A Coupled Land Surface–Boundary Layer Model and Its Adjoint

Abstract

In this paper, a simple coupled land surface?boundary layer model and its adjoint are presented. The primary goal is to demonstrate the capabilities of the adjoint model as a general tool for sensitivity analysis and data assimilation. The adjoint method was chosen primarily for two reasons: 1) the adjoint model can be used not only to obtain parameter sensitivities with greater efficiency but, more important, to provide added insight into the sensitivities as compared with that obtained with traditional simulation techniques (e.g., pathways, time variations in sensitivity) and 2) the adjoint model can be used in a variational data assimilation framework to combine measurements and the model of the physical system optimally in order to estimate state variables and fluxes. Two simple examples are presented to illustrate how the framework can be used for performing both diagnostic sensitivity experiments and hydrologic data assimilation. In the sensitivity experiment, temporal patterns and total influence of model states and parameters on average daily ground temperature are shown. In the synthetic data assimilation example, the adjoint model is used as an estimation tool to initialize the coupled model through assimilation of ground temperature observations. As a result, great improvement was gained in simulation of model states and surface fluxes based only on a minimal set of basic land temperature measurements and the auxiliary parameters: incident solar radiation, large-scale wind speed, and free atmosphere profiles of temperature and humidity. Forthcoming studies will use the framework developed here to examine thoroughly the consequences of using uncoupled versus coupled models of the land and the atmospheric boundary layer (ABL). In assimilation mode, the coupled surface?ABL model and its adjoint will be used to estimate surface fluxes and micrometeorological conditions based on remote sensing measurements of land temperature and minimal auxiliary data.