Adjoint retrieval of prognostic land surface model variables for an NWP model: Assimilation of ground surface temperature

Abstract

Based on a 2-layer land surface model, a rather general variational data assimilation framework for estimatingmodel state variables is developed. The method minimizes the error of surface soil temperature predictionssubject to constraints imposed by the prediction model. Retrieval experiments for soil prognostic variables areperformed and the results verified against model simulated data as well as real observations for the OklahomaAtmospheric Surface layer Instrumentation System (OASIS). The optimization scheme is robust with respect toa wide range of initial guess errors in surface soil temperature (as large as 30 K) and deep soil moisture (withinthe range between wilting point and saturation). When assimilating OASIS data, the scheme can reduce theinitial guess error by more than 90%, while for Observing Simulation System Experiments (OSSEs), the initialguess error is usually reduced by over four orders of magnitude.Using synthetic data, the robustness of the retrieval scheme as related to information content of the data andthe physical meaning of the adjoint variables and their use in sensitivity studies are investigated. Throughsensitivity analysis, it is confirmed that the vegetation coverage and growth condition determine whether ornot the optimally estimated initial soil moisture condition leads to an optimal estimation of the surface fluxes.This reconciles two recent studies.With the real data experiments, it is shown that observations during the daytime period are the most effectivefor the retrieval. Longer assimilation windows result in more accurate initial condition retrieval, underlining theimportance of information quantity, especially for schemes assimilating noisy observations.