Variational Assimilation of Radiometric Surface Temperature and Reference-Level Micrometeorology into a Model of the Atmospheric Boundary Layer and Land Surface

Abstract

Data assimilation provides a useful framework that allows us to combine measurements and models, by appropriately weighting the sources of error in both, to produce a statistically optimal and dynamically consistent estimate of the evolving state of the system. In this paper a variational approach is used to estimate regional land and atmospheric boundary layer states and fluxes via the assimilation of standard reference-level temperature and humidity and radiometric surface temperature measurements into a coupled land surface?atmospheric boundary layer model. Results from an application to a field experiment site show that using both surface temperature and reference-level micrometeorology measurements allows for the accurate and robust estimation of land surface fluxes even during nonideal conditions, where the evaporation rate is atmospherically controlled and processes that are not parameterized in the model (i.e., advection) are important. The assimilation scheme is able to provide estimates of model errors, which has implications for being able to diagnose structural model errors that may be present due to missing process representation and/or poor or biased parameterizations. Because robust estimates are not always obtainable with either measurement type in isolation, these results illustrate possible synergism that may exist when using multiple observation types.