The topic of combining observations from instruments of different frequencies and sensitivities on remote sensing platforms has been underutilized in undergraduate and graduate atmospheric science curricula, but is vital to obtaining holistic views of the Earth climate system. An online education applet on this topic has been developed for use in radar and remote sensing atmospheric science courses that meets the requirements outlined in Table 1. The developed applet attempts to reduce the curriculum gap by guiding students through atmospheric weather phenomena and allowing them to explore the performance of a variety of radar frequencies and characteristics. This applet leverages a high-resolution cloud-resolving model and satellite radar simulator to provide a synthetic learning laboratory environment. It contains one-to-one comparisons between the simulated environment and approximate observations of that environment through the use of hypothetical remote sensing instruments. A limited set of orbits and planar views are selected to highlight advantages and shortfalls of potential instrument configurations. Future formal assessments of the learning tool will allows its effectiveness to be quantified and guide further improvement of the tool beyond currently planned development activities.

A technique is presented that uses attenuated backscatter profiles from the CALIOP satellite lidar to estimate cloud base heights of lower-troposphere liquid clouds (cloud base height below approximately 3 km). Even when clouds are thick enough to attenuate the lidar beam (optical thickness τ≥5), the technique provides cloud base heights by treating the cloud base height of nearby thinner clouds as representative of the surrounding cloud field. Using ground-based ceilometer data, uncertainty estimates for the cloud base height product at retrieval resolution are derived as a function of various properties of the CALIOP lidar profiles. Evaluation of the predicted cloud base heights and their predicted uncertainty using a second statistically independent ceilometer dataset shows that cloud base heights and uncertainties are biased by less than 10 %. Geographic distributions of cloud base height and its uncertainty are presented. In some regions, the uncertainty is found to be substantially smaller than the 480m uncertainty assumed in the A-Train surface downwelling longwave estimate, potentially permitting the most uncertain of the radiative fluxes in the climate system to be better constrained.