Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222
Scott D Chambers, Elise-Andree Guerette, Khalia Monk, Alan D Griffiths, Yang Zhang, Hiep Duc, Martin Cope, Kathryn M Emmerson, Lisa T Chang, Jeremy D Silver, Steven Utembe, Jagoda Crawford, Alastair G Williams, Melita Keywood
ATMOSPHERE | MDPI | Published : 2019
We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short ( < 1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per cate..View full abstract
Y.Z. acknowledges the support by the University of Wollongong (UOW) Vice-Chancellors Visiting International Scholar Award (VISA), the University Global Partnership Network (UGPN), and the NC State Internationalization Seed Grant. Simulations using W-NC1 and W-NC2 were performed on Stampede and Stampede 2, provided as an Extreme Science and Engineering Discovery Environment (XSEDE) digital service by the Texas Advanced Computing Center (TACC), and on Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. We also acknowledge the Clean Air and Urban Landscapes Hub of Australia's National Environmental Science Program for funding.