atmospheric dispersion model
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Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1573
Author(s):  
Rachel Pelley ◽  
David Thomson ◽  
Helen Webster ◽  
Michael Cooke ◽  
Alistair Manning ◽  
...  

We present a Bayesian inversion method for estimating volcanic ash emissions using satellite retrievals of ash column load and an atmospheric dispersion model. An a priori description of the emissions is used based on observations of the rise height of the volcanic plume and a stochastic model of the possible emissions. Satellite data are processed to give column loads where ash is detected and to give information on where we have high confidence that there is negligible ash. An atmospheric dispersion model is used to relate emissions and column loads. Gaussian distributions are assumed for the a priori emissions and for the errors in the satellite retrievals. The optimal emissions estimate is obtained by finding the peak of the a posteriori probability density under the constraint that the emissions are non-negative. We apply this inversion method within a framework designed for use during an eruption with the emission estimates (for any given emission time) being revised over time as more information becomes available. We demonstrate the approach for the 2010 Eyjafjallajökull and 2011 Grímsvötn eruptions. We apply the approach in two ways, using only the ash retrievals and using both the ash and clear sky retrievals. For Eyjafjallajökull we have compared with an independent dataset not used in the inversion and have found that the inversion-derived emissions lead to improved predictions.


Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1305
Author(s):  
Spyros Andronopoulos ◽  
Ivan V. Kovalets

A computationally efficient source inversion algorithm was developed and applied with the Lagrangian atmospheric dispersion model DIPCOT. In the process of source location estimation by minimizing a correlation-based cost function, the algorithm uses only the values of the time-integrated concentrations at the monitoring stations instead of all of the individual measurements in the full concentration-time series, resulting in a significant reduction in the number of integrations of the backward transport equations. Following the source location estimation the release start time, duration and emission rate are assessed. The developed algorithm was verified for the conditions of the ETEX-I (European Tracer Experiment—1st release). Using time-integrated measurements from all available stations, the distance between the estimated and true source location was 108 km. The estimated start time of the release was only about 1 h different from the true value, within the possible accuracy of estimate of this parameter. The estimated release duration was 21 h (the true value was 12 h). The estimated release rate was 4.28 g/s (the true value was 7.95 g/s). The estimated released mass almost perfectly fitted the true released mass (323.6 vs. 343.4 kg). It thus could be concluded that the developed algorithm is suitable for further integration in real-time decision support systems.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
D. Viúdez-Moreiras

Abstract Atmospheric local-to-regional dispersion models are widely used on Earth to predict and study the effects of chemical species emitted into the atmosphere and to contextualize sparse data acquired at particular locations and/or times. However, to date, no local-to-regional dispersion models for Mars have been developed; only mesoscale/microscale meteorological models have some dispersion and chemical capabilities, but they do not offer the versatility of a dedicated atmospheric dispersion model when studying the dispersion of chemical species in the atmosphere, as it is performed on Earth. Here, a new three-dimensional local-to-regional-scale Eulerian atmospheric dispersion model for Mars (DISVERMAR) that can simulate emissions to the Martian atmosphere from particular locations or regions including chemical loss and predefined deposition rates, is presented. The model can deal with topography and non-uniform grids. As a case study, the model is applied to the simulation of methane spikes as detected by NASA’s Mars Science Laboratory (MSL); this choice is made given the strong interest in and controversy regarding the detection and variability of this chemical species on Mars.


2021 ◽  
Author(s):  
Vasileios Baousis ◽  
Umberto Modigliani ◽  
Florian Pappenberger ◽  
Martin Palkovic ◽  
Stephan Siemen ◽  
...  

<p>Since 2019, ECMWF (European Centre for Medium-Range Weather Forecasts) together with EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) initiated a project named “<strong>European Weather Cloud</strong>” (https://www.europeanweather.cloud/) expected to become operational in 2022. The strategic goal of this initiative is to build and offer a <strong>community cloud infrastructure</strong> on which Member and Co‐operating States of both organizations can create and manage on demand virtual resources enabling access to the ECMWF’s Numerical Weather Predication (NWP) products and EUMETSAT’s satellite data in a timely, efficient, and configurable fashion. Moreover, one of the main goals is to involve more entities in this initiative in a joint effort to form a federation of clouds/data offered from our Member States, for the maximum benefit of the European Meteorological Infrastructure.</p><p>During the current pilot phase of the project several use cases have been defined, mostly aimed at service developers own organisations. These broad categories of use cases are:</p><ul><li>Web services exploring hosted datasets.</li> <li>Infrastructure allowing the running of an atmospheric dispersion model on ECMWF forecast data.</li> <li>Platform to support the training of machine learning models on archive datasets.</li> <li>Platform to support workshops and training courses (DWD/ICON model training, various ECMWF training courses)</li> <li>Environment facilitating research in collaboration with external partners.</li> </ul><p>Some examples of the use cases currently developed at the European Weather Cloud are:</p><ul><li>The Royal Meteorological Institute of Belgium prepares ECMWF forecast data for use in a local atmospheric dispersion model.</li> <li>The German weather service, which is already feeding maps generated by a server it deployed on the cloud into its public GeoPortal service.</li> <li>The Royal Netherlands Meteorological Institute hosts a climate explorer web application based on KNMI climate explorer data and ECMWF weather and climate reanalyses.</li> <li>EUMETSAT Numerical Weather Prediction Satellite Application Facility (NWP SAF) develops a training module will develop a training module for a fast radiative transfer model (RTTOV) based on ERA5 reanalysis data.</li> <li>EUMETSAT and ECMWF joint use case assess bias correction schemes for the assimilation of radiance data based on several satellite data time series.</li> </ul><p>During the current pilot phase of the project, both organizations have organised user and technical workshops to actively engage with the meteorological community to align the evolution of the European Weather Cloud to reflect and satisfy their goals and needs.</p><p>In this presentation, the status of the project will be analysed describing the existing infrastructure, the offered services and how these are accessed by the end-users along with examples of the existing use cases. The plans, next steps for the evolution and the transition to operations of the European Weather Cloud and its relationship with other projects and initiatives will conclude the presentation.</p>


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yungang Zhao ◽  
Yuanyuan Liu ◽  
Li Wang ◽  
Jianping Cheng ◽  
Shilian Wang ◽  
...  

Source term reconstruction methods attempt to calculate the most likely source parameters of an atmospheric release given measurements, including both location and release amount. However, source term reconstruction is vulnerable to uncertainties. In this paper, a method combining Bayesian inference with the backward atmospheric dispersion model is developed for robust source term reconstruction. The backward model is used to quantify the relationship between the source and measurements and to reduce the search range of the Bayesian inference. A Markov chain Monte Carlo method is used to sample from the multidimensional parameter space of the source term. The source location and release rate are estimated simultaneously, and the posterior probability distribution is produced by applying Bayes’ theorem. The proposed method is applied to a set of real concentration data from the ETEX-I experiment. The results demonstrate that the source location is estimated to be −2.86° ± 1.01°E, 48.25° ± 0.33°N, and the release rate is estimated to be 20.16 ± 3.56 kg/h. The true source location is correctly estimated to be within a one standard deviation interval, and the release rate is correctly determined to be within a three standard deviation interval.


2021 ◽  
Author(s):  
Hiromi Yamazawa ◽  
Yousuke Sato ◽  
Tsuyoshi Sekiyama ◽  
Mizuo Kajino ◽  
Sheng Fang ◽  
...  

<p>The 3rd model intercomparison project (MIP) of atmospheric dispersion model targeting on <sup>137</sup>Cs emitted from the Fukushima Daiichi Nuclear Power Plant (FDNPP) in March 2011 was conducted (Sato et al. 2020). Nine models participated in the 3rd MIP. All participated models used the identical source term of Katata et al. (2015) and the identical meteorological data (Sekiyama and Kajino, 2020) as in the previous MIP (i.e., 2nd MIP Sato et al. 2018), but finer horizontal grid resolution (1 km) than that of 2nd MIP (3 km) was used for understanding the behavior of atmospheric <sup>137</sup>Cs measured in the vicinity of FDNPP. Results of the models elucidated that, as in the 2nd MIP, most of the observed high atmospheric <sup>137</sup>Cs concentrations (plumes) were reasonably well simulated by the models, and the good performance of some models cancelled a bad performance of some models when used as an ensemble, which highlights the advantage of the multimodel ensemble. The analyses also indicated that the use of the finer grid resolution (1 km) improved the meteorological field in the vicinity of FNDPP. As a consequence, the atmospheric <sup>137</sup>Cs measured near FDNPP was more reasonably reproduced in 3rd MIP than 2nd MIP.</p><p>As well as the evaluation of the performance of the model, we examined the usefulness of the results of atmospheric dispersion simulation in an emergency base on the results of 2nd and 3rd MIPs. For the analyses we defined the worst situation as that plume is observed but the model does not simulate it. The analyses reported that the worst situation happened in only 3% of the total calculation period by using the multimodel ensemble, even if the absolute value of the simulated <sup>137</sup>Cs in each model was different in the range of factor 3-6. The analyses also indicated that from six to eight models are required for making most of the advantages of the multimodel ensemble.</p>


PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245932
Author(s):  
Daiki Satoh ◽  
Hiromasa Nakayama ◽  
Takuya Furuta ◽  
Tamotsu Yoshihiro ◽  
Kensaku Sakamoto

In this study, we developed a simulation code powered by lattice dose-response functions (hereinafter SIBYL), which helps in the quick and accurate estimation of external gamma-ray doses emitted from a radioactive plume and contaminated ground. SIBYL couples with atmospheric dispersion models and calculates gamma-ray dose distributions inside a target area based on a map of activity concentrations using pre-evaluated dose-response functions. Moreover, SIBYL considers radiation shielding due to obstructions such as buildings. To examine the reliability of SIBYL, we investigated five typical cases for steady-state and unsteady-state plume dispersions by coupling the Gaussian plume model and the local-scale high-resolution atmospheric dispersion model using large eddy simulation. The results of this coupled model were compared with those of full Monte Carlo simulations using the particle and heavy-ion transport code system (PHITS). The dose-distribution maps calculated using SIBYL differed by up to 10% from those calculated using PHITS in most target locations. The exceptions were locations far from the radioactive contamination and those behind the intricate structures of building arrays. In addition, SIBYL’s computation time using 96 parallel processing elements was several tens of minutes even for the most computationally expensive tasks of this study. The computation using SIBYL was approximately 100 times faster than the same calculation using PHITS under the same computation conditions. From the results of the case studies, we concluded that SIBYL can estimate a ground-level dose-distribution map within one hour with accuracy that is comparable to that of the full Monte Carlo simulation.


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