Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

Abstract

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 category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications.

Multiscale Applications of Two Online-Coupled Meteorology-Chemistry Models during Recent Field Campaigns in Australia, Part I: Model Description and WRF/Chem-ROMS Evaluation Using Surface and Satellite Data and Sensitivity to Spatial Grid Resolutions

“…The underpredictions are attributed to underestimated CO emissions, inaccurate chemical boundary conditions, and possible overestimated atmospheric mixing in the PBL either during daytime or nighttime. The convective boundary-layer height determined by the lidar backscatter measurements at Westmead is 900 m during daytime on 8 May 2012 [29] and between 500-1700 m during daytime and between 100-250 m at night during the whole SPS2 period [104]. WRF/Chem-ROMS gives good agreement for nocturnal mixing depth but tends to underpredict daytime PBLH at this site.…”

Development and evaluation of pollen source methodologies for the Victorian Grass Pollen Emissions Module VGPEM1.0

“…The loss decay parameter (λ), is defined as a piece-wise polynomial function based on the rain rate such that pollen has a half-life on the plant of 2 d Fig. 3) and the grass pollen record in Melbourne (red); the full sequence of data is shown as circles, with a locally weighted polynomial regression overlaid (Cleveland, 1979). The EVI data are 16 d composites.…”

A Clean Air Plan for Sydney: An Overview of the Special Issue on Air Quality in New South Wales

“…The temperate coastal basin geography of Sydney means it is influenced by both synoptic and meso-scale meteorological phenomena [4]. In particular, cold air drainage into the basin during the cooler part of the year and afternoon sea breezes in the warmer months are frequent, persistent meso-scale processes that impact the city's air quality [5,6].…”