11.3 Accounting for cloud vertical structure to improve parameterizations and applications for passive satellite cloud parameters

Thursday, 18 August 2016: 11:00 AM
Madison Ballroom CD (Monona Terrace Community and Convention Center)
William L. Smith Jr., NASA, Hampton, VA; and P. Minnis, C. Wang, D. A. Spangenberg, S. Sun-Mack, and Y. Chen

Determining accurate cloud properties horizontally and vertically over a full range of time and space scales is currently next to impossible using data from either active or passive remote sensors or from modeling systems. For example, passive satellite imager data provide high horizontal and temporal resolution of clouds, but little direct information on vertical structure. Active sensors provide high vertical resolution but provide limited spatial and temporal coverage. Cloud models embedded in NWP produce realistic clouds in many respects but often not at the right time or location since relatively few observations are being assimilated. Thus, empirical techniques that integrate information from multiple observing and modeling systems are needed to more accurately characterize clouds and their impacts. Such a strategy is employed here in a new cloud water content profiling technique developed for application to satellite imager cloud retrievals based on visible, near-infrared, and infrared radiances. In this approach, parameterizations are developed to relate imager-based retrievals of cloud top phase, optical depth, effective radius and temperature to ice and liquid water content profiles. The vertical structure information contained in the parameterizations depends on cloud type and also provides guidance on cloud phase partitioning. This information is characterized climatologically from cloud model analyses, aircraft observations, ground-based remote sensing data, and from CloudSat and CALIPSO. When applied to geostationary satellite data, the profiling method provides a real-time characterization of clouds in 4-D including a simultaneous retrieval of the ice and liquid water content in deep mixed phase clouds such as those associated with convection and mid-latitude storm systems. Owing to their complexity, these clouds are particularly challenging to characterize accurately from satellite but are important due to their association with hazardous weather and precipitation, and because of their significant contribution to regional cloud water budgets. The method has been applied to GOES data to retrieve super-cooled liquid water contents embedded in deep clouds. These are used in a new application to infer aircraft icing conditions. Pilot reports of icing intensity, which are abundant over the CONUS, confirm the utility of the satellite icing products. These are currently being evaluated by a number of aviation weather forecast offices, including the Aviation Weather Center and the Alaska Aviation Weather Unit. When applied to the global constellation of satellite imagers, the approach also has the potential to improve cloud water path climatologies since it resolves both the liquid and ice fraction separately in deep cloud systems. For example, this method produces monthly mean LWP values that agree considerably better with satellite microwave estimates over oceanic deep convective and storm track areas than previous traditional estimates from MODIS and other imagers. This technique also produces estimates over land areas, which are missing from the microwave climatologies. The initial development and testing have mostly used data take over the contiguous United States. Validation with in situ data and retrievals from microwave and active sensor measurements are encouraging for single-layer cloud conditions, but much more work is needed to test and refine the method for global application in a wider range of cloud conditions. A brief overview of the approach and applications, initial verification, and plans for future work will be presented.
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