The National Air Quality Forecasting Capability (NAQFC) upgraded its modeling system that provides developmental numerical predictions of particulate matter smaller than 2.5 μm in diameter (PM2.5) in January 2015. The issuance of PM2.5 forecast guidance has become more punctual and reliable because developmental PM2.5 predictions are provided from the same system that produces operational ozone predictions on the National Centers for Environmental Prediction (NCEP) supercomputers. There were three major upgrades in January 2015: 1) incorporation of real-time intermittent sources for particles emitted from wildfires and windblown dust originating within the NAQFC domain, 2) suppression of fugitive dust emissions from snow- and/or ice-covered terrain, and 3) a shorter life cycle for organic nitrate in the gaseous-phase chemical mechanism. In May 2015 a further upgrade for emission sources was included using the U.S. Environmental Protection Agency’s (EPA) 2011 National Emission Inventory (NEI). Emissions for ocean-going ships and on-road mobile sources will continue to rely on NEI 2005. Incremental tests and evaluations of these upgrades were performed over multiple seasons. They were verified against the EPA’s AIRNow surface monitoring network for air pollutants. Impacts of the three upgrades on the prediction of surface PM2.5 concentrations show large regional variability: the inclusion of windblown dust emissions in May 2014 improved PM2.5 predictions over the western states and the suppression of fugitive dust in January 2015 reduced PM2.5 bias by 52%, from 6.5 to 3.1 μg m−3 against a monthly average of 9.4 μg m−3 for the north-central United States.

The sensitivity of regional air quality model to various lateral and top boundary conditions is studied at 2 scales: a 60 km domain covering the whole USA and a 12 km domain over northeastern USA. Three global models (MOZART‐NCAR, MOZART‐GFDL and RAQMS) are used to drive the STEM‐2K3 regional model with time‐varied lateral and top boundary conditions (BCs). The regional simulations with different global BCs are examined using ICARTT aircraft measurements performed in the summer of 2004, and the simulations are shown to be sensitive to the boundary conditions from the global models, especially for relatively long‐lived species, like CO and O₃. Differences in the mean CO concentrations from three different global‐model boundary conditions are as large as 40 ppbv, and the effects of the BCs on CO are shown to be important throughout the troposphere, even near surface. Top boundary conditions show strong effect on O₃ predictions above 4 km. Over certain model grids, the model's sensitivity to BCs is found to depend not only on the distance from the domain's top and lateral boundaries, downwind/upwind situation, but also on regional emissions and species properties. The near‐surface prediction over polluted area is usually not as sensitive to the variation of BCs, but to the magnitude of their background concentrations. We also test the sensitivity of model to temporal and spatial variations of the BCs by comparing the simulations with time‐varied BCs to the corresponding simulations with time‐mean and profile BCs. Removing the time variation of BCs leads to a significant bias on the variation prediction and sometime causes the bias in predicted mean values. The effect of model resolution on the BC sensitivity is also studied.

The National Air Quality Forecast Capability (NAQFC) project provides the US with operational and experimental real-time ozone predictions using two different versions of the three-dimensional Community Multi-scale Air Quality (CMAQ) modeling system. Routine evaluation using near-real-time AIRNow ozone measurements through 2011 showed better performance of the operational ozone predictions. In this work, quality-controlled and -assured Air Quality System (AQS) ozone and nitrogen dioxide (NO₂) observations are used to evaluate the experimental predictions in 2010. It is found that both ozone and NO₂ are overestimated over the contiguous US (CONUS), with annual biases of +5.6 and +5.1 ppbv, respectively. The annual root mean square errors (RMSEs) are 15.4 ppbv for ozone and 13.4 ppbv for NO₂. For both species the overpredictions are most pronounced in the summer. The locations of the AQS monitoring sites are also utilized to stratify comparisons by the degree of urbanization. Comparisons for six predefined US regions show the highest annual biases for ozone predictions in Southeast (+10.5 ppbv) and for NO₂ in the Lower Middle (+8.1 ppbv) and Pacific Coast (+7.1 ppbv) regions. The spatial distributions of the NO₂ biases in August show distinctively high values in the Los Angeles, Houston, and New Orleans areas. In addition to the standard statistics metrics, daily maximum eight-hour ozone categorical statistics are calculated using the current US ambient air quality standard (75 ppbv) and another lower threshold (70 ppbv). Using the 75 ppbv standard, the hit rate and proportion of correct over CONUS for the entire year are 0.64 and 0.96, respectively. Summertime biases show distinctive weekly patterns for ozone and NO₂. Diurnal comparisons show that ozone overestimation is most severe in the morning, from 07:00 to 10:00 local time. For NO₂, the morning predictions agree with the AQS observations reasonably well, but nighttime concentrations are overpredicted by around 100%.

In this study, a regional coupled climate–chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting – Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006–2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2 m temperature (T2) in this study (with a mean bias of −0.6 °C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM_2. 5 in winter (with a normalized mean bias (NMB) of 6.4 % in 2013) and O₃ in summer (with an NMB of 18.2 % in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM_2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM_2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate–chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in east Asia (e.g., SWDOWN and T2 decreased by 21.8 W m⁻² and 0.45 °C, respectively, and most pollutant concentrations increased by 4.8–9.5 % in January over China's major cities) were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled models over east Asia for regional climate and air quality modeling and to study the important climate–chemistry interactions. This work established a baseline for WRF-CMAQ simulations for a future period under the RCP4.5 climate scenario, which will be presented in a future paper.

Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM_2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM_2.5 concentrations near the source region but higher AOD and PM_2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %–30 % near the source regions and 5 %–10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM_2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (>35 µg m⁻³) predictions. Higher plume heights generally produce larger downwind PM_2.5 exceedance areas. The PM_2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM_2.5 concentration and predicted PM_2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM_2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events.