Characterizing Long-Term Observations of Convective Clouds from Combined ARM Profiling Radar and Lidar Measurements
Karen L Johnson1, Michael P Jensen
1, Shannon Baxter
2, Tami Fairless
1, Meng WANG
1, Pavlos Kollias
3 and Eugene Edmund Clothiaux
4, (1)Brookhaven National Laboratory, Upton, NY, United States, (2)SUNY at Geneseo, Geneseo, NY, United States, (3)Stony Brook University, Stony Brook, NY, United States, (4)Penn State, Meteorology and Atmospheric Science, University Park, United States
Contact First Author: Karen L Johnson; kjohnson@bnl.gov
English Abstract:
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 with a particular emphasis on the characterization of convective cloud types. We describe instrument availability and radar operating status and possible issues. Radar sensitivity is tracked 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. We assess the impacts of changes in radar sensitivity and proxy calibration changes on convective cloud property statistics and provide valuable guidance to potential data users, for both case-study research and long-term climatological applications.
