scholarly journals Assessing the value meteorological ensembles add to dispersion modelling using hypothetical releases

2022 ◽  
Vol 22 (1) ◽  
pp. 577-596
Author(s):  
Susan J. Leadbetter ◽  
Andrew R. Jones ◽  
Matthew C. Hort

Abstract. Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly, scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model, and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 h over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction (NWP) models where observations are sparse or non-existent, we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations, although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps. In addition there is a greater increase in skill score over time for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level; e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty, but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.

2021 ◽  
Author(s):  
Susan Janet Leadbetter ◽  
Andrew R. Jones ◽  
Matthew C. Hort

Abstract. Atmospheric dispersion model output is frequently used to provide advice to decision makers, for example, about the likely location of volcanic ash erupted from a volcano or the location of deposits of radioactive material released during a nuclear accident. Increasingly scientists and decision makers are requesting information on the uncertainty of these dispersion model predictions. One source of uncertainty is in the meteorology used to drive the dispersion model and in this study ensemble meteorology from the Met Office ensemble prediction system is used to provide meteorological uncertainty to dispersion model predictions. Two hypothetical scenarios, one volcanological and one radiological, are repeated every 12 hours over a period of 4 months. The scenarios are simulated using ensemble meteorology and deterministic forecast meteorology and compared to output from simulations using analysis meteorology using the Brier skill score. Adopting the practice commonly used in evaluating numerical weather prediction models (NWP) where observations are sparse or non-existent we consider output from simulations using analysis NWP data to be truth. The results show that on average the ensemble simulations perform better than the deterministic simulations although not all individual ensemble simulations outperform their deterministic counterpart. The results also show that greater skill scores are achieved by the ensemble simulation for later time steps rather than earlier time steps and at those later time steps for deposition than for air concentration. For the volcanic ash scenarios it is shown that the performance of the ensemble at one flight level can be different to that at a different flight level, e.g. a negative skill score might be obtained for FL350-550 and a positive skill score for FL200-350. This study does not take into account any source term uncertainty but it does take the first steps towards demonstrating the value of ensemble dispersion model predictions.


Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 200 ◽  
Author(s):  
Helen N. Webster ◽  
Benjamin J. Devenish ◽  
Larry G. Mastin ◽  
David J. Thomson ◽  
Alexa R. Van Eaton

Large explosive eruptions can result in the formation of an umbrella cloud which rapidly expands, spreading ash out radially from the volcano. The lateral spread by the intrusive gravity current dominates the transport of the ash cloud. Hence, to accurately forecast the transport of ash from large eruptions, lateral spread of umbrella clouds needs to be represented within volcanic ash transport and dispersion models. Here, we describe an umbrella cloud parameterisation which has been implemented into an operational Lagrangian model and consider how it may be used during an eruption when information concerning the eruption is limited and model runtime is key. We examine different relations for the volume flow rate into the umbrella, and the rate of spreading within the cloud. The scheme is validated against historic eruptions of differing scales (Pinatubo 1991, Kelud 2014, Calbuco 2015 and Eyjafjallajökull 2010) by comparing model predictions with satellite observations. Reasonable predictions of umbrella cloud spread are achieved using an estimated volume flow rate from the empirical equation by Bursik et al. and the observed eruption height. We show how model predictions can be refined during an ongoing eruption as further information and observations become available.


2020 ◽  
Author(s):  
Susan Leadbetter ◽  
Peter Bedwell ◽  
Gertie Geertsema ◽  
Irene Korsakissok ◽  
Jasper Tomas ◽  
...  

<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>


2016 ◽  
Author(s):  
Tianfeng Chai ◽  
Alice Crawford ◽  
Barbara Stunder ◽  
Michael Pavolonis ◽  
Roland Draxler ◽  
...  

Abstract. Currently NOAA's National Weather Service (NWS) runs the HYSPLIT dispersion model with a unit mass release rate to predict the transport and dispersion of volcanic ash. The model predictions provide information for the Volcanic Ash Advisory Centers (VAAC) to issue advisories to meteorological watch offices, area control centers, flight information centers, and others. This research aims provide quantitative forecasts of ash distributions generated by objectively and optimally estimating the volcanic ash source strengths, vertical distribution and temporal variations using an observation-modeling inversion technique. In this top-down approach, a cost functional is defined to mainly quantify the differences between model predictions and the satellite measurements of column integrated ash concentrations, weighted by the model and observation uncertainties. Minimizing this cost functional by adjusting the sources provides the volcanic ash emission estimates. As an example, MODIS (MOderate Resolution Imaging Spectroradiometer) satellite retrievals of the 2008 Kasatochi volcanic ash clouds are used to test the HYSPLIT volcanic ash inverse system. Because the satellite retrievals include the ash cloud top height but not the bottom height, three options for matching the model concentrations to the observed mass loadings are tested. Although the emission estimates vary significantly with different options the subsequent model predictions with the different release estimates all show decent skill when evaluated against the unassimilated satellite observations at later times. Among the three options, integrating over three model layers yields slightly better results than integrating from the surface up to the volcanic ash cloud top or using a single model layer. Inverse tests also show that including the ash-free region to constrain the model is not beneficial for the current case. In addition, extra constraints to the source terms can be given by explicitly enforcing ``no-ash'' for the atmosphere columns above or below the observed ash cloud top height. However, in this case such extra constraints are not helpful for the inverse modeling. It is also found that simultaneously assimilating observations at different times produces better hindcasts than only assimilating the most recent observations.


2009 ◽  
Vol 3 (1) ◽  
pp. 79-84 ◽  
Author(s):  
B. Szintai ◽  
P. Kaufmann ◽  
M. W. Rotach

Abstract. At MeteoSwiss an integrated modelling system is used to simulate the dispersion of radioactive material in emergency situations. For the prediction of the atmospheric flow, the COSMO numerical weather prediction model is used. The model is run operationally at 6.6 and 2.2 km horizontal resolution, respectively and uses a 1.5 order turbulence closure with a prognostic equation for turbulent kinetic energy. Both versions of the COSMO model are coupled off-line with a Lagrangian particle dispersion model (LPDM). The aim of this study is to investigate the sensitivity of the dispersion model to different interfacing approaches between LPDM and the COSMO model. The diagnosed turbulence variables are validated on an ideal convective case and two measurement campaigns. Simulations of hypothetical pollutant releases show that the different interfacing approaches can lead to substantial changes in the forecasted concentrations.


2021 ◽  
Author(s):  
Antonio Capponi ◽  
Natalie J. Harvey ◽  
Helen F. Dacre ◽  
Keith Beven ◽  
Mike R. James

<p>Volcanic ash poses a significant hazard for aviation. If an ash cloud forms as result of an eruption, it forces a series of flight planning decisions that consider important safety and economic factors. These decisions are made using a combination of satellite retrievals and volcanic ash forecasts issued by Volcanic Ash Advisory Centres.  However, forecasts of ash hazard remain deterministic, and lack quantification of the uncertainty that arises from the estimation of eruption source parameters, meteorology and uncertainties within the dispersion model used to perform the simulations. Quantification of these uncertainties is fundamental and could be achieved by using ensemble simulations. Here, we explore how ensemble-based forecasts — performed using the Met Office dispersion model NAME — together with sequential satellite retrievals of ash column loading, may improve forecast accuracy and uncertainty characterization.</p><p>We have developed a new methodology to evaluate each member of the ensemble based on its agreement with the satellite retrievals available at the time. An initial ensemble is passed through a filter of verification metrics and compared with the first available set of satellite observations. Members far from the observations are rejected. The members within a limit of acceptability are used to resample the parameters used in the initial ensemble, and design a new ensemble to compare with the next available set of satellite observations. The filtering process and parameter resampling are applied whenever new satellite observations are available, to create new ensembles propagating forward in time, until all available observations are covered.</p><p>Although the method requires the run of many ensemble batches, and it is not yet suited for operational use, it shows how combining ensemble simulations and sequential satellite retrievals can be used to quantify confidence in ash forecasts. We demonstrate the method by applying it to the recent Raikoke (Kurii Islands, Russia) eruption, which occurred on the 22<sup>nd</sup> July 2019. Each ensemble consists of 1000 members and it is evaluated against 6-hourly HIMAWARI satellite ash retrievals.</p>


2017 ◽  
Vol 21 (5) ◽  
pp. 2531-2544 ◽  
Author(s):  
Vianney Courdent ◽  
Morten Grum ◽  
Thomas Munk-Nielsen ◽  
Peter S. Mikkelsen

Abstract. Precipitation is the cause of major perturbation to the flow in urban drainage and wastewater systems. Flow forecasts, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimize the operation of integrated urban drainage–wastewater systems (IUDWSs) during both wet and dry weather periods. Numerical weather prediction (NWP) models have significantly improved in recent years, having increased their spatial and temporal resolution. Finer resolution NWP are suitable for urban-catchment-scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain, and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with ensemble prediction systems (EPSs). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain and therefore errors will be made; decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision-making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The relative economic value (REV) approach associates economic values with the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain–loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecast). The specificity of this study is to optimize the energy consumption in IUDWS during low-flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecast). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance the forecast skill and increase the range of beneficial uses.


2017 ◽  
Vol 17 (4) ◽  
pp. 2865-2879 ◽  
Author(s):  
Tianfeng Chai ◽  
Alice Crawford ◽  
Barbara Stunder ◽  
Michael J. Pavolonis ◽  
Roland Draxler ◽  
...  

Abstract. Currently, the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) runs the HYSPLIT dispersion model with a unit mass release rate to predict the transport and dispersion of volcanic ash. The model predictions provide information for the Volcanic Ash Advisory Centers (VAAC) to issue advisories to meteorological watch offices, area control centers, flight information centers, and others. This research aims to provide quantitative forecasts of ash distributions generated by objectively and optimally estimating the volcanic ash source strengths, vertical distribution, and temporal variations using an observation-modeling inversion technique. In this top-down approach, a cost functional is defined to quantify the differences between the model predictions and the satellite measurements of column-integrated ash concentrations weighted by the model and observation uncertainties. Minimizing this cost functional by adjusting the sources provides the volcanic ash emission estimates. As an example, MODIS (Moderate Resolution Imaging Spectroradiometer) satellite retrievals of the 2008 Kasatochi volcanic ash clouds are used to test the HYSPLIT volcanic ash inverse system. Because the satellite retrievals include the ash cloud top height but not the bottom height, there are different model diagnostic choices for comparing the model results with the observed mass loadings. Three options are presented and tested. Although the emission estimates vary significantly with different options, the subsequent model predictions with the different release estimates all show decent skill when evaluated against the unassimilated satellite observations at later times. Among the three options, integrating over three model layers yields slightly better results than integrating from the surface up to the observed volcanic ash cloud top or using a single model layer. Inverse tests also show that including the ash-free region to constrain the model is not beneficial for the current case. In addition, extra constraints on the source terms can be given by explicitly enforcing no-ash for the atmosphere columns above or below the observed ash cloud top height. However, in this case such extra constraints are not helpful for the inverse modeling. It is also found that simultaneously assimilating observations at different times produces better hindcasts than only assimilating the most recent observations.


2016 ◽  
Author(s):  
Vianney Courdent ◽  
Morten Grum ◽  
Thomas Munk-Nielsen ◽  
Peter S. Mikkelsen

Abstract. Precipitation is the major perturbation to the flow in urban drainage and wastewater systems. Flow forecast, generated by coupling rainfall predictions with a hydrologic runoff model, can potentially be used to optimise the operation of Integrated Urban Drainage–Wastewater Systems (IUDWS) during both wet and dry weather periods. Numerical Weather Prediction (NWP) models have significantly improved in recent years; increasing their spatial and temporal resolution. Finer resolution NWP are suitable for urban catchment scale applications, providing longer lead time than radar extrapolation. However, forecasts are inevitably uncertain and fine resolution is especially challenging for NWP. This uncertainty is commonly addressed in meteorology with Ensemble Prediction Systems (EPS). Handling uncertainty is challenging for decision makers and hence tools are necessary to provide insight on ensemble forecast usage and to support the rationality of decisions (i.e. forecasts are uncertain therefore errors will be made, decision makers need tools to justify their choices, demonstrating that these choices are beneficial in the long run). This study presents an economic framework to support the decision making process by providing information on when acting on the forecast is beneficial and how to handle the EPS. The Relative Economic Value (REV) approach associates economic values to the potential outcomes and determines the preferential use of the EPS forecast. The envelope curve of the REV diagram combines the results from each probability forecast to provide the highest relative economic value for a given gain-loss ratio. This approach is traditionally used at larger scales to assess mitigation measures for adverse events (i.e. the actions are taken when events are forecasted). The specificity of this study is to optimise the energy consumption in IUDWS during low flow periods by exploiting the electrical smart grid market (i.e. the actions are taken when no events are forecasted). Furthermore, the results demonstrate the benefit of NWP neighbourhood post-processing methods to enhance the forecast skill and increase the range of beneficial use.


2020 ◽  
Author(s):  
Frances Beckett ◽  
Claire Witham ◽  
Susan Leadbetter ◽  
Ric Crocker ◽  
Helen Webster ◽  
...  

<p>It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused chaos to air traffic across Europe. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. Here we present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned.</p><p>Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep atmospheric convection and parameterizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Further, atmospheric dispersion models are driven by 3-dimensional meteorological data from Numerical Weather Prediction (NWP) models and the Met Office’s upper air wind field data is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event affecting their region.</p>


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