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.
Highlights
The population density of urban centers is rising globally and source strengths of fine particles to which residents are exposed scales directly with population [1]
Low radon on 18–19 April (Figure S1) coincided with humid SE winds associated with the low-pressure trough, whereas low radon on 25 April was associated with dry fast-moving air from the NNW, with high ozone but low concentrations of other pollutants (Figure S2); consistent with downward mixing of tropospheric air to the atmospheric boundary layer (ABL)
The stability classification approach enabled the identification/isolation of synoptic non-stationary conditions so that simulated and observed results could be compared at night, under: well mixed, weakly mixed and most-stable conditions; and in the urban boundary layer as a function of the atmospheric mixing state
Summary
The population density of urban centers is rising globally and source strengths of fine particles to which residents are exposed scales directly with population [1]. Considering the scale and complexity of the problem, state-of-the-art chemical transport models (CTMs) linked to representative emissions inventories and driven by reliable prognostic meteorological models, potentially offer the most cost-effective means of providing valuable (if approximate) inputs to these problems in a timely manner. To this end, as well as updating emissions inventories, CTMs need to be continually improved and evaluated to reflect advancements in understanding of contributing physical and chemical processes, as well as computational ability. Skill-testing CTMs serves a dual purpose: on one hand it provides assurance (or otherwise) of the efficacy of incremental modifications, while on the other hand it provides valuable information on the level of confidence that can be placed in various representations and timescales of CTM output
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