Testing the importance of accurate meteorological input fields and parameterizations in atmospheric transport modelling using dream - validation against ETEX-1

A tracer model, the DREAM, which is based on a combination of a near-range Lagrangian model and a long-range Eulerian model, has been developed. The meteorological meso-scale model, MM5V1, is implemented as a meteorological driver for the tracer model. The model system is used for studying transport and dispersion of air pollutants caused by a single but strong source as, e.g. an accidental release from a nuclear power plant. The model system including the coupling of the Lagrangian model with the Eulerian model are described. Various simple and comprehensive parameterizations of the mixing height, the vertical dispersion, and different meterological input data have been implemented in the combined tracer model, and the model results have been validated against measurements from the ETEX-1 release. Several different statistical parameters have been used to estimate the differences between the parameterizations and meterological input data in order to find the best performing solution.     

Multi-model vs. EPS-based ensemble atmospheric dispersion simulations: A quantitative assessment on the ETEX-1 tracer experiment case

Several techniques have been developed over the last decade for the ensemble treatment of atmospheric dispersion model predictions. Among them two have received most of the attention, the multi-model and the ensemble prediction system (EPS) modeling. The multi-model approach relies on model simulations produced by different atmospheric dispersion models using meteorological data from potentially different weather prediction systems. The EPS-based ensemble is generated by running a single atmospheric dispersion model with the ensemble weather prediction members. In the paper we compare both approaches with the help of statistical indicators, using the simulations performed for the ETEX-1 tracer experiment. Both ensembles are also evaluated against measurement data. Among the most relevant results is that the multi-model median and the mean of EPS-based ensemble produced the best results, hence we consider a combination of multi-model and EPS-based approaches as an interesting suggestion for further research.     

Estimating micrometeorological inputs for modeling dispersion in urban areas during stable conditions

We examine the performance of three methods to estimate the surface friction velocity and the Monin–Obukhov (MO) length in stable conditions. Estimates from these methods are compared with measurements made at two urban sites: the Wilmington site located in the middle of an urban area, and the VTMX site located on a sloping, smooth area in Salt Lake City. The first method uses the mean wind at a single height (Single U or SU), the second uses the wind speed at a single level and the temperature difference between two levels (U delta T or UDT), and the third method uses two levels of wind speed and temperature (delta U delta T or DUDT). The performance of the SU and UDT methods in estimating u_* are comparable. The SU method yields better estimates of the MO length than the UDT method does. The DUDT method performs poorly in estimating both u_* and L. The major conclusions of this study are that (1) measurements of mean winds and temperatures at one or two levels at an urban location can provide adequate estimates of micrometeorological variables required in modeling dispersion in the stable boundary layer, and (2) methods based on using differences in temperatures and velocities between two levels can provide unreliable estimates of these variables because these differences can be overwhelmed by inevitable uncertainties in the measurement of mean variables.     

Data assimilation in meteorological pre-processors: Effects on atmospheric dispersion simulations

In previous work [Kovalets, I., Andronopoulos, S., Bartzis, J.G., Gounaris, N., Kushchan, A., 2004. Introduction of data assimilation procedures in the meteorological pre-processor of atmospheric dispersion models used in emergency response systems. Atmospheric Environment 38, 457–467.] the authors have developed data assimilation (DA) procedures and implemented them in the frames of a diagnostic meteorological pre-processor (MPP) to enable simultaneous use of meteorological measurements with numerical weather prediction (NWP) data. The DA techniques were directly validated showing a clear improvement of the MPP output quality in comparison with meteorological measurement data. In the current paper it is demonstrated that the application of DA procedures in the MPP, to combine meteorological measurements with NWP data, has a noticeable positive effect on the performance of an atmospheric dispersion model (ADM) driven by the MPP output. This result is particularly important for emergency response systems used for accidental releases of pollutants, because it provides the possibility to combine meteorological measurements with NWP data in order to achieve more reliable dispersion predictions. This is also an indirect way to validate the DA procedures applied in the MPP. The above goal is achieved by applying the Lagrangian ADM DIPCOT driven by meteorological data calculated by the MPP code both with and without the use of DA procedures to simulate the first European tracer experiment (ETEX I). The performance of the ADM in each case was evaluated by comparing the predicted and the experimental concentrations with the use of statistical indices and concentration plots. The comparison of resulting concentrations using the different sets of meteorological data showed that the activation of DA in the MPP code clearly improves the performance of dispersion calculations in terms of plume shape and dimensions, location of maximum concentrations, statistical indices and time variation of concentration at the detectors locations.     

Pore-scale modeling of dispersion in disordered porous media

We employ a direct pore-level model of incompressible flow that uses the modified moving particle semi-implicit (MMPS) method. The model is capable of simulating both unsteady- and steady-state flow directly in microtomography images of naturally-occurring porous media. We further develop this model to simulate solute transport in disordered porous media. The governing equations of flow and transport at the pore level, i.e., Navier-Stokes and convection-diffusion, are solved directly in the pore space mapped by microtomography techniques. Three naturally-occurring sandstones are studied in this work. We verify the accuracy of the model by comparing the computed longitudinal dispersion coefficients against the experimental data for a wide range of Peclet numbers, i.e., 5×10(-2)相似文献   

Sensitivity of the DERMA long-range Gaussian dispersion model to meteorological input and diffusion parameters

The Danish Emergency Response Model of the Atmosphere (DERMA) is described and applied to the first ETEX experiment. By using analysed low-resolution numerical weather-prediction data from the global model of the European Centre for Medium-range Weather Forecast (ECMWF) as well as higher-resolution data from two versions of the High Resolution Limited Area Model (HIRLAM), which are operational at the Danish Meteorological Institute (DMI), the sensitivity of DERMA to the resolution of meteorological data is analysed by comparing DERMA results with concentration measurements. Furthermore, the sensitivity to boundary-layer height and diffusion parameters is studied. These parameters include the critical bulk Richardson number, which is used to estimate the atmospheric boundary-layer height, the horizontal eddy diffusivity and the Lagrangian turbulence time scale. The parameters, which provide the best performance of DERMA, are 0.25 for the critical bulk Richardson number, 6×10³ m² s^-1 for the horizontal eddy diffusivity, and 3 h for the Lagrangian time scale. DERMA is much more sensitive to boundary-layer parameters when using high-resolution DMI-HIRLAM data than when using data of lower resolution from the ECMWF. Finally, the bulk Richardson number method of boundary-layer height calculation applied to DMI-HIRLAM data is verified directly against routine radiosondes released under the tracer gas plume. The boundary-layer height estimates based on analysed NWP model data agree well with observations, and the agreement deteriorates as a function of forecast length.     

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.     

Evaluation of the effect of meteorological data resolution on Lagrangian particle dispersion simulations using the ETEX experiment

This paper presents results from a series of numerical experiments designed to evaluate operational long-range dispersion model simulations, and to investigate the effect of different temporal and spatial resolution of meteorological data from numerical weather prediction models on these simulations. Results of Lagrangian particle dispersion simulations of the first tracer release of the European Tracer Experiment (ETEX) are presented and compared with measured tracer concentrations. The use of analyzed data of higher resolution from the European Center for Medium-Range Weather Forecasts (ECMWF) model produced significantly better agreement between the concentrations predicted with the dispersion model and the ETEX measurements than the use of lower resolution Navy Operational Global Atmospheric Prediction System (NOGAPS) forecast data. Numerical experiments were performed in which the ECMWF model data with lower vertical resolution (4 instead of 7 levels below 500 mb), lower temporal resolution (12 h instead of 6 h intervals), and lower horizontal resolution (2.5° instead of 0.5°) were used. Degrading the horizontal or temporal resolution of the ECMWF data resulted in decreased accuracy of the dispersion simulations. These results indicate that flow features resolved by the numerical weather prediction model data at approximately 45 km horizontal grid spacing and 6 h time intervals, but not resolved at 225 km spacing and 12 h intervals, made an important contribution to the long-range dispersion.     

Using prognostic model-generated meteorological output in the AERMOD dispersion model: an illustrative application in Philadelphia, PA

In this study, we introduce the prospect of using prognostic model-generated meteorological output as input to steady-state dispersion models by identifying possible advantages and disadvantages and by presenting a comparative analysis. Because output from prognostic meteorological models is now routinely available and is used for Eulerian and Lagrangian air quality modeling applications, we explore the possibility of using such data in lieu of traditional National Weather Service (NWS) data for dispersion models. We apply these data in an urban application where comparisons can be made between the two meteorological input data types. Using the U.S. Environment Protection Agency's American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD) air quality dispersion model, hourly and annual average concentrations of benzene are estimated for the Philadelphia, PA, area using both hourly MM5 model-generated meteorological output and meteorological data taken from the NWS site at the Philadelphia International Airport. Our intent is to stimulate a discussion of the relevant issues and inspire future work that examines many of the questions raised in this paper.     

Application of dispersion modeling to indoor gas release scenarios

Many complex models are available to study the dispersion of contaminants or ventilation effectiveness in indoor spaces. Because of the computationally complex numerical schemes employed, most of these models require mainframe computers or workstations. However, simple design tools or guidelines are needed, in addition to complicated models. A dispersion model based on the basic governing equations was developed and uses an analytical solution. Because the concentration is expressed by an analytical solution, the grid size and time steps are user definable. A computer program was used to obtain numerical results and to obtain release history from a thermodynamic source model. The model can be used to estimate three-dimensional spatial and temporal variations in concentrations resulting from transient gas releases in an enclosure. The model was used to study a gas release scenario from a pressurized cylinder into a large ventilated building, in this case, a transit parking and fueling facility.     

Lagrangian dispersion modeling of vehicular emissions from a highway in complex terrain

Transit traffic through the Austrian Alps is of major concern in government policy. Pollutant burdens resulting from such traffic are discussed widely in Austrian politics and have already led to measures to restrict traffic on transit routes. In the course of an environmental assessment study, comprehensive measurements were performed. These included air quality observations using passive samplers, a differential optical absorption spectroscopy system, a mobile and a fixed air quality monitoring station, and meteorological observations. As was evident from several previous studies, dispersion modeling in such areas of complex terrain and, moreover, with frequent calm wind conditions, is difficult to handle. Further, in the case presented here, different pollutant sources had to be treated simultaneously (e.g., road networks, exhaust chimneys from road tunnels, and road tunnel portals). No appropriate system for modeling all these factors has so far appeared in the literature. A prognostic wind field model coupled with a Lagrangian dispersion model is thus presented here and is designed to treat all these factors. A comparison of the modeling system with results from passive samplers and from a fixed air quality monitoring station proved the ability of the model to provide reasonable figures for concentration distributions along the A10.     

Particulate matter emission rates from beef cattle feedlots in Kansas-reverse dispersion modeling

Open beef cattle feedlots emit various air pollutants, including particulate matter (PM) with equivalent aerodynamic diameter of 10 microm or less (PM10); however limited research has quantified PM10 emission rates from feedlots. This research was conducted to determine emission rates of PM10 from large cattle feedlots in Kansas. Concentrations of PM10 at the downwind and upwind edges of two large cattle feedlots (KS1 and KS2) in Kansas were measured with tapered element oscillating microbalance (TEOM) PM10 monitors from January 2007 to December 2008. Weather conditions at the feedlots were also monitored. From measured PM10 concentrations and weather conditions, PM10 emission rates were determined using reverse modeling with the American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model (AERMOD). The two feedlots differed significantly in median PM10 emission flux (1.60 g/m2-day for KS1 vs. 1.10 g/m2-day for KS2) but not in PM10 emission factor (27 kg/1000 head-day for KS1 and 30 kg/1000 head-day KS2). These emission factors were smaller than published U.S. Environmental Protection Agency (EPA) emission factor for cattle feedlots.     

Use of the Kit Fox field data to analyze dense gas dispersion modeling issues

The 1995 Kit Fox dense gas field data set consists of 52 trials where short-duration CO₂ gas releases were made at ground level over a rough surface during neutral to stable conditions. The experiments were intended to demonstrate the effects on dense gas clouds of relatively large roughnesses typical of industrial process plants. Fast response concentration observations were made by 80 samplers located on four downwind lines (25, 50, 100, and 225 m), including profile observations on three towers on each of the closest three arcs. Detailed meteorological measurements were made on several towers within and outside of the roughness arrays. The data analysis emphasized the variation of maximum concentration with surface roughness, the dependence of cloud advection speed on cloud depth, the variation of the three components of dispersion with ambient turbulence, and the dependence of vertical entrainment rate on ambient friction velocity and cloud Richardson number. The Kit Fox data were used to evaluate a specific dense gas dispersion model (HEGADAS 3+), with emphasis on whether it would be able to account for the increased roughness. The model was able to satisfactorily simulate the observed concentrations, with a mean bias of about 5% and with about 90% of the predictions within a factor of two of the observations.     

Wind direction bias in generating wind roses and conducting sector-based air dispersion modeling

Certain widely used wind rose programs and air dispersion models use an overly simple data-transfer algorithm that induces a directional bias in their output products. The purpose of this paper is to provide a revised algorithm that corrects the directional bias that occurs from the aliasing that occurs when the sector widths used to report wind direction data are on the same order of magnitude, but not equal, to the sector widths used in the wind direction summaries. The directional bias issue arises when output products in 16 direction sectors (22.5 degrees each) are produced from wind direction data reported in terms of 36 sectors (10 degrees each). The result directional bias affects the results of simulations of air and surface concentrations using widely applied air dispersion models. Datasets or models with the directional bias discussed here give consistent positive biases (approximately 30%) for cardinal direction sectors (north, south, east, and west) and consistent negative biases for all of the other sectors (around -10%). Data summary and air dispersion programs providing outputs in direction sectors that do not match the observational sectors need to be checked for this bias. A revised data-transfer algorithm is provided that corrects the directional bias that can occur in transferring wind direction data between different sector widths.     

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.     

Measurement and three-dimensional modeling of airflow and pollutant dispersion in an undersea traffic tunnel

Airflow and pollutant dispersion in a cross-harbor traffic tunnel were experimentally and numerically studied. Concentrations of the gaseous pollutants CO, NOx, and total hydrocarbons (THC) at three axial locations in the tunnel, together with traffic flow rate, traffic speed, and types of vehicle were measured. Three-dimensional (3D) turbulent flow and dispersion of air pollutants in the tunnel were modeled and solved numerically using the finite volume method. Traffic emissions were modeled accordingly as banded line sources along the tunnel floor. The results reveal that cross-sectional concentrations are nonuniformly distributed and that concentrations rise with downstream distance. The piston effect of vehicles alone can provide 9-23% dilution of air pollutants in the tunnel, compounded to a 23-74% dilution effect according to the ventilation condition.     

Some fluid modeling studies of flow and dispersion over two-dimensional low hills

The results of wind tunnel experiments on flow and dispersion over two-dimensional low and ‘gentle’ hills of different shapes and aspect ratios are discussed. The hill-induced influences on velocity and concentration fields are studied as functions of the hills‘ aspect ratio (the ratio of the half-length to the height of the hill).The speed-up of flow on the hilltop is shown to have an inverse relationship with the aspect ratio ‘a’, as predicted by several theories. However, this does not increase indefinitely as a → 0, but has a maximum value at some optimum aspect ratio greater than unity. The velocity variances and Reynolds stress increase rapidly in the near wake region in the lee of the hills. In the far wake region, beyond about five hill heights, the hill-induced perturbations in mean velocity, Reynolds stress and variances of velocity decay in inverse proportion to the distance behind the hill.Dispersion from elevated sources located on the top of ‘gentle’ hills is not significantly affected by the hills. For low-level sources on the top of steeper hills, where flow separation may occur, the ground level concentrations are reduced (by as much as a factor of 3). On the other hand, the ground level concentrations from sources on the lee side of the hills are considerably enhanced (by as much as a factor of 15) near the source, but slightly reduced far downwind. The influence of‘gentle’ hills bears an inverse relationship with the hills' aspect ratio. For steep hills, however, the hill influence is intimately related to the dimensions of the cavity region.     

The tidal hydrodynamics modeling of the Topolobampo coastal lagoon system and the implications for pollutant dispersion

The tidal hydrodynamics of the Topolobampo coastal lagoon system (Mexico) has been investigated through a modified two dimensional non-linear hydrodynamic finite difference model. The advective and diffusive process acting over a hypothetical pollutant released into the coastal lagoon have also been simulated. Maxima tidal currents (0.85 m/s) were predicted within the main channel, in agree with direct measurements. The direction of the observed fastest currents (SW), also agree quite well with the direction of the strongest tidal current predicted in this investigation, which occur during the ebb when the water of the coastal lagoon is discharged into the Gulf of California. Residual currents (0.01-0.05 m/s) were also predicted. The hypothetical pollutant released within the Topolobampo Harbor would spread to both Ohuira and Topolobampo sections, reaching the inlet after approximately 12 days.     

Effects of the density of meteorological observations on the diagnostic wind fields and the performance of photochemical modeling in the greater Seoul area

The high ozone episode in the greater Seoul area (GSA) for the period of 27 July–1 August 1997 was modeled by the California Institute of Technology (CIT) three-dimensional photochemical model. During the period, ozone concentrations around 140 ppb were observed for 2 days. Two sets of diagnostic wind fields were constructed by using observations from the weather stations operated by the Korea Meteorological Administration. One set of wind fields utilized only observations from the surface weather stations (SWS) and the other set also utilized observations from the automatic weather stations (AWS) that were more densely distributed than the SWS. The results showed that utilizing observations from the AWS could represent fine variations in the wind field such as those caused by topography. A better wind field gave a more reasonable spatial distribution of ozone concentrations. The model performance of ozone prediction was also improved to some extent, but only marginally acceptable owing to large day-to-day variations. Overshoots of primary pollutants particularly for NO₂ were observed as pollutants were accumulated where low wind speeds were maintained. More precise information on diurnal and daily variations in emissions was warranted in order to better model the photochemical phenomena over the GSA.