Conceptual data models describe information systems without the burden of implementation details, and are increasingly used to generate code. They could also be analyzed for consistency and to generate test data except that the expressive constraints supported by popular modeling notations make such analysis intractable. In an earlier empirical study of conceptual models created at LogicBlox Inc., Smaragdakis, Csallner, and Subramanian found that a restricted subset of ORM, called ORM−, includes the vast majority of constraints used in practice and, moreover, allows scalable analysis. After that study, however, LogicBlox Inc. obtained a new ORM modeling tool, which supports discovery and specification of more complex constraints than the previous tool. We report findings of a follow-up study of models constructed using the more powerful tool. Our study finds that LogicBlox developers increasingly rely on a small number of features not in the ORM− subset. We extend ORM− with support for two of them: objectification and a restricted class of external uniqueness constraints. The extensions significantly improve our ability to analyze the ORM models created by developers using the new tool. We also show that a recent change to ORM has rendered the original ORM− algorithms unsound, in general; but that an efficient test suffices to show that these algorithms are in fact sound for the ORM− constraints appearing in any of the models currently in use at LogicBlox.

An important cloud optical property derived from elastic backscatter lidars is depolarization ratio because it is used to determine cloud phase. Statistics and trends of volume depolarization ratio were analyzed for four years, 2003-2007, of Cloud Physics Lidar data during five projects of varying geographic locations and meteorological seasons. The volume depolarization ratio was computed using the parallel and perpendicular polarized 1064 nm channels. The majority of the cloud layers yielded a volume depolarization ratio between 0.3 and 0.6 with the volume depolarization ratio frequency distribution centered at 0.45 for ice clouds and 0.05 for water clouds. On average for ice clouds, volume depolarization ratio increased significantly as temperatures decreased. No trend for water clouds was observed, since all water particles are in theory spherical.

In this presentation, we will show results from intercomparison case studies of Cloud Physics Lidar under-flights of GLAS orbit tracks during the GLAS Validation Experiment in October 2003. Compared products include Rayleigh backscatter profiles, calibrated attenuated backscatter, layer identification, optical depth, extinction, and backscatter cross section. We will show examples of GLAS operational optical data products.

This letter proposes the use of solar background radiation scattered by ice clouds for validating space lidar depolarization calibration. The method takes advantage of the fact that the background light scattered by optically thick ice clouds is almost entirely unpolarized. The theory is examined with background light measurements acquired by the Cloud Physics Lidar.

We survey the rapid progress of "off-beam" cloud lidar, from inception to validation via laboratory-scale simulations. Cloud observations from ground, aircraft and even space are covered. Finally, we describe future work in this instrument development effort born out of pure theory in the mid-1990s.

In this presentation, we will show new optical data processing results from the Cloud Physics Lidar during SAFARI-2000. Retrieved products include aerosol and cloud layer location and identification, layer optical depths, vertical extinction profiles, and extinction-to-backscatter (S) ratios for 532 and 1064 nm. The retrievals will focus on the persistent and smoky planetary boundary layer and occasional elevated aerosol layers found in southern Africa during August and September 2000.