Search

A series of meteorological measurements with a small Uncrewed Aircraft System (sUAS) was collected at Oliver Springs Airport, Tennessee. The sUAS provides a unique observing system capable of obtaining vertical profiles of meteorological data within the lowest few hundred meters of the boundary layer. The measurements benefit simulated plume predictions by providing more accurate meteorological data to a dispersion model. The sUAS profiles can be used directly to drive HYSPLIT dispersion simulations. When using sUAS data covering a small domain near a release and meteorological model fields covering a larger domain, simulated pollutants may be artificially increased or decreased near the domain boundary due to inconsistencies in the wind fields between the two meteorological inputs. Numerical experiments using the Weather Research and Forecasting (WRF) model with observational nudging reveal that incorporating sUAS data improves simulated wind fields and can significantly affect mixing characteristics of the boundary layer, especially during the morning transition period of the planetary boundary layer. We conducted HYSPLIT dispersion simulations for hypothetical releases for three case study periods using WRF meteorological fields with and without assimilating sUAS measurements. The comparison of dispersion results on 15 and 16 December 2021 shows that using sUAS observational nudging is more significant under weak synoptic conditions than strong influences from regional weather. Very different dispersion results were introduced by the meteorological fields used. The observational nudging produced not just a sUAS-nudged wind flow but also adjusted meteorological fields that further impacted the mixing calculation in HYSPLIT.

SO2 mixing ratio measurements were made by a research aircraft close to three power plants in North Carolina on March 26, 2019. An ensemble of dispersion simulations with HYSPLIT model v5.2.0 are carried out using a total of 15 heat release parameters ranging from 10 MW to 150 MW for the Briggs plume rise calculation to quantify some of the modeling uncertainties related to this phenomena. For each heat release value, a total of 72 independent HYSPLIT Lagrangian model runs with unit hourly emissions from the three point sources are made to form a transfer coefficient matrix (TCM) with the airborne observations. The TCMs can be decoupled into six segments where the observations of each segment are only influenced by a single power plant in its morning or afternoon operation. Prior to estimating the power plant emissions, the simulation performance is first evaluated with the correlation coefficients between the observations and the model prediction with constant unit-emission in its morning or afternoon operations. The segment influenced by the afternoon operations of Belews Creek power plant has negative correlation coefficients for all the plume rise options and is excluded from the emission estimate when the “optimal” member is selected based on the correlation coefficient. For the other segments, the plume rise runs with the highest correlation coefficients are selected for the emission estimates using the HYSPLIT inverse modeling system. In the TCM-based inverse modeling, the emission estimates are obtained by minimizing a cost function which measures the difference between logarithmic predicted and observed mixing ratios but also takes model uncertainties into account. A cost function normalization scheme is adopted to avoid spurious emission solutions when using logarithmic concentration differences following Chai et al. (2018). The source estimation results of the three power plants with the morning and afternoon flight segments are compared with the Continuous Emissions Monitoring Systems (CEMS) data. Overestimations are found for all the segments before considering the background SO2 mixing ratios. Both constant background mixing ratios and several segment-specific background values are tested in the HYSPLIT inverse modeling. The estimation results by assuming the 25th percentile observed SO2 mixing ratio inside each of the five segments agree well with the CEMS data, with relative errors as 18 %, -12 %, 3 %, 93.5 %, and -4 %. After emission estimations are performed for all the plume rise runs, least root mean square error (RMSE) between the predicted and observed mixing ratios are calculated to select a different set of “optimal” plume rise runs which have the least RMSEs. Identical plume rise runs are chosen as the “optimal” members for Roxboro and Belews Creek morning segments, but different members for the other segments yield smaller RMSEs than the previous correlation-based “optimal” members. It is also no longer necessary to exclude the Belews Creek afternoon segment that has negative correlation between predictions and observations. The RMSE-based “optimal” runs result in a much better agreement with the CEMS data for the previous severely overestimated segment and do not deteriorate much for the other segments, with relative errors as 18 %, -18 %, 3 %, -9 %, and 27 % for the five segments, and 2 % for Belews Creek afternoon segment. While the RMSE-based “optimal” plume rise runs appear to agree better with the observations than the correlation-based “optimal” runs when they are different, significant differences exist in the area where observations are missing.

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.

A long-term archive of meteorological data using the Weather Research and Forecasting (WRF) Model was created to provide data that are compatible with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) dispersion model and to serve as initial and boundary conditions for simulations at a finer resolution. On the basis of these WRF data, generated with a variety of planetary boundary layer (PBL) schemes and nudging options, the HYSPLIT model was run to simulate four controlled tracer experiments—the Cross Appalachian Tracer Experiment (CAPTEX), the Across North America Tracer Experiment (ANATEX), a 1980 release in Oklahoma City, Oklahoma (OKC80), and the Metropolitan Tracer Experiment (METREX)—covering different time periods with diverse durations, including a summer day, several days in autumn, three months during winter, and one full year, respectively. The evaluation of the WRF results utilizing conventional observations showed a similar statistical performance for the different PBL schemes. Given the limited information the meteorological evaluation alone can provide, the authors used the dispersion evaluation with measurements from multiple tracer experiments to gain further insight into the most appropriate WRF configuration to generate reasonable data for dispersion applications. The dispersion simulations that were based on WRF data generated equal or slightly better statistical performance than did those driven by the North American Regional Reanalysis (NARR) dataset. The statistical comparison showed a mixed impact for the dispersion results driven by the nonnudged and nudged WRF data. The main advantage of the WRF data is the availability of hourly meteorological data from 1980 to the present and the inclusion of additional variables that are relevant to atmospheric dispersion and are not available from NARR. This WRF dataset will be accessible online, providing additional capabilities for using different meteorological inputs and a variety of options to compute the HYSPLIT mixing parameters.

A Hybrid Single-Particle Lagrangian Integrated Trajectory version 4 (HYSPLIT-4) inverse system that is based on variational data assimilation and a Lagrangian dispersion transfer coefficient matrix (TCM) is evaluated using the Cross-Appalachian Tracer Experiment (CAPTEX) data collected from six controlled releases. For simplicity, the initial tests are applied to release 2, for which the HYSPLIT has the best performance. Before introducing model uncertainty terms that will change with source estimates, the tests using concentration differences in the cost function result in severe underestimation, while those using logarithm concentration differences result in overestimation of the release rate. Adding model uncertainty terms improves results for both choices of the metric variables in the cost function. A cost function normalization scheme is later introduced to avoid spurious minimal source term solutions when using logarithm concentration differences. The scheme is effective in eliminating the spurious solutions and it also helps to improve the release estimates for both choices of the metric variables. The tests also show that calculating logarithm concentration differences generally yields better results than calculating concentration differences, and the estimates are more robust for a reasonable range of model uncertainty parameters. This is further confirmed with nine ensemble HYSPLIT runs in which meteorological fields were generated with varying planetary boundary layer (PBL) schemes. In addition, it is found that the emission estimate using a combined TCM by taking the average or median values of the nine TCMs is similar to the median of the nine estimates using each of the TCMs individually. The inverse system is then applied to the other CAPTEX releases with a fixed set of observational and model uncertainty parameters, and the largest relative error among the six releases is 53.3 %. At last, the system is tested for its capability to find a single source location as well as its source strength. In these tests, the location and strength that yield the best match between the predicted and the observed concentrations are considered as the inverse modeling results. The estimated release rates are mostly not as good as the cases in which the exact release locations are assumed known, but they are all within a factor of 3 for all six releases. However, the estimated location may have large errors.

International audience; After performing a first multi-model exercise in 2015 a comprehensive and technically more demanding atmospheric transport modelling challenge was organized in 2016. Release data were provided by the Australian Nuclear Science and Technology Organization radiopharmaceutical facility in Sydney (Australia) for a one month period. Measured samples for the same time frame were gathered from six International Monitoring System stations in the Southern Hemisphere with distances to the source ranging between 680 (Melbourne) and about 17,000 km (Tristan da Cunha). Participants were prompted to work with unit emissions in pre-defined emission intervals (daily, half-daily, 3-hourly and hourly emission segment lengths) and in order to perform a blind test actual emission values were not provided to them. Despite the quite different settings of the two atmospheric transport modelling challenges there is common evidence that for long-range atmospheric transport using temporally highly resolved emissions and highly space-resolved meteorological input fields has no significant advantage compared to using lower resolved ones. As well an uncertainty of up to 20% in the daily stack emission data turns out to be acceptable for the purpose of a study like this. Model performance at individual stations is quite diverse depending largely on successfully capturing boundary layer processes. No single model-meteorology combination performs best for all stations. Moreover, the stations statistics do not depend on the distance between the source and the individual stations. Finally, it became more evident how future exercises need to be designed. Set-up parameters like the meteorological driver or the output grid resolution should be pre-scribed in order to enhance diversity as well as comparability among model runs.

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 model (HYSPLIT), developed by NOAA’s Air Resources Laboratory, is one of the most widely used models for atmospheric trajectory and dispersion calculations. We present the model’s historical evolution over the last 30 years from simple hand-drawn back trajectories to very sophisticated computations of transport, mixing, chemical transformation, and deposition of pollutants and hazardous materials. We highlight recent applications of the HYSPLIT modeling system, including the simulation of atmospheric tracer release experiments, radionuclides, smoke originated from wild fires, volcanic ash, mercury, and wind-blown dust.

A HYSPLIT inverse system that is based on variational data assimilation and a Lagrangian dispersion transfer coefficient matrix (TCM) is evaluated using the Cross Appalachian Tracer Experiment (CAPTEX) data collected from six controlled releases. For simplicity, the initial tests are applied to release 2 for which the HYSPLIT has the best performance. Before introducing model uncertainty terms, the tests using concentration differences in the cost function results in severe underestimation while those using logarithm concentrations differences results in overestimation of the release rate. Adding model uncertainty terms improves results for both choices of the metric variables in the cost function. A cost function normalization scheme is later introduced to avoid spurious minimal source term solutions when using logarithm concentration differences. The scheme is effective in eliminating the spurious solutions and it also helps to improve the release estimates for both choices of the metric variables. The tests also show that calculating logarithm concentration differences generally yield better results than calculating concentration differences and the estimates are more robust for a reasonable range of model uncertainty parameters. This is further confirmed with nine ensemble HYSPLIT runs in which meteorological fields were generated with varying planetary boundary layer (PBL) schemes. In addition, it is found that the emission estimate using a combined TCM by taking the average or median values of the nine TCMs is similar to the median of the nine estimates using each of the TCMs individually. The inverse system is then applied to the other CAPTEX releases with a fixed set of observational and model uncertainty parameters and the largest relative error among the six releases is 53.3 %. At last, the system is tested for its capability to find a single source location as well as its source strength. In these tests, the location and strength that yield the best match between the predicted and the observed concentrations are considered as the inverse modeling results. The estimated release rates are mostly not as good as the cases in which the exact release locations are assumed known, but they are all within a factor of 3 for all the six releases. However, the estimated location may have large errors.

A conservative downscaling technique was applied when comparing nitrogen dioxide (NO2) column densities from space-borne observations and a fine-scale regional model. The conservative downscaling was designed to enhance the spatial resolution of satellite measurements by applying the fine-scale spatial structure from the model, with strict mass conservation at each satellite footprint pixel level. With the downscaling approach, NO2 column densities from the Ozone Monitoring Instrument (OMI; 13 × 24 km nadir footprint resolution) and the Global Ozone Monitoring Experiment-2 (GOME-2; 40 × 80 km) show excellent agreement with the Community Multiscale Air Quality (CMAQ; 4 × 4 km) NO2 column densities, with R = 0.96 for OMI and R = 0.97 for GOME-2. We further introduce an approach to reconstruct surface NO2 concentrations by combining satellite column densities and simulated surface-to-column ratios from the model. Compared with the Environmental Protection Agency’s (EPA) Air Quality System (AQS) surface observations, the reconstructed surface concentrations show a good agreement; R = 0.86 for both OMI and GOME-2. This study demonstrates that the conservative downscaling approach is a useful tool to compare coarse-scale satellites with fine-scale models or observations in urban areas for air quality and emissions studies. The reconstructed fine-scale surface concentration field could be used for future epidemiology and urbanization studies.

A regional air-quality forecast system's model of surface ozone variability based on cloud coverage is evaluated using satellite-observed cloud fraction (CF) information and a surface air-quality monitoring system. We compared CF and daily maximum ozone from the National Oceanic and Atmospheric Administration's National Air Quality Forecast Capability (NOAA NAQFC) with CFs from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the U.S. Environmental Protection Agency's AirNow surface ozone measurements during May to October 2014. We found that observed surface ozone shows a clear (negative) correlation with the MODIS CFs, showing around 1 ppb decrease for 10% MODIS CF change over the Contiguous United States, while the correlation of modeled surface ozone with the model CFs is much weaker, showing only −0.5 ppb per 10% NAQFC CF change. Further, daytime CF differences between MODIS and NAQFC are correlated with modeled surface-ozone biases between AirNow and NAQFC, showing −1.05 ppb per 10% CF change, implying that spatial- and temporal-misplacement of the modeled cloud field might have biased modeled surface ozone-level. Current NAQFC cloud fields seem to be too bright compared to MODIS cloud fields (mean NAQFC CF = 0.38 and mean MODIS CF = 0.55), contributing up to 35% of surface-ozone bias in the current NAQFC system.

The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), a Lagrangian dispersion model, has been coupled (inline) to the the Weather Research and Forecasting (WRF) Model meteorological model in such a way that the HYSPLIT calculation is run as part of the WRF-ARW prediction calculation. This inline version of HYSPLIT takes advantage of the higher temporal frequency of WRF-ARW variables relative to what would be available for the offline approach. Furthermore, the dispersion calculation uses the same vertical coordinate system as WRF-ARW, resulting in a more consistent depiction of the state of the atmosphere and the dispersion simulation. Both inline and the offline HYSPLIT simulations were conducted for two tracer experiments at quite different model spatial resolutions: the Cross Appalachian Tracer Experiment (CAPTEX) in regional scale (at 9-km grid spacing) and the Atmospheric Studies in Complex Terrain (ASCOT) in finescale (at 333.3-m grid spacing). A comparison of the model with the measured values showed that the results of the two approaches were very similar for all six releases in CAPTEX. For the ASCOT experiments, the cumulative statistical score of the inline simulations was better than or equal to offline runs in four of five releases. Although the use of the inline approach did not provide any advantage over the offline method for the regional spatial scale and medium-range temporal scale represented by the CAPTEX experiment, the inline HYSPLIT was able to improve the simulation of the dispersion when compared with the offline version for the fine spatial and temporal resolutions over the complex-terrain area represented by ASCOT. The improvement of the inline over the offline calculation is attributed to the elimination of temporal and vertical interpolation of the meteorological data as compared with the offline version.

Using ensembles to improve the simulation of plume dispersion is becoming a more common practice. One of the biggest challenges in creating ensembles is developing the appropriate member selection process to get the most accurate results, quantify ensemble uncertainty, and use computing resources more efficiently by avoiding the use of redundant model information. In this work, two reduction techniques are tested: one that is independent of observations and is based on the exclusion of redundant members by using uncorrelation as a measure of distance among the members and a second one based on measured data, which minimizes the mean square error (MSE) of the average of all possible model combinations. These techniques are applied to a 24-member ensemble, created by varying the boundary layer parameterizations in the meteorological WRF Model and dispersion Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) simulating six releases of the Cross-Appalachian Tracer Experiment (CAPTEX). Applying the first technique produced results statistically comparable to the full ensemble for four out of six releases, while the second technique shows a similar or superior performance for all cases. Furthermore, to mimic a forecast application, the first day of the tracer release is used to select the ensemble members and the subsequent days are utilized as a forecast proxy to evaluate their performance. The reduced ensembles chosen by applying the technique based on the minimization of the MSE statistically perform similarly to or better than the full ensemble for the forecasting time periods. This suggests that when observational data are available, the application of ensemble reduction techniques provides a potentially promising tool for improving the dispersion forecast.

Measurements at the Grand Bay National Estuarine Research Reserve support a range of research activities aimed at improving the understanding of the atmospheric fate and transport of mercury. Routine monitoring was enhanced by two intensive measurement periods conducted at the site in summer 2010 and spring 2011. Detailed meteorological data are required to properly represent the weather conditions, to determine the transport and dispersion of plumes and to understand the wet and dry deposition of mercury. To describe the mesoscale features that might influence future plume calculations for mercury episodes during the Grand Bay Intensive campaigns, fine-resolution meteorological simulations using the Weather Research and Forecasting (WRF) model were conducted with various initialization and nudging configurations. The WRF simulations with nudging generated reasonable results in comparison with conventional observations in the region and measurements obtained at the Grand Bay site, including surface and sounding data. The grid nudging, together with observational nudging, had a positive effect on wind prediction. However, the nudging of mass fields (temperature and moisture) led to overestimates of precipitation, which may introduce significant inaccuracies if the data were to be used for subsequent atmospheric mercury modeling. The regional flow prediction was also influenced by the reanalysis data used to initialize the WRF simulations. Even with observational nudging, the summer case simulation results in the fine resolution domain inherited features of the reanalysis data, resulting in different regional wind patterns. By contrast, the spring intensive period showed less influence from the reanalysis data.

There are several studies on the effects of emissions of highly reactive volatile organic compounds (HRVOC) from the industrial sources in the Houston- Galveston-Brazoria (HGB) area on the high ozone events during the Texas Air Quality Study (TexAQS) in summer of 2000. They showed that the modeled atmosphere lacked reactivity to produce the observed high ozone event and suggested “imputation” of HRVOC emissions from the base inventory. Byun et al. (2007b) showed the imputed inventory leads to too high ethylene concentrations compared to the measurements at the chemical super sites but still too little aloft compared to the NOAA aircraft. The paper suggested that the lack of reactivity in the modeled Houston atmosphere must be corrected by targeted, and sometimes of episodic, increase of HRV OC emissions from the large sources such as flares in the Houston Ship Channel (HSC) distributed into the deeper level of the boundary layer. We performed retrospective meteorological and air quality modeling to achieve better air quality prediction of ozone by comparison with various chemical and meteorological measurements during the Texas Air Quality Study periods in August-September 2006 (TexA QS-II). After identifying several shortcomings of the forecast meteorological simulations and emissions inputs, we prepared new retrospective meteorological simulations and updated emissions inputs. We utilized assimilated MM5 inputs to achieve better meteorological simulations (detailed description of MM5 assimilation can be found in F. Ngan et al., 2012) and used them in this study for air quality simulations. Using the better predicted meteorological results, we focused on the emissions uncertainty in order to capture high peak ozone which occasionally happens in the HGB area. We described how the ozone predictions are affected by emissions uncertainty in the air quality simulations utilizing different emission inventories and adjustments.

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.

CHP 2014 Module Feedback Form. Form used to solicit feedback on the scoring system. (PDF 13 kb)

Each page represents one student, whose names have been replaced by Roman or Greek capitals. The index student referred in each page is indicated by the down arrow (â ) on the top row of letters. The upper chart indicates the marks the index student gave to each peer (black or colored circles), together with the average points awarded to that peer (grey bars). The lower chart indicates the marks each peer gives the index student (circles) and the average mark the index student received. Note: these marks are the raw marks prior to scaling. If any mark is more than 1.5 marks away from average, this is indicated by coloring the circle (orange for less, red for much [2.5] less, light blue for more, dark blue for much [2.5] more), increasing the shading on the bar, and adding an arrow. By maximizing the graph on screen and running through each page as a slide show, faculty can quickly assess for the presence of collusion between students. (PDF 140 kb)

We demonstrate that daily pollutant transport patterns in East Asia are visible from satellite images when inspected with corresponding synoptic weather analyses. Transport pathways of air pollutants in East Asia are investigated using satellite observations, surface weather charts, and chemical-transport model simulations. It is found that during cool season (fall to spring), pollutant transports in East Asia are largely determined by synoptic weather patterns associated with high pressure system over southern China, which is extended from the Siberia High, and low pressure system over Manchuria, which is initiated by Altai-Sayan cyclogenesis. Based on the relative location and strength of these weather systems, three types of synoptic weather patterns that may contribute to pollutants transport in East Asia, especially in China and Korea, are identified: i.e., (1) a strengthening of the Siberian High and its southeastward propagation; (2) a high-pressure system over southern China followed by a frontal passage associated with a northern low-pressure system; and (3) a stagnant high-pressure system over southern China. For all three patterns, the high-pressure system in southern China is essential for the development of regional air pollution, while frontal activities associated with low-pressure system provide a forcing mechanism to transport those pollutants eastward or southeastward. Observed and simulated surface PM distributions show good agreement in both aerosol optical depth and NO2 column density further implying that anthropogenic emissions also contribute to regional events of high surface PM concentrations. It is argued that the quasi-periodic migration of synoptic weather systems in East Asia works as an efficient pump of pollutants; i.e., regional air pollutions developed under high-pressure systems are transported downstream by low-pressure systems.

The current work focuses on improving volcanic ash forecasts by integrating satellite observations of ash into the Lagrangian transport and dispersion model, HYSPLIT. The accuracy of HYSPLIT output is dependent on the accuracy of the initialization: the initial position, size distribution and amount of ash as a function of time. Satellite observations from passive infrared, IR, sensors are used both to construct the initialization term and for verification. Space-based lidar observations are used for further verification. We compare model output produced using different initializations for the 2008 eruption of Kasatochi in the Aleutian Islands. Simple source terms, such as a uniform vertical line or cylindrical source above the vent, are compared to initializations derived from satellite measurements of position, mass loading, effective radius and height of the downwind ash cloud. Using satellite measurements of column mass loading of ash to constrain the source term produces better long term predictions than using an empirical equation relating mass eruption rate and plume height above the vent. Even though some quantities, such as the cloud thickness, must be estimated, initializations which release particles at the position of the observed ash cloud produce model output which is comparable to or better than the model output produced with source terms located above and around the vent. Space-based lidar data, passive IR retrievals of ash cloud top height, and model output agree well with each other, and all suggest that the Kasatochi ash cloud evolved into a complex three dimensional structure.