scholarly journals Diagnosing the Surface Layer Parameters for Dispersion Models within the Meteorological-to-Dispersion Modeling Interface

2010 ◽  
Vol 49 (2) ◽  
pp. 221-233 ◽  
Author(s):  
M. Sofiev ◽  
E. Genikhovich ◽  
P. Keronen ◽  
T. Vesala
Keyword(s):  
Surface Layer ◽  
Weather Forecasting ◽  
Dispersion Model ◽  
Temporal Correlation ◽  
Complex Surface ◽  
Dispersion Modeling ◽  
Limited Area ◽  
Dispersion Models ◽  
Absolute Flux ◽  
The Absolute

Abstract The problem of providing dispersion models with meteorological information from general atmospheric models used, for example, for weather forecasting is considered. As part of a generalized meteorological-to-dispersion model interface, a noniterative scheme diagnosing the surface layer characteristics from wind, temperature, and humidity profiles was developed. The scheme verification included long-term comparison with data of meteorological masts at Cabauw, the Netherlands, and Hyytiälä, Finland. The algorithm compatibility and consistency with the High-Resolution Limited-Area Model (HIRLAM) was also checked, as this model is routinely used as a meteorological driver for the Air Quality and Emergency Modeling System (SILAM). The comparison with Cabauw mast data showed a good quantitative agreement between observed and diagnosed heat and momentum fluxes: the temporal correlation coefficient was ∼0.8, bias was less than 10% of the absolute flux levels, regression slope deviated from unity for less than 20% with the intercept being less than 10% of the absolute flux values, and so on. In the case of complex surface features (Hyytiälä mast in forest) the scheme proved to be robust with large deviations appearing only if the input profile data were taken outside the constant-flux layer. Comparison with the HIRLAM model showed qualitatively good agreement but also highlighted several differences between the goals, standards, and methodologies of meteorological and dispersion models. The scheme was implemented in SILAM, which served as the development platform.

scholarly journals GEOSTATISTICS FOR AIR QUALITY MAPPING: CASE OF BAGUIO CITY, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 353-359
Author(s):  
R. V. Ramos ◽  
A. C. Blanco
Keyword(s):  
Air Quality ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Spatial Variations ◽  
Dispersion Modeling ◽  
Dispersion Models ◽  
Night Time ◽  
Smoothing Factor ◽  
Pollutant Concentrations ◽  
Atmospheric Dispersion Models

Abstract. Mapping of air quality are often based on ground measurements using gravimetric and air portable sensors, remote sensing methods and atmospheric dispersion models. In this study, Geographic Information Systems (GIS) and geostatistical techniques are employed to evaluate coarse particulate matter (PM10) concentrations observed in the Central Business District of Baguio City, Philippines. Baguio City has been reported as one of the most polluted cities in the country and several studies have already been conducted in monitoring its air quality. The datasets utilized in this study are based on hourly simulations from a Gaussian-based atmospheric dispersion model that considers the impacts of vehicular emissions. Dispersion modeling results, i.e., PM10 concentrations at 20-meter interval, show that high values range from 135 to 422 μg/mm3. The pollutant concentrations are evident within 40 meters from the roads. Spatial variations and PM10 estimates at unsampled locations are determined using Ordinary Kriging. Geostatistical modeling estimates are evaluated based on recommended values for mean error (ME), root mean square error (RMSE) and standardized errors. Optimal predictors for pollutant concentrations at 5-meter interval include 2 to 5 search neighbors and variable smoothing factor for night-time datasets while 2 to 10 search neighbors and smoothing factors 0.3 to 0.5 were used for daytime datasets. Results from several interpolation tests indicate small ME (0.0003 to 0.0008 μg/m3) and average standardized errors (4.24 to 8.67 μg/m3). RMSE ranged from 2.95 to 5.43 μg/m3, which are approximately 2 to 3% of the maximum pollutant concentrations in the area. The methodology presented in this paper may be integrated with atmospheric dispersion models in refining estimates of pollutant concentrations, in generating surface representations, and in understanding the spatial variations of the outputs from the model simulations.

scholarly journals Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection

2018 ◽  
Vol 15 (8) ◽  
pp. 1640 ◽  
Author(s):  
Zhengqiu Zhu ◽  
Sihang Qiu ◽  
Bin Chen ◽  
Rongxiao Wang ◽  
Xiaogang Qiu
Keyword(s):  
Particle Filtering ◽  
Error Propagation ◽  
Dispersion Model ◽  
Data Driven ◽  
Dispersion Modeling ◽  
Dispersion Models ◽  
Concentration Data ◽  
Gas Dispersion ◽  
Computational Errors ◽  
Hazardous Gas

The accurate prediction of hazardous gas dispersion process is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in a chemical cluster. Conventional Gaussian-based dispersion models can seldom give accurate predictions due to inaccurate input parameters and the computational errors. In order to improve the prediction accuracy of a dispersion model, a data-driven air dispersion modeling method based on data assimilation is proposed by applying particle filter to Gaussian-based dispersion model. The core of the method is continually updating dispersion coefficients by assimilating observed data into the model during the calculation process. Another contribution of this paper is that error propagation detection rules are proposed to evaluate their effects since the measured and computational errors are inevitable. So environmental protection authorities can be informed to what extent the model output is of high confidence. To test the feasibility of our method, a numerical experiment utilizing the SF6 concentration data sampled from an Indianapolis field study is conducted. Results of accuracy analysis and error inspection imply that Gaussian dispersion models based on particle filtering and error propagation detection have better performance than traditional dispersion models in practice though sacrificing some computational efficiency.

Toxic gas dispersion models: Can they predict protective action distances in case of a chemical spill?

2008 ◽  
Vol 6 (5) ◽  
pp. 23
Author(s):  
John S. Nordin, PhD
Keyword(s):  
Dispersion Model ◽  
Protective Action ◽  
Weather Conditions ◽  
Dispersion Modeling ◽  
Dispersion Models ◽  
Gas Dispersion ◽  
The Public ◽  
Emergency Responders ◽  
Toxic Concentration ◽  
Stable Conditions

Emergency responders often use a gas dispersion model to estimate downwind airborne concentrations of a toxic chemical in case of a chemical spill accident. For protecting the public, a protective action distance from the spill source is established based on the distance where the toxic concentration drops below some level of concern. This distance is used as a basis for evacuation of the public from the area or for instructions to shelter-in-place. However, in real-world accidents, the responders neither know the amount of chemicals released into the air nor the duration of the release, and moreover, the concentrations of chemicals at any location will vary over time. Depending on what input information is put into the model, different results will be obtained. The problem of what input parameters to use for gas dispersion modeling is illustrated for a hypothetical 90-ton chlorine railcar accident, where the railcar is breached. Different answers for a protective action distance are obtained depending on whether the tables in the Emergency Response Guidebook or any of the popular gas dispersion models are used. Very different answers are obtained from any model depending on whether whole of the chemical is released at once as a gas or aerosol or whether the liquefied chlorine evaporates slowly inside a ruptured 90-ton railcar tank, and also the weather conditions. To avoid misunderstandings, people who use models to establish a protective action distance must also communicate the circumstances in which the models are used, eg, “worst possible what-if scenario,” etc, or “nighttime stable conditions,” or other situations.

scholarly journals Atmospheric CO<sub>2</sub> observations and models suggest strong carbon uptake by forests in New Zealand

2016 ◽  
Author(s):  
K. Steinkamp ◽  
S. E. Mikaloff Fletcher ◽  
G. Brailsford ◽  
D. Smale ◽  
S. Moore ◽  
...  
Keyword(s):  
New Zealand ◽  
Process Model ◽  
Dispersion Model ◽  
Weather Prediction ◽  
Atmospheric Dispersion ◽  
Temporal Distribution ◽  
Inversion Method ◽  
Back Trajectory ◽  
Dispersion Modeling ◽  
Limited Area

Abstract. A regional atmospheric inversion method has been developed to determine the spatial and temporal distribution of CO2 sinks and sources across New Zealand for 2011–2013. This approach infers air-sea and air-land CO2 fluxes from measurement records, using back-trajectory simulations from the Numerical Atmospheric dispersion Modeling Environment (NAME) Lagrangian dispersion model, driven by meteorology from the New Zealand Limited Area Model (NZLAM) weather prediction model. The inversion uses in situ measurements from two fixed sites, Baring Head on the southern tip of New Zealand’s North Island (41.408°S, 174.871°E) and Lauder from the central South Island (45.038°S, 169.684°E), and ship board data from monthly cruises between Japan, New Zealand and Australia. A range of scenarios is used to assess the sensitivity of the inversion method to underlying assumptions, and to ensure robustness of the results. The results indicate a strong seasonal cycle in terrestrial land fluxes from the South Island of New Zealand, especially in western regions covered by indigenous forest, suggesting higher photosynthetic and respiratory activity than is evident in the current a priori land process model. On the annual scale, the terrestrial biosphere in New Zealand is estimated to be a net CO2 sink, removing 98 (±37) Tg CO2 yr−1 from the atmosphere on average during 2011–2013. This sink is much larger than the reported 27 Tg CO2 yr−1 from the national inventory for the same time period. The difference can be partially reconciled when factors related to forest and agricultural management and exports, fossil fuel emission estimates, hydrologic fluxes, and soil carbon change are considered, but some differences are likely to remain.

scholarly journals Development of the city-scale chemistry transport model CityChem-EPISODE and its application to the city of Hamburg

2018 ◽  
Author(s):  
Matthias Karl
Keyword(s):  
Air Quality ◽  
Urban Area ◽  
Transport Model ◽  
Dispersion Model ◽  
Temporal Correlation ◽  
Urban Atmosphere ◽  
Aerosol Formation ◽  
Urban Background ◽  
New Model ◽  
The City

Abstract. This paper describes the City-scale Chemistry (CityChem) extension of the urban dispersion model EPISODE with the aim to enable chemistry/transport simulations of multiple reactive pollutants on urban scales. The new model is called CityChem-EPISODE. The primary focus is on the simulation of urban ozone concentrations. Ozone is produced in photochemical reaction cycles involving nitrogen oxides (NOx) and volatile organic compounds (VOC) emitted by various anthropogenic activities in the urban area. The performance of the new model was evaluated with a series of synthetic tests and with a first application to the air quality situation in the city of Hamburg, Germany. The model performs fairly well for ozone in terms of temporal correlation and bias at the air quality monitoring stations in Hamburg. In summer afternoons, when photochemical activity is highest, modelled median ozone at an inner-city urban background station was about 30 % lower than the observed median ozone. Inaccuracy of the computed photolysis frequency of nitrogen dioxide (NO2) is the most probable explanation for this. CityChem-EPISODE reproduces the spatial variation of annual mean NO2 concentrations between urban background, traffic and industrial stations. However, the temporal correlation between modelled and observed hourly NO2 concentrations is weak for some of the stations. For daily mean PM10, the performance of CityChem-EPISODE is moderate due to low temporal correlation. The low correlation is linked to uncertainties in the seasonal cycle of the anthropogenic particulate matter (PM) emissions within the urban area. Missing emissions from domestic heating might be an explanation for the too low modelled PM10 in winter months. Four areas of need for improvement have been identified: (1) dry and wet deposition fluxes; (2) treatment of photochemistry in the urban atmosphere; (3) formation of secondary inorganic aerosol (SIA); and (4) formation of biogenic and anthropogenic secondary organic aerosol (SOA). The inclusion of secondary aerosol formation will allow for a better sectorial attribution of observed PM levels. Envisaged applications of the CityChem-EPISODE model are urban air quality studies, environmental impact assessment, sensitivity analysis of sector-specific emission and the assessment of local and regional emission abatement policy options.

scholarly journals Determination of time- and height-resolved volcanic ash emissions and their use for quantitative ash dispersion modeling: the 2010 Eyjafjallajökull eruption

2011 ◽  
Vol 11 (9) ◽  
pp. 4333-4351 ◽  
Author(s):  
A. Stohl ◽  
A. J. Prata ◽  
S. Eckhardt ◽  
L. Clarisse ◽  
A. Durant ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
A Priori ◽  
Volcanic Eruptions ◽  
General Nature ◽  
Aviation Industry ◽  
Dispersion Modeling ◽  
Source Information ◽  
European Area ◽  
Ash Dispersion

Abstract. The April–May, 2010 volcanic eruptions of Eyjafjallajökull, Iceland caused significant economic and social disruption in Europe whilst state of the art measurements and ash dispersion forecasts were heavily criticized by the aviation industry. Here we demonstrate for the first time that large improvements can be made in quantitative predictions of the fate of volcanic ash emissions, by using an inversion scheme that couples a priori source information and the output of a Lagrangian dispersion model with satellite data to estimate the volcanic ash source strength as a function of altitude and time. From the inversion, we obtain a total fine ash emission of the eruption of 8.3 ± 4.2 Tg for particles in the size range of 2.8–28 μm diameter. We evaluate the results of our model results with a posteriori ash emissions using independent ground-based, airborne and space-borne measurements both in case studies and statistically. Subsequently, we estimate the area over Europe affected by volcanic ash above certain concentration thresholds relevant for the aviation industry. We find that during three episodes in April and May, volcanic ash concentrations at some altitude in the atmosphere exceeded the limits for the "Normal" flying zone in up to 14 % (6–16 %), 2 % (1–3 %) and 7 % (4–11 %), respectively, of the European area. For a limit of 2 mg m−3 only two episodes with fractions of 1.5 % (0.2–2.8 %) and 0.9 % (0.1–1.6 %) occurred, while the current "No-Fly" zone criterion of 4 mg m−3 was rarely exceeded. Our results have important ramifications for determining air space closures and for real-time quantitative estimations of ash concentrations. Furthermore, the general nature of our method yields better constraints on the distribution and fate of volcanic ash in the Earth system.

scholarly journals A Scale-Selective Data Assimilation Approach to Improving Tropical Cyclone Track and Intensity Forecasts in a Limited-Area Model: A Case Study of Hurricane Felix (2007)

2012 ◽  
Vol 27 (1) ◽  
pp. 124-140 ◽  
Author(s):  
Bin Liu ◽  
Lian Xie
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Dynamical Downscaling ◽  
Weather Forecasting ◽  
Forecast Error ◽  
Regional Model ◽  
Small Scale ◽  
Limited Area ◽  
Track Forecast ◽  
Global Models

Abstract Accurately forecasting a tropical cyclone’s (TC) track and intensity remains one of the top priorities in weather forecasting. A dynamical downscaling approach based on the scale-selective data assimilation (SSDA) method is applied to demonstrate its effectiveness in TC track and intensity forecasting. The SSDA approach retains the merits of global models in representing large-scale environmental flows and regional models in describing small-scale characteristics. The regional model is driven from the model domain interior by assimilating large-scale flows from global models, as well as from the model lateral boundaries by the conventional sponge zone relaxation. By using Hurricane Felix (2007) as a demonstration case, it is shown that, by assimilating large-scale flows from the Global Forecast System (GFS) forecasts into the regional model, the SSDA experiments perform better than both the original GFS forecasts and the control experiments, in which the regional model is only driven by lateral boundary conditions. The overall mean track forecast error for the SSDA experiments is reduced by over 40% relative to the control experiments, and by about 30% relative to the GFS forecasts, respectively. In terms of TC intensity, benefiting from higher grid resolution that better represents regional and small-scale processes, both the control and SSDA runs outperform the GFS forecasts. The SSDA runs show approximately 14% less overall mean intensity forecast error than do the control runs. It should be noted that, for the Felix case, the advantage of SSDA becomes more evident for forecasts with a lead time longer than 48 h.

scholarly journals Estimating Daytime Planetary Boundary Layer Heights over a Valley from Rawinsonde Observations at a Nearby Airport: An Application to the Page Valley in Virginia, United States

2016 ◽  
Vol 55 (3) ◽  
pp. 791-809 ◽  
Author(s):  
Temple R. Lee ◽  
Stephan F. J. De Wekker
Keyword(s):  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Weather Forecasting ◽  
Wrf Model ◽  
Heat Fluxes ◽  
Weather Research And Forecasting ◽  
Dispersion Modeling ◽  
Blue Ridge Mountains ◽  
Terrain Following ◽  
Regional Reanalysis

AbstractThe planetary boundary layer (PBL) height is an essential parameter required for many applications, including weather forecasting and dispersion modeling for air quality. Estimates of PBL height are not easily available and often come from twice-daily rawinsonde observations at airports, typically at 0000 and 1200 UTC. Questions often arise regarding the applicability of PBL heights retrieved from these twice-daily observations to surrounding locations. Obtaining this information requires knowledge of the spatial variability of PBL heights. This knowledge is particularly limited in regions with mountainous terrain. The goal of this study is to develop a method for estimating daytime PBL heights in the Page Valley, located in the Blue Ridge Mountains of Virginia. The approach includes using 1) rawinsonde observations from the nearest sounding station [Dulles Airport (IAD)], which is located 90 km northeast of the Page Valley, 2) North American Regional Reanalysis (NARR) output, and 3) simulations with the Weather Research and Forecasting (WRF) Model. When selecting days on which PBL heights from NARR compare well to PBL heights determined from the IAD soundings, it is found that PBL heights are higher (on the order of 200–400 m) over the Page Valley than at IAD and that these differences are typically larger in summer than in winter. WRF simulations indicate that larger sensible heat fluxes and terrain-following characteristics of PBL height both contribute to PBL heights being higher over the Page Valley than at IAD.

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