The relationship between land cover types, soil moisture, surface roughness, satellite vegetation indices and convective cloud development during the warm season (April-September) is investigated. Convective clouds are the visual indicator of land surface-atmosphere interaction. Data integration methods are used to combine conventional meteorological data (gridded map fields, rawinsonde ascents), land use-land cover digital maps and satellite (Geosynchronous Operational Environmental Satellite- GOES; Advanced Very High Resolution Radiometer- AVHRR) data to study these interactions in the United States Midwest. The results show that satellite derived vegetation indices are more sensitive to surface climate variables during the mid-growing season (June-August) than the entire warm season. The correspondence between the vegetation indices, soil moisture and precipitation improve significantly when time lags are imposed, which indicates that soil moisture values may be useful for the prediction of satellite inferred vegetation conditions up to one season in advance.

The results of the satellite cloud data analyses confirm the hypothesis that clouds form earlier and persist longer over drier (i.e., cropped) areas and later over moister (i.e., forest) areas under weak synoptic flow conditions. Land cover boundary zones between more homogenous areas of cropland and forest surface covers are favored areas for enhanced cloud development under moderate (<30 m/s) flow conditions in the mid-troposphere. The boundary zones tend to behave like regions of differential vertical circulations (also called Non-Classical Mesoscale Circulations-NCMCs), suggesting that land cover induced surface discontinuities influence the organization of free convective cloud masses during the mid-summer months in the Midwest.

These research findings underscore the importance of incorporating detailed landscape information in mesoscale weather forecast models for the prediction of convective cloud patterns and precipitation in the warm season. The accurate prediction of cloud cover is as crucial to understanding the energy balance of the earth surface as it is important for the realistic modeling of the land surface-atmosphere interactions at all time and space scales of atmospheric motion. The findings also have significance in improving our understanding of the feedback mechanisms (especially the interactions between heat sinks and cloud formation) associated with human modifications to the landscape and climate in mid-latitude locations.