Non-Gaussian ensemble filtering and adaptive inflation for soil moisture data assimilation

Dibia, E. C., Reichle, R. H., Anderson, J. L., Liang, X.. (2023). Non-Gaussian ensemble filtering and adaptive inflation for soil moisture data assimilation. Journal of Hydrometeorology, doi:10.1175/JHM-D-22-0046.1

Title Non-Gaussian ensemble filtering and adaptive inflation for soil moisture data assimilation
Author(s) Emmanuel C. Dibia, Rolf H. Reichle, Jeffrey L. Anderson, Xin-Zhong Liang
Abstract The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture esti-mation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.
Publication Title Journal of Hydrometeorology
Publication Date Jun 1, 2023
Publisher's Version of Record https://dx.doi.org/10.1175/JHM-D-22-0046.1
OpenSky Citable URL https://n2t.net/ark:/85065/d7df6w7h
OpenSky Listing View on OpenSky
CISL Affiliations TDD, DARES

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