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Jerome D. Fast, Larry K. Berg, Lizbeth Alexander, David Bell, Emma D’Ambro, John Hubbe, Chongai Kuang, Jiumeng Liu, Chuck Long, Alyssa Matthews, Fan Mei, Rob Newsom, Mikhail Pekour, Tamara Pinterich, Beat Schmid, Siegfried Schobesberger, John Shilling, James N. Smith, Stephen Springston, Kaitlyn Suski, Joel A. Thornton, Jason Tomlinson, Jian Wang, Heng Xiao, and Alla Zelenyuk


Shallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land–atmosphere interactions and aerosol–radiation–cloud interactions can influence the formation of shallow clouds and their properties. These processes exhibit large spatial and temporal variability and occur at the subgrid scale for all current climate, operational forecast, and cloud-system-resolving models; therefore, they must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface-layer, boundary layer, and aerosol properties. Predictions of the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data are a key factor needed to achieve a more complete understanding of the life cycle of shallow convective clouds and to develop improved model parameterizations. To address these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north-central Oklahoma during the spring and summer of 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, overall weather conditions during the campaign, and preliminary findings from the measurements. Finally, we discuss scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data.

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Julie K. Lundquist, James M. Wilczak, Ryan Ashton, Laura Bianco, W. Alan Brewer, Aditya Choukulkar, Andrew Clifton, Mithu Debnath, Ruben Delgado, Katja Friedrich, Scott Gunter, Armita Hamidi, Giacomo Valerio Iungo, Aleya Kaushik, Branko Kosović, Patrick Langan, Adam Lass, Evan Lavin, Joseph C.-Y. Lee, Katherine L. McCaffrey, Rob K. Newsom, David C. Noone, Steven P. Oncley, Paul T. Quelet, Scott P. Sandberg, John L. Schroeder, William J. Shaw, Lynn Sparling, Clara St. Martin, Alexandra St. Pe, Edward Strobach, Ken Tay, Brian J. Vanderwende, Ann Weickmann, Daniel Wolfe, and Rochelle Worsnop


To assess current capabilities for measuring flow within the atmospheric boundary layer, including within wind farms, the U.S. Department of Energy sponsored the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign at the Boulder Atmospheric Observatory (BAO) in spring 2015. Herein, we summarize the XPIA field experiment, highlight novel measurement approaches, and quantify uncertainties associated with these measurement methods. Line-of-sight velocities measured by scanning lidars and radars exhibit close agreement with tower measurements, despite differences in measurement volumes. Virtual towers of wind measurements, from multiple lidars or radars, also agree well with tower and profiling lidar measurements. Estimates of winds over volumes from scanning lidars and radars are in close agreement, enabling the assessment of spatial variability. Strengths of the radar systems used here include high scan rates, large domain coverage, and availability during most precipitation events, but they struggle at times to provide data during periods with limited atmospheric scatterers. In contrast, for the deployment geometry tested here, the lidars have slower scan rates and less range but provide more data during nonprecipitating atmospheric conditions. Microwave radiometers provide temperature profiles with approximately the same uncertainty as radio acoustic sounding systems (RASS). Using a motion platform, we assess motion-compensation algorithms for lidars to be mounted on offshore platforms. Finally, we highlight cases for validation of mesoscale or large-eddy simulations, providing information on accessing the archived dataset. We conclude that modern remote sensing systems provide a generational improvement in observational capabilities, enabling the resolution of finescale processes critical to understanding inhomogeneous boundary layer flows.

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