Evaluation of NU-WRF model performance on air quality simulation under various model resolutions – an investigation within the framework of MICS-Asia Phase III

Abstract

Abstract. Horizontal grid resolution has a profound effect on model performances on meteorology and air quality simulations. In contribution to MICS-Asia Phase III, one of whose goals was to identify and reduce model uncertainty in air quality prediction, this study examined the impact of grid resolution on meteorology and air quality simulation over East Asia, focusing on the North China Plain (NCP) region. The NASA Unified Weather Research and Forecasting (NU-WRF) model has been applied with the horizontal resolutions at 45, 15, and 5 km. The results revealed that, in comparison with ground observations, no single resolution can yield the best model performance for all variables across all stations. From a regional average perspective (i.e., across all monitoring sites), air temperature modeling was not sensitive to the grid resolution but wind and precipitation simulation showed the opposite. NU-WRF with the 5 km grid simulated the wind speed best, while the 45 km grid yielded the most realistic precipitation as compared to the site observations. For air quality simulations, finer resolution generally led to better comparisons with observations for O3, CO, NOx, and PM2.5. However, the improvement of model performance on air quality was not linear with the resolution increase. The accuracy of modeled surface O3 of the 15 km grid was greatly improved over the one from the 45 km grid. A further increase in grid resolution to 5 km, however, showed diminished impact on model performance improvement on O3 prediction. In addition, a 5 km resolution grid showed large advantage in better capturing the frequency of high-pollution occurrences. This was important for the assessment of noncompliance with ambient air quality standards, which was key to air quality planning and management. Balancing the modeling accuracy and resource limitation, a 15 km grid resolution was suggested for future MICS-Asia air quality modeling activity if the research region remained unchanged. This investigation also found a large overestimate of ground-level O3 and an underestimate of surface NOx and CO, likely due to missing emissions of NOx and CO.

Aerosol radiative effects and feedbacks on boundary layer meteorology and PM<sub>2.5</sub> chemical components during winter haze events over the Beijing-Tianjin-Hebei region

“…(3) The direct aerosol radiative effect dominated the feedback effect in this study, so more cases in different regions and seasons, when the indirect effect could be more important, are needed to elucidate the complete feedback mechanism at different spatial and temporal scales. (4) Finer model grid resolution is expected to be applied to look into details of the feedback effect at the urban scale along with finer-resolution emission inventories (Tao et al, 2020), vertical observations (Wilcox et al, 2016;Wang et al, 2018), and higher computational efficiency when available in the future. Finally, this study pointed out the significance and necessity of developing online coupled models for exploring chemistry-aerosol-weather-climate interactions and for improving meteorological and chemical predictions in both air quality and climate research in the future.…”

“…Aerosols affect radiation transfer by scattering or absorbing solar and infrared radiation, by acting as cloud condensation nuclei (CCN) to modify cloud properties, and by heating the atmosphere to alter cloud formation, termed as the aerosol direct radiative effect, indirect effect, and semi-direct effect, respectively (Twomey, 1974;Albrecht, 1989;Ramanathan et al, 2001). In addition, there exists a set of interactions between chemistry, radiation, and meteorology (Dawson et al, 2007;Zhang, 2008;Isaksen et al, 2009;Baklanov et al, 2014;Cai et al, 2017), which is highly complex and nonlinear, and this is currently one of the least understood mechanisms in the atmospheric science community.…”

High-resolution modeling of the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source–receptor model (GNAQPMS-SM)

“…Sensitivity simulation also requires expensive computational resources, which results in most global simulations of pollutants running at coarse horizontal resolutions ranging from 1 • × 1 • to 5 • × 5 • , such as 4 • × 5 • in J. , 2 • × 2.5 • in Han et al (2020), 1 • × 1 • in Crippa et al (2019), and 2.8 • × 2.8 • in Nagashima et al (2017), and S-R relationships are limited to a few regions (e.g., Europe, North America, East Asia, South Asia). Many recent studies have revealed that horizontal resolution has a considerable influence on model performance in air quality simulations (Lin et al, 2010;Tao et al, 2020). Urban-scale pollution plumes are often unresolved in global model simulations with lower resolution, which tend to underestimate the magnitude of pollution formation and destruction, especially in urban areas (Huijnen et al, 2010).…”

High-resolution modeling the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source-receptor model (GNAQPMS-SM)

“…Sensitivity simulation also requires expensive computational resources, which results in most global simulations of pollutants running at coarse horizontal resolutions ranging from 1°×1° to 5°×5° , such as 4°×5° in Zhu et al (2017a), 2°×2.5° in Han et al (2020), 1°×1° in Crippa et al (2019), and 2.8°×2.8° in Nagashima et al (2017), and S-R relationships are limited to a few regions (e.g., Europe, North America, East Asia, South Asia). Many recent studies have revealed that horizontal resolution has a considerable influence on model performance in air quality simulations (Lin et al, 2010;Tao et al, 2020). Urban-scale pollution plumes are often unresolved in global model simulations with lower resolution, which tend to underestimate the magnitude of pollution formation and destruction, especially in urban areas (Huijnen et al, 2010).…”