estimates this error covariance through an ensemble of model simulations with perturbed forcing data. Consequently, a correct specification of perturbation magnitude is essential for the EnKF to work optimally. To this end, an adaptive EnKF (AEnKF), a variant of the EnKF with an additional component that dynamically detects and corrects error misspecifications during the filtering process, has been applied. Due to the low spatial and temporal resolutions of GRACE data, the efficiency of this method could be different than for other hydrologic applications. Therefore, instead of spatially or temporally averaging the internal diagnostic (normalized innovations) to detect the misspecifications, spatiotemporal averaging was used. First, sensitivity of the estimation accuracy to the degree of error in forcing perturbations was investigated. Second, efficiency of the AEnKF for GRACE assimilation was explored using two synthetic and one real data experiment. Results show that there is considerable benefit in using this method to estimate the forcing error magnitude and that the AEnKF can efficiently estimate this magnitude.

Satellite soil moisture data assimilation for improved operational continental water balance prediction

“…In this study, we assimilated SMAP and SMOS data into an operational AWRA-L water balance modelling system through a simple sequential state updating approach, with weightings derived using triple collocation approach (DA-TC), followed by a post-adjustment for mass redistribution (DA-TCAIR). Previous data assimilation studies using the AWRA-L model opted for ensemble-based methods (Renzullo et al, 2014;Shokri et al, 2019;Tian et al, 2017Tian et al, , 2019a. Ensemble-based methods rely on a priori knowledge of uncertainty in forcing data and model error variances to derive spatially and temporally varying gain matrices at each time step.…”

Satellite soil moisture data assimilation for improved operational continental water balance prediction

“…In this study, we assimilated SMAP and SMOS data into an operational AWRA-L water balance modelling system through a simple sequential state updating approach, with weightings derived using triple collocation approach (DA-TC), followed by a post-adjustment for mass redistribution (DA-TCAIR). Previous data assimilation studies using the AWRA-L model opted for ensemble-based methods (Renzullo et al, 2014;Shokri et al, 2019;Tian et al, 2019a;Tian et al, 2017;Tian et al, 2019b).…”

Development and evaluation of 0.05° terrestrial water storage estimates using CABLE land surface model and GRACE data assimilation

“…A similar effort is also seen in hydrologic model development, such as the PCRaster Global Water Balance (PCR-GLOBWB; Sutanudjaja et al, 2018), which improves the spatial resolution from 30 arcmin (∼ 50 m or ∼ 0.5 • ) to 5 arcmin (∼ 9 km or 0.083 • ) and extends the time span to more than a 50-year period. The enhancement of model spa-tial resolution and time span receives even more attention at the local level, where spatial detail down to a few kilometers is needed (e.g., Rasmussen et al, 2014;Singh et al, 2015;Beamer et al, 2016;Dong et al, 2020).…”