Abstract Wet and dry depositions of aerosols in WRF‐Chem are revisited and updated based on recent observational findings. Traditionally, in‐cloud scavenging was thought to play a more dominant role in aerosol wet removal than below‐cloud scavenging. However, recent field measurements indicated a considerable contribution of below‐cloud scavenging of 50%–60% to total wet deposition. In contrast, the simulated contribution of in‐cloud scavenging in the previous version of WRF‐Chem was too large, exhibiting 88%–95%, likely due to the binary representation of cloud fraction. To reduce the model bias, this study adopts a continuous‐type cloud fraction and implements a semi‐empirical below‐cloud scavenging parameterization. Simulation results with the new scheme show that the contribution of below‐cloud (in‐cloud) scavenging is increased to 63%–66% (decreased to 34%–37%), well capturing the observational estimates. The magnitude of total wet deposition is increased by 18.2% for SO4, 7.16% for NO3, and 14.8% for NH4, showing better agreements with observations particularly for SO4 and NH4 deposition. The increased wet removal with the new scheme reduces and so better reproduces surface PM2.5 and PM10 concentrations, which is also partly attributed to the increased contribution of below‐cloud scavenging. It is found that dry deposition velocity in the previous version was too high for coarse mode particles when friction velocity is large, which underestimates surface PM10 concentration. The updated dry deposition scheme that is constrained by observations effectively improves PM10 performance by reducing the dry deposition velocity for coarse mode particles.

Clouds and precipitation play critical roles in wet removal of aerosols and soluble gases in the atmosphere, and hence their accurate prediction largely influences accurate prediction of air pollutants. In this study, the impacts of clouds and precipitation on wet scavenging and long-range transboundary transport of pollutants are examined during the 2016 Korea-United States Air Quality (KORUS-AQ) field campaign using the Weather Research and Forecasting Model coupled with chemistry. Two simulations}one in which atmospheric moisture is constrained and one in which it is not}are performed and evaluated against surface and airborne observations. The simulation with moisture constraints is found to better reproduce precipitation as well as surface PM2.5, whereas the areal extent and amount of precipitation are overpredicted in the simulation without moisture constraints. As a results of overpredicted clouds and precipitation and consequently overpredicted wet scavenging, PM2.5 concentration is generally underpredicted across the model domain in the simulation without moisture constraints. The effects are significant not only in the precipitating region (upwind region, southern China in this study) but also in the downwind region (South Korea) where no precipitation is observed. The difference in upwind precipitation by 77% on average between the two simulations leads to the difference in PM2.5 by similar to 39% both in the upwind and downwind regions. The transboundary transport of aerosol precursors, especially nitric acid, has a considerable impact on ammonium-nitrate aerosol formation in the ammonia-rich downwind region. This study highlights that skillful prediction of atmospheric moisture can have ultimate potential to skillful prediction of aerosols across regions.

Severe air pollution is hazardous to human health and long-term exposure to air pollution degrades not only human health but also the quality of life. In the recent years, public concern and awareness of air quality have been greatly raised in South Korea, and this is somewhat contradictory to the level of particulate matter with diameters less than 10 μm (PM10). The observed PM10 levels cannot explain the elevated levels of public concern specifically after 2013–2014 because the average PM10 was much higher in the past (prior to 2013) and shows a decreasing tendency during the recent decades over South Korea. This study utilizes big data from internet search engines (internet search volume data from Google and NAVER) to understand how people perceive air quality differently from the level of observed PM10 and what influences public perception of air quality. An index, air quality perception index (AQPI), is newly proposed in this study and it is assumed that the internet search volume data with a keyword of “air quality” are representative of this index. An empirical model that simulates AQPI is developed by employing the decay theory of forgetting and is trained by PM10, visibility, and internet search volume data. The results show that the memory decay exponent and the accumulation of past memory traces, which represent the weighted sum of past perceived air quality, play key roles in explaining the public's perception of air quality. A severe haze event with an extremely long duration that occurred in the year 2013–2014 is found to trigger the increase in public awareness of air quality, acting as a turning point. Before the turning point, AQPI is more influenced by sensory information (visibility) due to the low awareness level, but after the turning point it is more influenced by PM10 and people slowly forget about air quality. The retrospective AQPI analysis assuming a low level of awareness confirms that perceived air quality is indeed worst in the year 2013–2014. In other words, the high level of awareness after experiencing the record-long severe haze event in 2013–2014 makes people remember longer and more sensitive to the level of pollutants, thus explaining the increased public concerns in the recent years. Our results suggest the promising potential of social data for a better understanding of public perception and awareness of other natural and/or man-made hazards.

Urban trees are one of the most effective strategies to mitigate excessive heat stress in cities. To understand the underlying mechanisms of their cooling effect and to assess their use in urban planning, the accurate simulation of how trees interact with the ambient built environment is critical and imperative. However, the representation of urban trees in existing urban canopy models (in particular the single-layer ones) remains oversimplified. Here we develop a new Monte Carlo ray tracing method to explicitly resolve the canopy transmittance and evaluate its impact on radiative view factors between trees and regular building facets. The new method is highly accurate in reproducing analytical solutions. Sensitivity tests of radiative view factors suggest the importance of canopy transmittance in changing the radiation exchange. We then incorporate the ray tracing algorithm into the new version of the Arizona State University (ASU) Single-Layer Urban Canopy Model (ASLUM v3.1). In addition to radiation transmittance, ASLUM v3.1 explicitly resolves the radiative shading, evapotranspiration, and root water uptake of urban trees in street canyons, with significantly improved performance in predictions (especially latent heat flux) when compared to previous versions. We further apply ASLUM v3.1 to evaluate the impacts of trees with varying characteristics on urban radiation exchange and turbulent heat fluxes. Results show that urban trees reduce the net radiation of ground and wall as well as the daytime temperature via shading and transpiration, but may slightly warm the nighttime street canyons through radiative trapping effect.

We present in this technical note the research protocol for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This research initiative is divided into two activities, collectively having three goals: (i) to define the current state of the science with respect to representations of wet and especially dry deposition in regional models, (ii) to quantify the extent to which different dry deposition parameterizations influence retrospective air pollutant concentration and flux predictions, and (iii) to identify, through the use of a common set of detailed diagnostics, sensitivity simulations, model evaluation, and reduction of input uncertainty, the specific causes for the current range of these predictions. Activity 1 is dedicated to the diagnostic evaluation of wet and dry deposition processes in regional air quality models (described in this paper), and Activity 2 to the evaluation of dry deposition point models against ozone flux measurements at multiple towers with multiyear observations (to be described in future submissions as part of the special issue on AQMEII4). The scope of this paper is to present the scientific protocols for Activity 1, as well as to summarize the technical information associated with the different dry deposition approaches used by the participating research groups of AQMEII4. In addition to describing all common aspects and data used for this multi-model evaluation activity, most importantly, we present the strategy devised to allow a common process-level comparison of dry deposition obtained from models using sometimes very different dry deposition schemes. The strategy is based on adding detailed diagnostics to the algorithms used in the dry deposition modules of existing regional air quality models, in particular archiving diagnostics specific to land use-land cover (LULC) and creating standardized LULC categories to facilitate cross-comparison of LULC-specific dry deposition parameters and processes, as well as archiving effective conductance and effective flux as means for comparing the relative influence of different pathways towards the net or total dry deposition. This new approach, along with an analysis of precipitation and wet deposition fields, will provide an unprecedented process-oriented comparison of deposition in regional air quality models. Examples of how specific dry deposition schemes used in participating models have been reduced to the common set of comparable diagnostics defined for AQMEII4 are also presented.

A 16-year (2003–2018) evaluation of CO, NO2, SO2, O3, and PM10 from three reanalysis products (CAMS, TCR-2, and MERRA-2) against independent observations and intercomparison among reanalyses are performed over South Korea. All three reanalyses show significant and persistent biases in the five pollutants, but the reanalyses partly capture decreasing trends in CO, NO2, SO2, and PM10 and increasing trends in O3 over the 16-year period. CAMS outperforms TCR-2 and MERRA-2 in terms of area-averaged climatology and trends of all species except for SO2. SO2 is best reproduced in TCR-2. The long-term averaged spatial distributions of the pollutants in CAMS reveal that their spatial distributions are largely influenced by a priori emissions. All reanalysis products show larger biases in CO, NO2, and SO2 in cold months than in warm months. The average daily correlations between bias-corrected reanalysis data and observations are found to be reasonably high for some species: 0.74–0.82 for CO, NO2, and PM10 in CAMS, 0.66 for SO2 in TCR-2, and 0.7 for PM10 in MERRA-2. Although TCR-2 generally shows lower daily correlations than CAMS, it shows the potential that correction of emissions through data assimilation can improve its performance at seasonal and interannual time scales. It is recommended that a bias correction be applied to the reanalysis products for use in regional air quality modeling as initial/boundary conditions or in other relevant studies because each reanalysis product has persistent biases throughout season/year. The evaluation of vertical profiles during KORUS-AQ campaign indicates that CAMS reasonably captures CO, SO2, O3, and aerosols but underestimates NO2.

Clouds play a critical role in modulating tropospheric radiation and thus photochemistry. We develop a methodology for calculating the vertical distribution of tropospheric ultraviolet (300–420 nm) actinic fluxes using satellite cloud retrievals and a radiative transfer model. We demonstrate that our approach can accurately reproduce airborne‐measured actinic fluxes from the 2013 Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign as a case study. The results show that the actinic flux is reduced below moderately thick clouds with increasing cloud optical depth and can be enhanced by a factor of 2 above clouds. Inside clouds, the actinic flux can be enhanced by up to 2.4 times in the upper part of clouds or reduced up to 10 times in the lower parts of clouds. Our study suggests that the use of satellite‐derived actinic fluxes as input to chemistry‐transport models can improve the accuracy of photochemistry calculations.

Low-level convergence induced by land surface heterogeneities can have substantial influence on atmospheric convection and rainfall. Analyses of heavy convective rainfall in the Baltimore-Washington metropolitan area are performed using the Weather Research and Forecasting (WRF) Model, coupled with the Princeton Urban Canopy Model (PUCM) that resolves urban subfacet heterogeneity. Analyses center on storms that produced heavy rainfall and record urban flooding in Baltimore on 1 June 2012. The control simulation using PUCM shows a better performance in reproducing the surface energy balance and rainfall than the simulation using a traditional slab model for the urban area. Sensitivity experiments are carried out to identify the role of the land surface heterogeneities, arising from land-water and urban-nonurban contrasts in the Baltimore-Washington metropolitan area, on heavy rainfall from organized thunderstorm systems. The intersection of low-level convergence zones from thunderstorm downdrafts and from the bay breeze from the Chesapeake Bay enhances the upward motion of preexisting convective storms. The larger sensible heating from the urban area modifies the low-level temperature and wind fields, which in turn modifies the bay breeze. The enhanced moisture supply in the deepened bay-breeze inflow layer due to urban heating promotes intense convection and heavy rainfall in conjunction with the enhanced upward motion at intersecting convergence zones. This study suggests that better representations of surface heat and moisture fluxes in urban areas along major water bodies are required to better capture the timing and location of severe thunderstorms and heavy rainfall.

A single-layer urban canopy model that captures sub-facet heterogeneity and various hydrological processes is further developed to explicitly incorporate trees within the urban canyon. The physical processes associated with trees are shortwave/longwave radiation exchange, including mutual interception and shading by trees and buildings and multiple reflections, sensible heat and latent heat (through transpiration) exchange, and root water uptake. A computationally-efficient geometric approach is applied to the radiation exchanges, requiring a priori knowledge of view factors. These view factors are first obtained from independent Monte Carlo ray-tracing simulations, and subsequently simple relations, which are functions of canyon aspect ratio and tree-crown ratio, are proposed to estimate them. The developed model is evaluated against field observations at two urban sites and one suburban site, showing improved performance for latent heat flux compared to the previous version that only includes ground vegetation. The trees in the urban canopy act to considerably decrease sensible heat flux and increase latent heat flux, and these effects are found to be more significant in the more dense urban site. Sensitivity tests are then performed to examine the effects of tree geometry relative to canyon geometry. The results indicate that the tree-crown size relative to canyon width is the most influential parameter to decrease sensible heat flux and increase latent heat flux, resulting in cooling of the urban area.

Modified local meteorology owing to heterogeneities in the urban–rural surface can affect urban air quality. In this study, the impacts of urban land-surface forcing on ozone air quality during a high ozone (O3) episode in the Seoul metropolitan area, South Korea, are investigated using a high-resolution chemical transport model (CMAQ). Under fair weather conditions, the temperature excess (urban heat island) significantly modifies boundary layer characteristics/structures and local circulations. The modified boundary layer and local circulations result in an increase in O3 levels in the urban area of 16 ppb in the nighttime and 13 ppb in the daytime. Enhanced turbulence in the deep urban boundary layer dilutes pollutants such as NOx, and this contributes to the elevated O3 levels through the reduced O3 destruction by NO in the NOx-rich environment. The advection of O3 precursors over the mountains near Seoul by the prevailing valley-breeze circulation in the mid- to late morning results in the build-up of O3 over the mountains in conjunction with biogenic volatile organic compound (BVOC) emissions there. As the prevailing local circulation in the afternoon changes to urban-breeze circulation, the O3-rich air masses over the mountains are advected over the urban area. The urban-breeze circulation exerts significant influences on not only the advection of O3 but also the chemical production of O3 under the circumstances in which both anthropogenic and biogenic (natural) emissions play important roles in O3 formation. As the air masses that are characterized by low NOx and high BVOC levels and long OH chain length are advected over the urban area from the surroundings, the ozone production efficiency increases in the urban area. The relatively strong vertical mixing in the urban boundary layer embedded in the sea-breeze inflow layer reduces NOx levels, thus contributing to the elevated O3 levels in the urban area.