| The relationships between land-surface conditions and the climate of warm season months for the humid lowlands of the Midwest United States are evaluated using conventional meteorological data, digital maps of land-surface characteristics, and satellite data. The methodology involves: compositing patterns of pressure-level data, surface climate variables, and cloud frequencies from GOES data according to barotropic-flow type; relating these patterns to the synoptic environment; and visually comparing these patterns to land-surface characteristics (e.g., LULC and soil order) at the synoptic scale to discern associations between the land surface and the patterns of climate variables and cloud frequency. The diurnal patterns of convective cloud development for three Midwest target areas are also related to land-surface characteristics to identify mesoscale land surface-convective cloud interactions during the warm season.; The results obtained from pressure-level data and first order weather station data for the warm seasons in the contemporary (1950–1999) period suggest that the atmospheric background conditions are the dominant control of the surface climate at synoptic scales. For the recent (1991–1999) period, a combination of GOES cloud data, atmospheric thermodynamic indices, and information on land-surface characteristics confirm that atmospheric background conditions dominantly influence the patterns of cloud formation at synoptic scales. However, a mesoscale association also exists: cumulus clouds initiate first and persist longer over dry (moist) surfaces within an initially dry (moist) atmosphere, across a range of mid-tropospheric wind conditions. There is an equivocal association between convective cloud development and other land-surface conditions, suggesting that surface moisture dominates the land surface-convective cloud interactions.; This dissertation reveals important and previously undisclosed information about land surface-atmosphere interactions at both synoptic and meso-scales. The research findings underscore the importance of incorporating detailed landscape information, particularly surface moisture, in mesoscale forecast models for the prediction of convective clouds. Moreover, numerical modeling under idealized conditions, or empirical research that considers only a limited range of synoptic-flow conditions, may not adequately portray land surface-atmosphere associations. Finally, these findings are significant in furthering our understanding of the feedback mechanisms associated with human modifications to the landscape and climate in mid-latitude, humid regions. |
