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The Sagebrush experiment, led by NOAA's Field Research Division of the Air Resources Laboratory, consisted of five releases (intensive observation periods, or IOPs) of a chemically inert trace gas on five days in October 2013. All releases occurred in the afternoon under either near neutral stability conditions with high wind speeds or unstable conditions with low wind speeds. The sampling network for the tracer concentrations covered distances 200 m–3200 m from the release location and samples were obtained in 10-min averages. HYSPLIT, NOAA's transport and dispersion model, was used to simulate the spatial and temporal distribution of the tracer. The dispersion simulations were driven by WRF meteorological data with 27-km to 333-m grid spacing and using the inline and offline approaches as well as different planetary boundary layer schemes and a large-eddy simulation parameterization. Comparisons with measured wind speeds showed that none of the WRF PBL schemes or the large-eddy simulation parameterization was able to reproduce the rapid increase in high wind speeds observed during IOP3. The dispersion results were compared with the tracer measurements obtained during the experiment. The HYSPLIT dispersion simulations for IOP3, driven by the WRF data generated with various PBL schemes, showed greater concentration variability than the simulations performed for IOP5. The comparison between the inline and offline HYSPLIT simulations showed that the inline approach statistically outperformed the offline approach in three out of four IOPs because the tight coupling between the advection and dispersion processes implemented in the inline approach produced higher simulated concentrations close to the release location.

Seventeen years (2001–2017) of surface observations and spatial synoptic classification (SSC) data are used to analyze the characteristics of surface ozone concentration according to synoptic weather patterns. While weather conditions are known to play an important role in regional air quality, the extent to which synoptic weather patterns affect the production of high ground-level ozone concentrations has not yet been fully quantified. Using thermal characteristics and geographic origins, the SSC method classifies air masses into six types: dry polar (DP), dry moderate (DM), dry tropical (DT), moist polar (MP), moist moderate (MM), and moist tropical (MT). We link daily maximum 8-h ozone concentrations (MDA8 O3) from 306 monitoring sites to the closest SSC classifications at 17 airport sites and then analyze their association. We find that DM, DT, and MT are commonly associated with high ozone, whereas DT produces ozone with the greatest efficiency, especially high levels of concentration. This finding implies a potentially strong connection between surface ozone and climate change because the occurrence of DT weather has increased by more than three times over the past 50 years in South Korea. Sensitivity tests reveal that mean MDA8 ozone may increase by 3.5% (7.5%) as the DT frequency increases by 200% (300%). The impacts are larger for higher levels of concentration, with 31.7% (63.3%) or more prevalence of the >80 ppb range with the same increased DT frequency. We conclude that synoptic weather and its long-term trends play important roles in the increased surface ozone recently seen in South Korea.

The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, developed by the National Oceanic and Atmospheric Administration's Air Resources Laboratory, has been configured to simulate the dispersion and deposition of nuclear materials from a surface-based nuclear detonation using publicly available information on nuclear explosions. Much of the information was obtained from “The Effects of Nuclear Weapons” by Glasstone and Dolan (1977) . The model was evaluated against the measurements of nuclear fallout from six nuclear tests conducted between 1951 and 1957 at the Nevada Test Site using the global NCEP/NCAR Reanalysis Project (NNRP) and the Weather Research and Forecasting (WRF) meteorological data as input. The model was able to reproduce the general direction and deposition patterns using the coarse NNRP data with Figure of Merit in Space (FMS – the percent overlap between predicted and measured deposition patterns) scores in excess of 50% for four of six simulations for the smallest dose rate contour, with FMS scores declining for higher dose rate contours. When WRF meteorological data were used the FMS scores were 5–20% higher in five of the six simulations, especially at the higher dose rate contours. The one WRF simulation where the scores declined slightly (10–30%) was also the best scoring simulation when using the NNRP data. When compared with measurements of dose rate and time of arrival from the Town Data Base ( Thompson et al., 1994 ), similar results were found with the WRF simulations providing better results for four of six simulations. The overall result was that the different plume simulations using WRF data had more consistent performance than the plume simulations using NNRP data fields.

This study is conducted to identify the synoptic weather patterns that are prone to cause high carbon monoxide (CO) concentrations observed at a mountain site, Lulin atmospheric background station (LABS), in Taiwan due to the biomass-burning activity in Southeast (SE) Asia. LABS is recognized as a clean background station. The study period targets the biomass-burning season (February to May) from 2007 to 2010. The synoptic weather patterns were classified using a two-stage clustering method with inputs from the Weather Research and Forecasting (WRF) meteorological model simulation result in a 27-km spatial grid. A 9-km resolution WRF modeling was performed additionally for 13 to 26 March 2007, when a high CO concentration reaching 500 ppb was observed at LABS. The simulation result indicates that not only the existence of the thermal forcing induced low pressure system formed in Indochina, but also the presence of the high terrain located in the northern part of SE Asia that further forced the uplift of the biomass-burning emissions. On the other hand, when the northeasterly monsoonal flow is strong enough and intruding into Indochina, this would hinder the development of the thermal low and weaken the upward movements, in turn preventing the transport of biomass-burning emissions from Indochina to the area of Taiwan. The simulation results also demonstrate that the location of the SE Asia high pressure system has a moderate effect on the particle dispersion path in the upper level.

To achieve more accurate meteorological inputs than was used in the daily forecast for studying the TexAQS 2006 air quality, retrospective simulations were conducted using objective analysis and 3D/surface analysis nudging with surface and upper observations. Model ozone using the assimilated meteorological fields with improved wind fields shows better agreement with the observation compared to the forecasting results. In the post-frontal conditions, important factors for ozone modeling in terms of wind patterns are the weak easterlies in the morning for bringing in industrial emissions to the city and the subsequent clockwise turning of the wind direction induced by the Coriolis force superimposing the sea breeze, which keeps pollutants in the urban area. Objective analysis and nudging employed in the retrospective simulation minimize the wind bias but are not able to compensate for the general flow pattern biases inherited from large scale inputs. By using an alternative analyses data for initializing the meteorological simulation, the model can re-produce the flow pattern and generate the ozone peak location closer to the reality. The inaccurate simulation of precipitation and cloudiness cause over-prediction of ozone occasionally. Since there are limitations in the meteorological model to simulate precipitation and cloudiness in the fine scale domain (less than 4-km grid), the satellite-based cloud is an alternative way to provide necessary inputs for the retrospective study of air quality.

Regional air quality models such as the Community Multiscale Air Quality (CMAQ) model have been widely used to study and simulate multi-scale air quality issues. Although they are capable of providing high quality atmospheric chemistry profiles through the utilization of high resolution inputs relating meteorology and emissions with chemical reactions, they cannot simulate air quality accurately if other input data are not appropriate and reliable. There have been few studies on the importance of chemical initial conditions (ICs) as it is considered that the impact of concentration fields specified at the beginning of simulation wears off quickly. This paper demonstrates that the significant errors during the early part of the simulation can damage model predictions and conversely if the ICs are prescribed appropriately with available observations, they can compensate for the shortcomings of the air quality prediction system especially when the episode-based emissions inputs representing real-life emission variations such as forest fires as well as the effects of long-range transport events that are not reflected in the basic model inputs. The key hypothesis of the present study is that prediction of aerosols can be improved by the initialization of the aerosol fields with the satellite-derived Aerosol Optical Depth (AOD). We compare the effects of using fine mode and total AOD for the initialization in terms of regional bias characteristics. We found that the impacts of two-step initial conditions adjustments could be substantial in the case of aerosol events such as wildfires, which the present modeling system does not consider during simulation due to the deficiency in the emission inputs. The total AOD case helped to refine PM2.5 predictions over the northwestern area, where wildfire events occurred, for the fire event days improving the correlation coefficient significantly from 0.12 to 0.67. CMAQ predicted PM2.5 concentrations in the fine mode case decreased by 10–50 μg/m3 in large areas of the northwestern region, resulting in more realistic PM2.5 predictions with reduced unusual high peak cells. This study suggest that the use of initial conditions adjusted by total or fine mode AOD can help to improve PM2.5 simulations, even though further refinements of the vertical distribution of aerosols are critically needed.

The Tropospheric Ozone Pollution Project (TOPP) launched >220 ozonesondes in Houston (July 2004– June 2008) providing examples of pollution transported into, re-circulated within, and exported from the Houston area. Fifty-one launches occurred during the Texas Air Quality Study (TexAQS) II and the summer portion of IONS-06 (INTEX [Intercontinental Transport Experiment] Ozonesonde Network Study). On 11 days during TexAQS II and on 8 other occasions, ozonesondes were launched both at dawn and in the afternoon. Analysis of these ‘‘intensive’’ launch sequences shows that morning residual layer (RL) ozone concentrations ([O3]) explained 60–70% of the variability found in the afternoon mixed layer (ML). Furthermore, maximum RL [O3] is nearly identical to the mean ML [O3] from the previous afternoon (morning minus afternoon ¼� 1.6 � 8.4 ppbv). During TexAQS II, mean [O3] below 1.3 km (the mean ML height from ozonesonde data) increased from 37 � 22 ppbv in the morning to 74 � 18 ppbv in the afternoon, suggesting an average net local daily O3 production of w500–900 tons over the metropolitan Houston area.