4.6 An Integrated Approach to Improve Cloud Characterizations from Satellite Imager Data for Weather and Climate Applications

Wednesday, 13 January 2016: 9:45 AM
Room 345 ( New Orleans Ernest N. Morial Convention Center)
William L. Smith Jr., NASA, Hampton, VA; and P. Minnis, C. Fleeger, 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 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 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 provides guidance on cloud phase partitioning. This information is characterized climatologically from CloudSat and CALIPSO active sensor data, from cloud model analyses, aircraft observations and ground-based remote sensing data. While the method is designed for application to any cloud type, it is demonstrated here for deep ice over water clouds such as those associated with convection and mid-latitude storm systems. Owing to their complexity, these clouds are particularly challenging to characterize 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. A unique outcome of our profiling approach is a simultaneous retrieval of the ice and liquid water content in these types of clouds. When compared to CloudSat and CALIPSO retrievals, the imager-based IWC retrievals in the upper troposphere agree to within 30% on average. The satellite imager estimates of the embedded LWP are also found to agree reasonably well with ground-based microwave radiometer observations over a wide range of overlapping cloud conditions. Retrievals of the super-cooled LWC and LWP are another outcome of the profiling method. These are used to estimate the icing threat to aircraft using additional guidance from an airfoil modeling study and are verified using icing intensity reports from pilots (PIREPS). Considerable skill is found for detecting icing conditions embedded in overlapping clouds and for discerning the more dangerous conditions often associated with higher values of LWC. Overall, the method appears to provide an unprecedented level of accuracy for determining the cloud water budget in deep ice overlapping liquid cloud systems. When applied to geostationary satellite data, the profiling method provides a real-time characterization of clouds in 4-D. When applied to polar-orbiting imagers such as MODIS, the approach 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 synergistic approach presented here provides a new framework to improve the instantaneous resolution of clouds for weather applications, and has the potential to improve knowledge of the global distribution of cloud ice and liquid water for model evaluation and climate studies. A brief description of the method, motivation, and results from initial verification studies will be presented.
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