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 mergecoarse-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. TheEnKF estimates this error covariance through an ensemble of model simulations with perturbed forcingdata. Consequently, a correct specification of perturbation magnitude is essential for the EnKF to workoptimally. To this end, an adaptive EnKF (AEnKF), a variant of the EnKF with an additional componentthat 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 bedifferent than for other hydrologic applications. Therefore, instead of spatially or temporally averaging theinternal diagnostic (normalized innovations) to detect the misspecifications, spatiotemporal averaging wasused. First, sensitivity of the estimation accuracy to the degree of error in forcing perturbations wasinvestigated. Second, efficiency of the AEnKF for GRACE assimilation was explored using two syntheticand one real data experiment. Results show that there is considerable benefit in using this method toestimate the forcing error magnitude and that the AEnKF can efficiently estimate this magnitude. Show more