Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties

The project is concerned with the characterization of cloud macrophysical and microphysical properties by combining radar, lidar, and radiometer measurements available from the U.S. Department of Energy's ARM Climate Research Facility (ACRF). To facilitate the production of integrated cloud product by applying different algorithms to the ARM data streams, an advanced cloud classification algorithm was developed to classified clouds into eight types at the SGP site based on ground-based active and passive measurements. Cloud type then can be used as a guidance to select an optimal retrieval algorithm for cloud microphysical property retrieval. The ultimate goal of the effort is to develop an operational cloud classification algorithm for ARM data streams. The vision 1 IDL code of the cloud classification algorithm based on the SGP ACRF site observations was delivered to the ARM cloud translator during 2004 ARM science team meeting. Another goal of the project is to study midlevel clouds, especially mixed-phase clouds, by developing new retrieval algorithms using integrated observations at the ACRF sites. Mixed-phase clouds play a particular role in the Arctic climate system. A multiple remote sensor based algorithm, which can provide ice water content and effective size profiles, liquid water path, and layer-mean effective radius of water droplet, was developed to study arctic mixed-phase clouds. The algorithm is applied to long-term ARM observations at the NSA ACRF site. Based on these retrieval results, we are studying seasonal and interannual variations of arctic mixed-phase cloud macro- and micro-physical properties