Characterizing Decades of Cloud Measurements from Combined ARM Profiling Radar and Lidar Measurements

Tuesday, 16 December 2014: 11:35 AM
Karen L Johnson1, Michael P Jensen1, Shannon Baxter2, Tami Toto1, Meng Wang1, Pavlos Kollias3 and Eugene Edmund Clothiaux4, (1)Brookhaven National Laboratory, Upton, NY, United States, (2)SUNY at Geneseo, Geneseo, NY, United States, (3)McGill University, Montreal, QC, Canada, (4)Penn State, University Park, PA, United States
The U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) program has continuously operated profiling cloud radars and micropulse lidars at five fixed sites, for periods ranging from eight to nineteen years. The sites include the U.S. southern Great Plains, the Alaska North Slope and three Tropical Western Pacific locations. The radar and lidar observations, along with ceilometer and precipitation measurements, have been synthesized using ARM's Active Remote Sensing of Clouds (ARSCL) value-added product, which provides cloud boundaries and best-estimate radar reflectivities, mean Doppler velocities and spectral widths. The product’s time resolution ranges from 10 seconds down to 4 seconds, with height resolution of 45 meters or better. Through its use in retrievals of cloud microphysics and dynamics, this high-resolution, long-term data set has the potential to make major contributions toward improved cloud representations in climate models and the understanding of cloud processes. However, it is essential that data set quality and accuracy be assessed and made available to data users in order to maximize utility and reliability.

In this study, we apply a variety of approaches to characterize observation quality throughout the ARSCL data record at each site. We describe instrument availability and radar operating status and possible issues. We track radar sensitivity as a function of time through cirrus detection statistics as well as changes in radar signal saturation level over time. We also examine noise and insect clutter reflectivity levels as possible surrogates for radar calibration changes. Through these and other techniques, we assess the most and least reliable time periods for each instrumented site and provide valuable guidance to potential data users, for both case-study research and long-term climatological applications.