Cloud properties are currently being derived in near-real time at NASA Langley Research Center from operational geostationary satellite data for applications such as aircraft icing detection and MWP model assimilation. These include cloud amount, top height, thickness, base height, phase, effective particle size, and condensed/frozen water path among others. Data from the Geostationary Operational Environmental Satellites, GOES-11 & 12, are analyzed each half hour over a large portion of North America to provide input for the applications. When these parameters are used it is important to know their uncertainties. Cloud height is a first order parameter for weather forecasting and aircraft icing. Before the advent of lidars in space, it has been very difficult to assess the uncertainties in the cloud heights in any rigorous manner. To have a rapid and continuous evaluation of the cloud-top heights derived from the Langley products, an automated system has been developed to match the archived and near-real-time products with the cloud information determined from the lidars on the Ice Cloud and Land Elevation Satellite (ICESat) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. The new algorithms take the predicted paths of each satellite lidar and extract those GOES pixels that match the path most closely in time and space. The results are compared both visually by overlaying the GOES analyses and quantitatively by differencing the GOES and lidar cloud height products. By using both ICESat and CALIPSO data, it is possible to assess the GOES cloud height uncertainties over many different local times because of differences in the orbits of the two lidar satellites. This paper presents the methodology and results from the initial comparisons.