Expanding Curtain Observations of Cloud Vertical Structure and Layering to Model-Relevant Spatial Scales

Clouds, representing perhaps the most obvious physical manifestations of atmospheric dynamics at work, remain in many ways an enigmatic and unifying intellectual challenge to researchers of all disciplines within the atmospheric sciences. Given the universally acknowledged importance of cloud systems in determining the state of current and future climate through radiative, chemical, dynamic, and thermodynamic processes tied intimately to the hydrological cycle, it is no wonder that so much recent attention has been given to better understanding the non-linear feedbacks involving clouds and ways to improve their handling in numerical weather prediction (NWP) models. In terms of operational community interests, knowledge of cloud vertical structure, ceiling (cloud base) height, and phase is key to aviation safety assurance in the private, commercial, and defense-agency sectors alike. The launch of the NASA Earth System Science Pathfinder CloudSat (cloud radar; 3 mm wavelength) mission in 2006 changed forever the way we view cloud systems from the space platform--providing vertically-resolved 'cuts' through the cloudy troposphere. The Cloud Profiling Radar (CPR) system resolves nearly all radiatively significant cloud structures present in the column at vertical resolutions sufficient to afford scientists the opportunity to examine new hypotheses on cloud formation (leading potentially to new/improved cloud process parameterizations) and make observationally-based discoveries bordering on the frontiers of our current understanding. At the same time, the non-scanning nature of the CPR (providing so-called 'curtain' observations) represents in some respects a frustrating tease to the potential of a three-dimensional scanning system, relegating its utility to the realms of research as opposed to full spatial environmental characterization and data assimilation. This research examines ways to extend via statistical methods the curtain slices provided by CloudSat into the horizontal to construct pseudo three-dimensional information. These statistics are based on cloud-type classification, which are identifiable from cloud top observations by conventional 2-D observing systems. Preliminary cloud-type-dependent vertical structures, based on the CloudSat Level-2 Cloud Water Content (CWC) product, are presented for an assortment of cloud classifications. Such statistics can then be applied to the vertically-integrated liquid/ice water content as retrieved by 2-D sensors to distribute this water in the column according to type-dependency. In addition, the degree to which cloud layer base heights can be extended into the cross-track direction (e.g., given an observation of similar cloud-type from a conventional 2-D optical radiometer) can be assessed via correlation lengths computed along the CloudSat track. The effective result is a pseudo 3-D swath of cloud water content of potential use to operational support and numerical weather prediction analysis and/or validation. Preliminary results from the currently available compilation of CloudSat data are presented to illustrate conceptually the potential and limitations of such approaches.