This study investigates the extent to which assimilating high-resolution remotely sensed cloud cover into the Regional Atmospheric Modeling System (RAMS) provides an improved regional diagnosis of downward short- and long- wave surface radiation fluxes and precipitation. An automatic procedure was developed to derive high-resolution (4kmx4km) fields of fractional cloud cover from visible band (GOES series) geostationary satellite data using a novel tracking procedure to determine the clear-sky composite image. Initial studies, in which RAMS surface short-wave radiation fluxes were replaced by estimates obtained by applying satellite-derived cloud cover in the UMD GEWEX/SRB model, revealed problems associated with inconsistencies between the revised solar radiation fields and the RAMS-calculated incoming long-wave radiation and precipitation fields. Consequently, in this study, the relationship between cloud albedo, optical depth, and water/ice content used in the UMD GEWEX/SRB model was applied instead to provide estimates of whole-column cloud water/ice that were ingested into RAMS. This potentially enhances the realism of the modeled short- and long-wave radiation and precipitation. The ingested cloud image took the horizontal distribution of clouds from the satellite image but derives its vertical distribution from the fields simulated by RAMS in the time step immediately prior to assimilation. The resulting image was ingested every minute, with linear interpolation used to derive the 1-minute cloud images between 15-minute GOES samples. Comparisons were made between modeled and observed data taken from the AZMET weather station network in southern Arizona for model runs with and without cloud ingestion. Cloud ingestion was found to substantially improve the ability of RAMS model to capture temporal and spatial variations in surface fields associated with cloud cover. The sensitivity of these comparisons to model initiation was explored by making five ensemble runs starting from different initiation. In general, RAMS with cloud assimilation technique is not sensitive to realistic perturbation of initial conditions. An initial test suggests that cloud ingestion enhanced RAMS short-term forecast ability.

yucel@hwr.arizona.edu