Analysis of a two-year meteorological dataset produced on Italian territory with a coupling procedure between a limited area atmospheric model and a sequential MSG-SEVIRI LST assimilation scheme

The sensible and latent heat exchanges at the interface between soil and atmosphere are fundamental components of the surface energy budget and constitute the lower boundary condition for atmospheric numerical models. These models often represent such phenomena in very simplified ways or employ a detailed description of these dynamics that require complex parameterization, difficult or impossible to retrieve with sufficient accuracy. The characterization of such variables on large areas is possible only through remote-sensing techniques by monitoring indirectly related variables, with particular reference to the land surface temperature (LST). In this work, the Regional Atmospheric Modeling System (RAMS), a limited area atmospheric model, was coupled with the 1-D LST variational assimilation scheme Assimilation Code for Hydrologic-Atmospheric Budget (ACHAB), which is able to provide optimal estimates of the surface energy budget. The source of the LST maps for this work was Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) imagery. A 2 year-long meteorological dataset (2005-2006) was produced on the Italian territory using this coupling framework. The purpose of the experiment was to evaluate how much the assimilation of satellite-derived LST in a simple surface energy budget model is able to affect the dynamics of the troposphere on the study area, considering both summer and winter. Evaluations of the results of the coupling framework were carried out by comparing with both observations of the ground sensors network and the atmospheric soundings available in the study period. The analysis showed significant improvements in terms of reconstructions of the surface and atmospheric states during summer, while a general worsening of the performances of the atmospheric model was observed in winter months.