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Accurate assignment of model and observation errors is crucial for the successful application of land surface data assimilation algorithms. Poorly-specified model and observation errors can significantly degrade assimilation results. In 2008, Reichle et al. developed an operational procedure to adaptively tune model and observation errors. In this paper, we modified and applied Reichle’s procedure in the Noah land surface model to assimilate observed surface soil moisture data. Numerical simulations showed that: (1) the best estimate of model and observation errors appears when the empirical factor β equals 1.02; (2) the Reichle procedure can be deployed to adaptively tune errors if their true values change slowly; and (3) convergence of the Reichle procedure was improved using better initial errors achieved by iterative computations.
Accurate assignment of model and observation errors is crucial for the successful application of land surface data assimilation algorithms. Poorly-specified model and observation errors can significantly degrade assimilation results. In 2008, Reichle et al. Developed an operational procedure to adaptively tune model and observation errors. In this paper, we modified and applied Reichle’s procedure in the Noah land surface model to assimilate observed surface soil moisture data. Numerical simulations showed that: (1) the best estimate of model and observation errors appears when the empirical factor β equals 1.02 ; (2) the Reichle procedure can be deployed to adaptively tune errors if their true values change slowly; and (3) convergence of the Reichle procedure was improved using better initial errors achieved by iterative computations.