Infrared brightness temperatures provide valuable information about atmospheric water vapor, cloud cover, and surface properties, and thus are very useful for a wide variety of research purposes. In this presentation, results will be shown for several recent studies that have used geosynchronous satellite observations to examine the accuracy of highresolution numerical model output, to improve the analysis and forecast accuracy of regional forecast models through use of an ensemble data assimilation system, and to monitor drought conditions across the contiguous U.S.

For the model validation study, the ability of four double-moment cloud microphysical parameterization schemes to accurately simulate cloud characteristics was evaluated through comparison of simulated and observed GOES infrared brightness temperatures. Large differences were found in the simulated cloud cover, especially in the upper troposphere. Overall, the microphysics schemes that predicted two moments for all five hydrometeor species (cloud water, rain, ice, snow, and graupel) produced too much upper level cloud cover, whereas simpler schemes did not contain enough high clouds. The large differences indicate that large uncertainties remain in how these schemes represent cloud processes.

In the second part of the talk, results will be shown from several regional-scale Observing System Simulation Experiments that were used to explore how the assimilation of clear and cloudy sky infrared brightness temperatures using an ensemble Kalman filter data assimilation system impacts the analysis and forecast accuracy. Overall, the results revealed that the assimilation of cloud-affected brightness temperatures had a large positive impact on the simulated cloud and moisture fields, with a less consistent impact on the wind and temperature analyses. Short-range precipitation forecasts for a highimpact weather event were improved when cloudy observations were assimilated.

In the last part of the talk, the ability of a new drought analysis metric based on thermal infrared remote sensing imagery to provide early warning of drought development is assessed. This new metric, called the Rapid Change Index (RCI), is designed to highlight areas undergoing rapid changes in moisture stress. The results indicate that compared to climatology there is often a much higher risk for drought development over sub-seasonal time scales when the RCI is negative. These results suggest that the RCI may provide useful drought early warning capabilities that could be used to alert stakeholders of an increased risk for drought development.