scholarly journals Meteorological data recommendations for input to dispersion models applied to Jack Rabbit II trials

2020 ◽  
Vol 235 ◽  
pp. 117516 ◽  
Author(s):  
Steven Hanna
Keyword(s):  
Meteorological Data ◽  
Dispersion Models

Evaluation of the performance of different short-range atmospheric dispersion models for the monitoring of CH4 emissions from industrial facilities

2021 ◽  
Author(s):  
Bonaventure Fontanier ◽  
Pramod Kumar ◽  
Grégoire Broquet ◽  
Christopher Caldow ◽  
Olivier Laurent ◽  
...  
Keyword(s):  
High Frequency ◽  
Meteorological Data ◽  
Atmospheric Dispersion ◽  
Local Scale ◽  
Lagrangian Model ◽  
Dispersion Models ◽  
Industrial Facilities ◽  
Gaussian Plume ◽  
Gaussian Puff ◽  
Gaussian Puff Model

<p>Methane (CH<sub>4</sub>) is a powerful greenhouse gas which plays a major role in climate change. The accurate monitoring of emissions from industrial facilities is needed to ensure efficient emission mitigation strategies. Local-scale atmospheric inversions are increasingly being used to provide estimates of the rates and/or locations of CH<sub>4</sub> sources from industrial sites. They rely on local-scale atmospheric dispersion models, CH<sub>4</sub> measurements and inversion approaches. Gaussian plume models have often been used for local-scale atmospheric dispersion modelling and inversions of emissions, because of their simplicity and good performance when used in a flat terrain and relatively constant mean wind conditions. However, even in such conditions, failure to account for wind and mole fraction variability can limit the ability to exploit the full potential of these measurements at high frequency.</p><p>We study whether the accuracy of inversions can be increased by the use of more complex dispersion models. Our assessments are based on the analysis of 25 to 75-min CH<sub>4 </sub>controlled releases during a one-week campaign in October 2019 at the TOTAL’s TADI operative platform in Lacq, France (in a flat area). During this campaign, for each controlled release, we conducted near-surface in situ measurements of CH<sub>4</sub> mole fraction from both a mobile vehicle and a circle of fixed points around the emission area. Our inversions based on a Gaussian model and either the mobile or fixed-point measurements both provided estimates of the release rates with 20-30% precision.  </p><p>Here we focus on comparisons between modeling and inversion results when using this Gaussian plume model, a Lagrangian model “GRAL” and a Gaussian puff model. The parameters for the three models are based on high-frequency meteorological values from a single stationary 3D sonic anemometer. GRAL should have relatively good skills under low-wind speed conditions. The Gaussian puff is a light implementation of time-dependent modeling and can be driven by high-frequency meteorological data. The performance of these dispersion models is evaluated with various metrics from the observation field that are relevant for the inversion. These analyses lead to the exploration of new types of definitions of the observational constraint for the inversions with the Gaussian puff model, when using the timeseries from fixed measurement points. The definitions explore a range of metrics in the time domain as well as in the frequency domain.</p><p>Eventually, the Lagrangian model does not outperform the Gaussian plume model in these experiments, its application being notably limited by the short scales of the transport characteristics. On the other hand, the Gaussian puff model provides promising results for the inversion, in particular, in terms of comparison between the simulated and observed timeseries for fixed stations. Its performance when driven by a spatially uniform wind field is an incentive to explore the use of meteorological data from several sonic stations to parameterize its configuration. The fixed-point measurements are shown to allow for more robust inversions of the source location than the mobile measurements, with an average source localization error of the order of 10 m.</p>

Quality Control of Meteorological Data for the Chemical Stockpile Emergency Preparedness Program

2009 ◽  
Vol 26 (8) ◽  
pp. 1510-1526 ◽  
Author(s):  
James C. Liljegren ◽  
Stephen Tschopp ◽  
Kevin Rogers ◽  
Fred Wasmer ◽  
Lucia Liljegren ◽  
...  
Keyword(s):  
Emergency Response ◽  
Emergency Preparedness ◽  
Visual Inspection ◽  
Meteorological Data ◽  
Chemical Weapons ◽  
Plume Dispersion ◽  
Dispersion Models ◽  
Problem Reporting ◽  
Automated Screening ◽  
Response Modeling

Abstract The Chemical Stockpile Emergency Preparedness Program Meteorological Support Project ensures the accuracy and reliability of data acquired by meteorological monitoring stations located at seven U.S. Army chemical weapons depots where storage and weapons destruction (demilitarization) activities are ongoing. The data are delivered in real time to U.S. Army plume dispersion models, which are used to plan for and respond to a potential accidental release of a chemical weapons agent. The project provides maintenance, calibration, and audit services for the instrumentation; collection, automated screening, visual inspection, and analysis of the data; and problem reporting and tracking to carefully control the data quality. The resulting high-quality meteorological data enhance emergency response modeling and public safety.

Take one dispersing plume and add some precipitation: using ensembles to simulate deposition uncertainty

2020 ◽  
Author(s):  
Susan Leadbetter ◽  
Peter Bedwell ◽  
Gertie Geertsema ◽  
Irene Korsakissok ◽  
Jasper Tomas ◽  
...  
Keyword(s):  
High Resolution ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Radioactive Material ◽  
Positional Error ◽  
Multiple Model ◽  
Dispersion Models ◽  
Air Concentration ◽  
Initial State

<p>In the event of an accidental airborne release of radioactive material, dispersion models would be used to simulate the spread of the pollutant in the atmosphere and its subsequent deposition. Typically, meteorological information is provided to dispersion models from numerical weather prediction (NWP) models. As these NWP models have increased in resolution their ability to resolve short-lived, heavy precipitation events covering smaller areas has improved. This has led to more realistic looking precipitation forecasts. However, when traditional statistics comparing precipitation predictions to measurements at a point (e.g. an observation site) are used, these high-resolution models appear to have a lower skill in predicting precipitation due to small differences in the location and timing of the precipitation with respect to the observations. This positional error is carried through to the dispersion model resulting in predictions of high deposits where none are observed and vice versa; a problem known as the double penalty problem in meteorology.</p><p>Since observations are not available at the onset of an event, it is crucial to gain insight into the possible location and timing errors. One method to address this issue is to use ensemble meteorological data as input to the dispersion model. Meteorological ensembles are typically generated by running multiple model integrations where each model integration starts from a perturbed initial state and uses slightly different model parametrisations to represent uncertainty in the atmospheric state and its evolution. Ensemble meteorological data provide several possible predictions of the precipitation that are all considered to be equally likely and this allows the dispersion model to produce several possible predictions of the deposits of radioactive material.</p><p>As part of the Euratom funded project, CONFIDENCE, a case study involving the passage of a warm front, where the timing of the front is uncertain in relation to a hypothetical nuclear accident in Europe was examined. In this study a ten-member meteorological ensemble was generated using time lagged forecasts to simulate perturbations in the initial state and two different model parameterisations. This meteorological ensemble was used as input to a single dispersion model to generate a dispersion model ensemble. The resulting ensemble dispersion output and methods to communicate the uncertainty in the deposition and the resulting uncertainty in the air concentration predictions are presented. The results demonstrate how high-resolution meteorological ensembles can be combined with dispersion models to simulate the maximum impact of precipitation and the uncertainty in its position and timing.</p>

Exploring forecast variability during volcanic ash cloud events 

2021 ◽  
Author(s):  
Frances Beckett ◽  
Ralph Burton ◽  
Fabio Dioguardi ◽  
Claire Witham ◽  
John Stevenson ◽  
...  
Keyword(s):  
Real Time ◽  
Volcanic Ash ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Atmospheric Dispersion ◽  
Dispersion Models ◽  
British Geological Survey ◽  
Volcanic Ash Cloud ◽  
Ash Cloud

<p>Atmospheric transport and dispersion models are used by Volcanic Ash Advisory Centers (VAACs) to provide timely information on volcanic ash clouds to mitigate the risk of aircraft encounters. Inaccuracies in dispersion model forecasts can occur due to the uncertainties associated with source terms, meteorological data and model parametrizations. Real-time validation of model forecasts against observations is therefore essential to ensure their reliability. Forecasts can also benefit from comparison to model output from other groups; through understanding how different modelling approaches, variations in model setups, model physics, and driving meteorological data, impact the predicted extent and concentration of ash. The Met Office, the National Centre for Atmospheric Science (NCAS) and the British Geological Survey (BGS) are working together to consider how we might compare data (both qualitatively and quantitatively) from the atmospheric dispersion models NAME, FALL3D and HYSPLIT, using meteorological data from the Met Office Unified Model and the NOAA Global Forecast System (providing an effective multi-model ensemble). Results from the model inter-comparison will be used to provide advice to the London VAAC to aid forecasting decisions in near real time during a volcanic ash cloud event. In order to facilitate this comparison, we developed a Python package (ash-model-plotting) to read outputs from the different models into a consistent structure. Here we present our framework for generating comparable plots across the different partners, with a focus on total column mass loading products. These are directly comparable to satellite data retrievals and therefore important for model validation. We also present outcomes from a recent modelling exercise and discuss next steps for further improving our forecast validation.</p>

scholarly journals A field study to estimate the impact of noise barriers on mitigation of near road air pollution

2021 ◽  
Author(s):  
Ranga Rajan Thiruvenkatachari ◽  
Yifan Ding ◽  
David Pankratz ◽  
Akula Venkatram
Keyword(s):  
Air Pollution ◽  
Field Study ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Dispersion Models ◽  
Noise Barriers ◽  
Data Set ◽  
Wind Speeds ◽  
Sonic Anemometers ◽  
The Impact

AbstractAir pollution associated with vehicle emissions from roadways has been linked to a variety of adverse health effects. Wind tunnel and tracer studies show that noise barriers mitigate the impact of this pollution up to distances of 30 times the barrier height. Data from these studies have been used to formulate dispersion models that account for this mitigating effect. Before these models can be incorporated into Federal and State regulations, it is necessary to demonstrate their applicability under real-world conditions. This paper describes a comprehensive field study conducted in Riverside, CA, in 2019 to collect the data required to evaluate the performance of these models. Eight vehicles fitted with SF6 tracer release systems were driven in a loop on a 2-km stretch of Interstate 215 that had a 5-m tall noise barrier on the downwind side. The tracer, SF6, was sampled at over 40 locations at distances ranging from 5 to 200 m from the barrier. Meteorological data were measured with several 3-D sonic anemometers located upwind and downwind of the highway. The data set, corresponding to 10 h collected over 4 days, consists of information on emissions, tracer concentrations, and micrometeorological variables that can be used to evaluate barrier effects in dispersion models. An analysis of the data using a dispersion model indicates that current models are likely to overestimate concentrations, or underestimate the mitigation from barriers, at low wind speeds. We suggest an approach to correct this problem.

scholarly journals Parameterizing Unresolved Mesoscale Motions in Atmospheric Dispersion Models

2018 ◽  
Vol 57 (3) ◽  
pp. 645-657 ◽  
Author(s):  
Helen N. Webster ◽  
Thomas Whitehead ◽  
David J. Thomson
Keyword(s):  
Time Scales ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Strong Dependence ◽  
Atmospheric Dispersion ◽  
Three Dimensional ◽  
Dispersion Models ◽  
Atmospheric Dispersion Models ◽  
Velocity Variances ◽  
Nwp Model

AbstractIn atmospheric dispersion models driven by meteorological data from numerical weather prediction (NWP) models, it is necessary to include a parameterization for plume spread that is due to unresolved mesoscale motions. These are motions that are not resolved by the input NWP data but are larger in size than the three-dimensional turbulent motions represented by turbulence parameterizations. Neglecting the effect of these quasi-two-dimensional unresolved mesoscale motions has been shown to lead to underprediction of plume spread and overprediction of concentrations within the plume. NWP modeling is conducted at a range of resolutions that resolve different scales of motion. This suggests that any parameterization of unresolved mesoscale motions should depend on the resolution of the input NWP data. Spectral analysis of NWP data and wind observations is used to assess the mesoscale motions unresolved by the NWP model. Appropriate velocity variances and Lagrangian time scales for these motions are found by calculating the missing variance in the energy spectra and analyzing correlation functions. A strong dependence on the resolution of the NWP data is seen, resulting in larger velocity variances and Lagrangian time scales from the lower-resolution models. A parameterization of unresolved mesoscale motions on the basis of the NWP resolution is proposed.

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