Assimilation of conventional and satellite wind observations in a mesoscale atmospheric model for studying atmospheric dispersion

A mesoscale atmospheric model PSU/NCAR MM5 is used to provide operational weather forecasts for a nuclear emergency response decision support system on the southeast coast of India. In this study the performance of the MM5 model with assimilation of conventional surface and upper-air observations along with satellite derived 2-d surface wind data from QuickSCAT sources is examined. Two numerical experiments with MM5 are conducted: one with static initialization using NCEP FNL data and second with dynamic initialization by assimilation of observations using four dimensional data assimilation (FDDA) analysis nudging for a pre-forecast period of 12 h. Dispersion simulations are conducted for a hypothetical source at Kalpakkam location with the HYSPLIT Lagrangian particle model using simulated wind field from the above experiments. The present paper brings out the differences in the atmospheric model predictions and the differences in dispersion model results from control and assimilation runs. An improvement is noted in the atmospheric fields from the assimilation experiment which has led to significant alteration in the trajectory positions, plume orientation and its distribution pattern. Sensitivity tests using different PBL and surface parameterizations indicated the simple first order closure schemes (Blackadar, MRF) coupled with the simple soil model have given better results for various atmospheric fields. The study illustrates the impact of the assimilation of the scatterometer wind and automated weather stations (AWS) observations on the meteorological model predictions and the dispersion results.     

The effect of improved nowcasting of precipitation on air quality modeling

The predictive potential of air quality models and thus their value in emergency management and public health support are critically dependent on the quality of their meteorological inputs. The atmospheric flow is the primary cause of the dispersion of airborne substances. The scavenging of pollutants by cloud particles and precipitation is an important sink of atmospheric pollution and subsequently determines the spatial distribution of the deposition of pollutants. The long-standing problem of the spin-up of clouds and precipitation in numerical weather prediction models limits the accuracy of the prediction of short-range dispersion and deposition from local sources. The resulting errors in the atmospheric concentration of pollutants also affect the initial conditions for the calculation of the long-range transport of these pollutants. Customary the spin-up problem is avoided by only using NWP (Numerical Weather Prediction) forecasts with a lead time greater than the spin-up time of the model. Due to the increase of uncertainty with forecast range this reduces the quality of the associated forecasts of the atmospheric flow.In this article recent improvements through diabatic initialization in the spin-up of large-scale precipitation in the Hirlam NWP model are discussed. In a synthetic example using a puff dispersion model the effect is demonstrated of these improvements on the deposition and dispersion of pollutants with a high scavenging coefficient, such as sulphur, and a low scavenging coefficient, such as cesium-137. The analysis presented in this article leads to the conclusion that, at least for situations where large-scale precipitation dominates, the improved model has a limited spin-up so that its full forecast range can be used. The implication for dispersion modeling is that the improved model is particularly useful for short-range forecasts and the calculation of local deposition. The sensitivity of the hydrological processes to proper initialization implies that the spin-up problem may reoccur with changes in the model and increased model resolution. Spin-up should be an ongoing concern for atmospheric modelers.     

Norwegian Meteorological Institute’s real-time dispersion model snap (Severe Nuclear Accident Program): Runs for ETEX and ATMES II experiments with different meteorological input

The Norwegian Meteorological Institute (DNMI) has developed and implemented for operational use a real-time dispersion model Severe Nuclear Accident Program (SNAP) with capability for predicting concentrations and depositions of the radioactive debris from large accidental releases. SNAP has been closely linked to DNMI’s operational numerical weather prediction (NWP) models.How good are these predictions? Participation in ETEX has partly answered this question. DNMI used SNAP with LAM50S giving meteorological input for these real-time dispersion calculations. LAM50S Limited Area Model with 50 km grid squareswas DNMI’s operational NWP model in 1994 when ETEX took place.In this article we report on how SNAP performed in the first of the ETEX releases in near-real-time mode, using LAM50S—and in hindcast mode for ATMES II, using “ECMWF 1995: ETEX Data set (ATMES II)”as meteorological input data. These two input data sets came from NWP models with quite different characteristics but with similar resolution in time and space.The results from these dispersion simulations matched closely. Deviations early in the simulation period shrank to insignificant differences later on. Since both input data sets were based on “weather analysis” and had similar resolution in space and time, SNAP described the dispersion of the released material very similar in these two simulations.     

A hybrid plume model for local-scale atmospheric dispersion

This paper presents a local-scale dispersion model, based on atmospheric boundary layer scaling theory. In the vicinity of the source, Gaussian equations are used in both the horizontal and vertical directions. After a specified transition distance, gradient transfer theory is applied in the vertical direction, while the horizontal dispersion is still assumed to be Gaussian. The dispersion parameters and eddy diffusivity are modelled in a form, which facilitates the use of a meteorological pre-processor. We present a novel model of the vertical eddy diffusivity (K_z), which is a continuous function of height in various atmospheric scaling regions. The model also includes a treatment of the dry deposition of gases and particulate matter. The accuracy of the numerical model was analysed by comparing the model predictions with two analytical solutions; the numerical deviations from these solutions were less than 2% for the computational regime. The model has been tested against the Kincaid experimental field data. The agreement of the predictions and the data is good on the average, although the internal variation of the predictions versus data scatter plot is substantial.     

Evaluation of the Meteo-France response in ETEX release 1

During ETEX Meteo-France applied part of its emergency response system for critical events developped in the framework of the World Meteorological Organization environmental emergency response program. The atmospheric transport model used to forecast the evolution of a passive tracer is an eulerian model called MEDIA. In real time this model is driven by meteorological data from ARPEGE, the operational numerical weather prediction model available at the Meteo-France operation center. The overall evaluation of the results show that the model can reproduce the cloud displacement, but there exists a stretching in the transport direction. In the ATMES-II phase, the results are closer to the observations when meteorological data from the European Center for Medium range Weather Forecast are used. A simulation using analyzed meteorological data from ARPEGE every 6 h slightly improve the results comparing with the real-time experiment. All the simulations we performed reveal that the quality of the atmospheric transport model is strongly dependent on the quality of the driving numerical weather prediction model.     

Experimental verification for real-time environmental emergency response system: WSPEEDI by European tracer experiment

Japan Atomic Energy Research Institute has developed an emergency response system WSPEEDI to forecast long-range atmospheric dispersions of radionuclides discharged into the atmosphere. The latest version of WSPEEDI consists of an atmospheric dynamic model MM5 for calculating meteorological fields and a particle random-walk model for atmospheric dispersion. The performance of WSPEEDI was evaluated by data obtained from a field tracer experiment over Europe (ETEX) in this paper. The model validation was done with respect to the following points: (1) the dependence of model accuracy on the temporal and spatial resolutions of the meteorological fields and (2) the superiority of an atmospheric dynamic model over a mass-consistent wind model. Regarding (1), it was shown that the calculation accuracy of the new version with high temporal resolution was improved, especially at the edge of the plume. Moreover, although the increase in horizontal spatial resolution of the old version had no substantial effect on the model performance, increase in horizontal resolution of the new version contributed to the significant improvement of the calculation accuracy. These results showed that the dynamically calculated meteorological field with the spatial resolution of the meso-β–γ scale greatly improved calculation accuracy.     

Assimilating concentration observations for transport and dispersion modeling in a meandering wind field

Assimilating concentration data into an atmospheric transport and dispersion model can provide information to improve downwind concentration forecasts. The forecast model is typically a one-way coupled set of equations: the meteorological equations impact the concentration, but the concentration does not generally affect the meteorological field. Thus, indirect methods of using concentration data to influence the meteorological variables are required. The problem studied here involves a simple wind field forcing Gaussian dispersion. Two methods of assimilating concentration data to infer the wind direction are demonstrated. The first method is Lagrangian in nature and treats the puff as an entity using feature extraction coupled with nudging. The second method is an Eulerian field approach akin to traditional variational approaches, but minimizes the error by using a genetic algorithm (GA) to directly optimize the match between observations and predictions. Both methods show success at inferring the wind field. The GA-variational method, however, is more accurate but requires more computational time. Dynamic assimilation of a continuous release modeled by a Gaussian plume is also demonstrated using the genetic algorithm approach.     

Evaluation of results of a numerical simulation of dispersion in an idealised urban area for emergency response modelling

WinMISKAM is evaluated from an emergency response perspective. Comparisons are made between ground level concentrations observed during selected Mock Urban Setting Test (MUST) field trials and predictions generated by the model. The model was driven by 5 min averaged on-site meteorological data, and used minimum grid spacing of 0.5 m in both the horizontal and vertical. The code was found to perform well, with 46% of all predictions (paired in time and space) and 83% of arc maxima predictions within a factor of two of observed concentrations. The model was found to perform better for neutral cases than stable cases with 27% of stable case predictions and 57% of neutral case predictions within a factor of two when compared in time and space.     

Evaluation of the CAR-FMI model against measurements near a major road

A field measurement campaign was conducted near a major road in southern Finland from September 15 to October 30, 1995. The concentrations of NO, NO₂ and O₃ were measured simultaneously at three locations, at three heights (3.5, 6 and 10 m) on both sides of the road. Traffic densities and relevant meteorological parameters were also measured on-site. We have compared measured concentration data with the predictions of the road network dispersion model CAR-FMI, used in combination with a meteorological pre-processing model MPP-FMI. In comparison with corresponding results presented previously in the literature, the agreement of measured and predicted datasets was good, as measured using various statistical parameters. For all data (N=587), the index of agreement (IA) was 0.83, 0.82 and 0.89 for the measurements of NO_x, NO₂ and O₃, respectively. The IA is a statistical measure of the correlation of the predicted and measured time series of concentrations. However, the modelling system overpredicts NO_x concentrations with a fractional bias FB=+13%, and O₃ concentrations with FB=+8%, while for NO₂ concentrations FB=−2%. We also analyzed the difference between model predictions and measured data in terms of meteorological parameters. Model performance clearly deteriorated as the wind direction approached a direction parallel to the road, and for the lowest wind speeds. The range of variability concerning atmospheric stability, ambient temperature and the amount of solar radiation was modest during the measurement campaign. As expected, no clear dependencies of model performance were therefore detected in terms of these parameters. The experimental dataset is available for the evaluation of other roadside dispersion models.     

Assessment of ensemble-based chemical data assimilation in an idealized setting

Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction (NWP). Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper, we assess the performance of the ensemble Kalman filter (EnKF). Results in an idealized setting show that EnKF is promising for chemical data assimilation.     

Which meteorological conditions produce worst-case odors from area sources?

Two competing meteorological factors influence atmospheric concentrations of pollutants from open liquid area sources such as wastewater treatment plant units: temperature and stability. High temperatures in summer produce greater emissions from liquid area sources because of increased compound volatility; however, these emissions tend to disperse more readily because of frequent occurrence of unstable conditions. An opposite scenario occurs in winter, with lesser emissions due to lower temperatures, but also frequently less dispersion, due to stable atmospheric conditions. The primary objective of this modeling study was thus to determine whether higher atmospheric concentrations from open liquid area sources occur more frequently in summer, when emissions are greater but so is dispersion, or in winter, when emissions are lesser but so is dispersion. The study utilized a rectangular clarifier emitting hydrogen sulfide as a sample open liquid area source. Dispersion modeling runs were conducted using ISCST3 and AERMOD, encompassing 5 yr of hourly meteorological data divided by season. Emission rates were varied hourly on the basis of a curve-fit developed from previously collected field data. Model output for each season was used to determine (1) maximum 2-min average concentrations, (2) the number of odor events (2-min average concentrations greater than odor detection thresholds), and (3) areas of impact. On the basis of these 3 types of output, it was found that the worst-case odors were associated with summer, considering impacts of meteorology upon both emissions and dispersion. Not accounting for the impact of meteorology on emissions (using a constant worst-case emission rate) caused concentrations to be overpredicted compared with a variable emission rate case. The highest concentrations occurred during stability classes D, E, and F, as anticipated. A comparison of ISCST3 and AERMOD found that for the area source modeled, ISCST3 predicted higher concentrations and more odor events for all seasons.     

Evaluating the use of outputs from comprehensive meteorological models in air quality modeling applications

Currently used dispersion models, such as the AMS/EPA Regulatory Model (AERMOD), process routinely available meteorological observations to construct model inputs. Thus, model estimates of concentrations depend on the availability and quality of meteorological observations, as well as the specification of surface characteristics at the observing site. We can be less reliant on these meteorological observations by using outputs from prognostic models, which are routinely run by the National Oceanic and Atmospheric Administration (NOAA). The forecast fields are available daily over a grid system that covers all of the United States. These model outputs can be readily accessed and used for dispersion applications to construct model inputs with little processing. This study examines the usefulness of these outputs through the relative performance of a dispersion model that has input requirements similar to those of AERMOD. The dispersion model was used to simulate observed tracer concentrations from a Tracer Field Study conducted in Wilmington, California in 2004 using four different sources of inputs: (1) onsite measurements; (2) National Weather Service measurements from a nearby airport; (3) readily available forecast model outputs from the Eta Model; and (4) readily available and more spatially resolved forecast model outputs from the MM5 prognostic model. The comparison of the results from these simulations indicate that comprehensive models, such as MM5 and Eta, have the potential of providing adequate meteorological inputs for currently used short-range dispersion models such as AERMOD.     

Biomagnetic monitoring of industry-derived particulate pollution

Clear association exists between ambient PM₁₀ concentrations and adverse health outcomes. However, determination of the strength of associations between exposure and illness is limited by low spatial-resolution of particulate concentration measurements. Conventional fixed monitoring stations provide high temporal-resolution data, but cannot capture fine-scale spatial variations. Here we examine the utility of biomagnetic monitoring for spatial mapping of PM₁₀ concentrations around a major industrial site. We combine leaf magnetic measurements with co-located PM₁₀ measurements to achieve inter-calibration. Comparison of the leaf-calculated and measured PM₁₀ concentrations with PM₁₀ predictions from a widely-used atmospheric dispersion model indicates that modelling of stack emissions alone substantially under-predicts ambient PM₁₀ concentrations in parts of the study area. Some of this discrepancy might be attributable to fugitive emissions from the industrial site. The composition of the magnetic particulates from vehicle and industry-derived sources differ, indicating the potential of magnetic techniques for source attribution.     

Influence of dense surface meteorological data assimilation on the prediction accuracy of ozone pollution in the southeastern coastal area of the Korean Peninsula

In order to clarify the influence of surface meteorological data assimilation with various resolutions on the photochemical ozone concentration in the southeastern Korean Peninsula, several numerical experiments were conducted. The meteorological and photochemical reaction models used in this study are the fifth-generation mesoscale model (MM5) and the three-dimensional photochemical urban airshed model (UAM-V), respectively. Dense meteorological data make it easier to obtain accurate estimates and surface characteristics than coarse-resolution data. As a result, the estimated temperature obtained from high resolution surface data assimilation in the Busan and Ulsan metropolitan areas is higher than that obtained from coarse resolution surface data assimilation. These high temperatures resulted in strong winds from the sea and a significant dispersion of ozone. In analyses involving an index of agreement (IOA) and root mean square deviation (RMSD), the temperature and wind speed estimated with dense surface data assimilation agreed well with those obtained from observations.However, the influence of dense surface data assimilation tends to be stronger in the flat Ulsan metropolitan area than in the mountainous Busan metropolitan area. This is caused by the heterogeneity of the surface characteristics, including the topography. If the surface parameters induced by regional circulation, such as the topography and land use, are complex and heterogeneous, the efficiency of observational data on data assimilation has to be verified before air pollution is assessed.     

Modeling Short Range Air Dispersion from Area Sources of Non-buoyant Toxics

A model based on K-theory has been developed for describing the short range air dispersion from area sources of non-buoyant toxics. Model parameter estimation is via boundary layer theory. Lateral dispersion by plume meander is considered but ail other sources of horizontal dispersion are neglected. The model can be applied on and near area sources and it can be adapted for predictions of downwind concentrations with a wide variety of meteorological Inputs.

The model has been evaluated by simulating the data obtained during atmospheric tracer studies and by comparison to vinyl chloride concentrations near the BKK landfill in southern California. The model appears to represent a useful and accurate tool for regulatory planning and risk assessment close to area sources of toxics.     

Validation of modeling approach to evaluate congener-specific concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans in air and soil near a solid waste incinerator.

A concise modeling approach using long-term averaged meteorological data was developed to estimate site-specific concentrations of congeners of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) near a solid waste incinerator. This approach consists of calculation of atmospheric dispersion, dry and wet deposition of gaseous and particle-bound congeners, and non-steady-state concentrations in soil. The predictability of this approach was evaluated by comparison of calculated concentrations of congeners in soil with those measured at eight locations near a municipal solid waste incinerator (MSWI). The variation of these concentrations due to variability of meteorological parameters is small. A considerable number of mean values show good agreement with measured concentrations within a factor of three. The reasonable agreement between calculated and measured concentrations indicates that algorithms for the calculation of vapor-phase deposition and non-steady-state concentrations in soil must be included in the modeling approach for an accurate estimation of the concentrations of congeners of PCDD/Fs emitted from MSWIs to the atmosphere. For a detailed estimation of site-specific concentrations, it is important to specify the bulk density of soil in the evaluated area, together with meteorological parameters.     

Analysis of air quality data near roadways using a dispersion model

We used a dispersion model to analyze measurements made during a field study conducted by the U.S. EPA in July–August 2006, to estimate the impact of traffic emissions on air quality at distances of tens of meters from an eight-lane highway located in Raleigh, NC. The air quality measurements consisted of long path optical measurements of NO at distances of 7 and 17 m from the edge of the highway. Sonic anemometers were used to measure wind speed and turbulent velocities at 6 and 20 m from the highway. Traffic flow rates were monitored using traffic surveillance cameras. The dispersion model [Venkatram, A., 2004. On estimating emissions through horizontal fluxes. Atmospheric Environment 38, 2439–2446] explained over 60% of the variance of the observed path averaged NO concentrations, and over 90% of the observed concentrations were within a factor of two of the model estimates.Sensitivity tests conducted with the model indicated that the traffic flow rate made the largest contribution to the variance of the observed NO concentrations. The meteorological variable that had the largest impact on the near road NO concentrations was the standard deviation of the vertical velocity fluctuations, σ_w. Wind speed had a relatively minor effect on concentrations. Furthermore, as long as the wind direction was within ±45° from the normal to the road, wind direction had little impact on near road concentrations. The measurements did not allow us to draw conclusions on the impact of traffic-induced turbulence on dispersion. The analysis of air quality and meteorological observations resulted in plausible estimates of on-road emission factors for NO.     

Global circulation of atmospheric mercury: a modelling study

This paper presents a comprehensive atmospheric global and regional mercury model and its capability in describing the atmospheric cycling of mercury. This is an on-line model (integrated within the Canadian operational environmental forecasting and data assimilation system) which can be used to understand the role of meteorology in mercury cycling (atmospheric pathways), the inter-annual variability of mercury and can be evaluated against observations on global scales. This is due to the fact that the model uses a combination of actual observed and predicted meteorological state of the atmosphere at high resolution to integrate the model as opposed to the climatological approach used in existing global mercury models. The model was integrated and evaluated on global scale using only anthropogenic emissions. North to south gradients in mercury concentrations, seasonal variability, dry and wet deposition and vertical structure are well simulated by the model. The model was used to explain the observed seasonal variations in atmospheric mercury circulation. The results from this study include a global animation of surface air concentrations of total gaseous mercury for 1997.     

Data assimilation in the atmospheric dispersion model for nuclear accident assessments

Uncertainty factors in atmospheric dispersion models may influence the reliability of model prediction. The ability of a model in assimilating measurement data will be helpful to improve model prediction. In this paper, data assimilation based on ensemble Kalman filter (EnKF) is introduced to a Monte Carlo atmospheric dispersion model (MCADM) designed for assessment of consequences after an accident release of radionuclides. Twin experiment has been performed in which simulated ground-level dose rates have been assimilated. Uncertainties in the source term and turbulence intensity of wind field are considered, respectively. Methodologies and preliminary results of the application are described. It is shown that it is possible to reduce the discrepancy between the model forecast and the true situation by data assimilation. About 80% of error caused by the uncertainty in the source term is reduced, and the value for that caused by uncertainty in the turbulence intensity is about 50%.     

A Multivariable Model for Atmospheric Dispersion Predictions

In conjunction with a 15-month air quality survey of Jacksonville, Fia., a mathematical model has been developed to describe the dispersion of atmospheric pollutants. The source inventory used with the model was compiled, in part, from the data obtained from the sampling of all major sources within the area. The major sources were considered separately from the one-mile square area sources which accounted for the remainder of the emissions. Meteorological data was recorded continuously in the city including vertical temperature observations to 750 ft. The model was compiled in FORTRAN and can be used for both gaseous and particulate pollutants, by utilizing proper decay rates. The variant nature of meteorological parameters and emission rates are considered. The ground level concentrations of several pollutants which were determined for 24 hr periods at 11 sites and continuously at two other sites were used to check the model. A limited tracer study was carried out in conjunction with the project.