Estimation of Surface Heat Fluxes via a Novel Variational Data Assimilation System

Surface heat and moisture fluxes play a vital role in the coupled land-atmosphere interaction. In contrast to the previous variational data assimilation (VDA) approaches that assimilated land surface temperature (LST) into an off-line land surface model, the new VDA system assimilates not only LST but also air temperature and specific humidity into a "coupled" land surface-atmospheric boundary layer model. Hence, it can take into account the interaction between the land surface and overlying atmosphere and generate more accurate turbulent heat fluxes. The main unknown parameters of the proposed VDA are bulk heat transfer coefficient (CHN) and evaporative fraction (EF). CHN scales the sum of turbulent heat fluxes and EF represents their partitioning. The VDA approach finds optimal values of CHN and EF by minimizing a cost function, which consists of the LST, air temperature, and specific humidity misfit terms and deviations of unknown parameters (CHN and EF) from their prior values. The model is tested over the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site. The results show that it can accurately estimate surface heat fluxes.Surface heat and moisture fluxes play a vital role in the coupled land-atmosphere interaction. In contrast to the previous variational data assimilation (VDA) approaches that assimilated land surface temperature (LST) into an off-line land surface model, the new VDA system assimilates not only LST but also air temperature and specific humidity into a "coupled" land surface-atmospheric boundary layer model. Hence, it can take into account the interaction between the land surface and overlying atmosphere and generate more accurate turbulent heat fluxes. The main unknown parameters of the proposed VDA are bulk heat transfer coefficient (CHN) and evaporative fraction (EF). CHN scales the sum of turbulent heat fluxes and EF represents their partitioning. The VDA approach finds optimal values of CHN and EF by minimizing a cost function, which consists of the LST, air temperature, and specific humidity misfit terms and deviations of unknown parameters (CHN and EF) from their prior values. The model is tested over the First International Satellite Land Surface Climatology Project Field Experiment (FIFE) site. The results show that it can accurately estimate surface heat fluxes.