Impact of Instantaneous Parameter Sensitivity on Ensemble-Based Parameter Estimation: Simulation With an Intermediate Coupled Model

Abstract

On ensemble-based coupled data assimilation, cross-component parameter estimation (CPE), has not been as extensively developed and applied as weakly coupled state and parameter estimation along with cross-component state estimation. This discrepancy is partially attributed to the lack of emphasis on the instantaneous response of coupled model states with respect to parameters across different components. We define so-called response as the instantaneous parameter sensitivity (IPS). Under the framework of sequential assimilation, the prior information heavily relies on the IPS of coupled states with different time scales. Based on the IPS analysis for an intermediate coupled model, a series of twin experiments of state and parameter estimation are conducted, in which an IPS-inspired adaptive inflation scheme for parameter ensemble is introduced. Results show that the success of a parameter estimation strategy is closely tied to the significant IPS of the observed state to the parameter targeted for optimization, as it maintains a high signal-to-noise ratio in the error covariance between parameter and prior state, thereby enhancing parameter estimation. An interesting finding in the context of IPS-based CPE is: an atmospheric parameter can be successfully estimated by assimilating observations from slow-varying oceanic component, but not vice versa. In comparison with cross-component state estimation, successful CPE significantly enhances the estimation accuracy of coupled states by mitigating model bias.