2019
DOI: 10.1029/2018wr024670
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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,… Show more

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Cited by 19 publications
(8 citation statements)
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“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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.…”
Section: Discussionmentioning
confidence: 99%
“…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).…”
Section: Discussionmentioning
confidence: 99%
“…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).…”
Section: Introductionmentioning
confidence: 99%