Matthew Simpson

Airborne spread of foot-and-mouth disease - model intercomparison.

Foot-and-mouth disease virus (FMDV) spreads by direct contact between animals, by animal products (milk, meat and semen), by mechanical transfer on people or fomites and by the airborne route, with the relative importance of each mechanism depending on the particular outbreak characteristics. Atmospheric dispersion models have been developed to assess airborne spread of FMDV in a number of countries, including the UK, Denmark, Australia, New Zealand, USA and Canada. These models were compared at a Workshop hosted by the Institute for Animal Health/Met Office in 2008. Each modeller was provided with data relating to the 1967 outbreak of FMD in Hampshire, UK, and asked to predict the spread of FMDV by the airborne route. A number of key issues emerged from the Workshop and subsequent modelling work: (1) in general all models predicted similar directions for livestock at risk, with much of the remaining differences strongly related to differences in the meteorological data used; (2) determination of an accurate sequence of events on the infected premises is highly important, especially if the meteorological conditions vary substantially during the virus emission period; (3) differences in assumptions made about virus release, environmental fate and susceptibility to airborne infection can substantially modify the size and location of the downwind risk area. All of the atmospheric dispersion models compared at the Workshop can be used to assess windborne spread of FMDV and provide scientific advice to those responsible for making control and eradication decisions in the event of an outbreak of disease.

Atmospheric Dispersion Modeling: Challenges of the Fukushima Daiichi Response

Abstract The U.S. Department of Energy’s (DOE) National Atmospheric Release Advisory Center (NARAC) provided a wide range of predictions and analyses as part of the response to the Fukushima Daiichi Nuclear Power Plant accident including: • Daily Japanese weather forecasts and atmospheric transport predictions to inform planning for field monitoring operations and to provide U.S. government agencies with ongoing situational awareness of meteorological conditions; • Estimates of possible dose in Japan based on hypothetical U.S. Nuclear Regulatory Commission scenarios of potential radionuclide releases to support protective action planning for U.S. citizens; • Predictions of possible plume arrival times and dose levels at U.S. locations; and • Source estimation and plume model refinement based on atmospheric dispersion modeling and available monitoring data. This paper provides an overview of NARAC response activities, along with a more in-depth discussion of some of NARAC’s preliminary source reconstruction analyses. NARAC optimized the overall agreement of model predictions to dose rate measurements using statistical comparisons of data and model values paired in space and time. Estimated emission rates varied depending on the choice of release assumptions (e.g., time-varying vs. constant release rates), the radionuclide mix, meteorology, and/or the radiological data used in the analysis. Results were found to be consistent with other studies within expected uncertainties, despite the application of different source estimation methodologies and the use of significantly different radiological measurement data. The paper concludes with a discussion of some of the operational and scientific challenges encountered during the response, along with recommendations for future work.

International challenge to model the long-range transport of radioxenon released from medical isotope production to six Comprehensive Nuclear-Test-Ban Treaty monitoring stations

After performing a first multi-model exercise in 2015 a comprehensive and technically more demanding atmospheric transport modelling challenge was organized in 2016. Release data were provided by the Australian Nuclear Science and Technology Organization radiopharmaceutical facility in Sydney (Australia) for a one month period. Measured samples for the same time frame were gathered from six International Monitoring System stations in the Southern Hemisphere with distances to the source ranging between 680 (Melbourne) and about 17,000 km (Tristan da Cunha). Participants were prompted to work with unit emissions in pre-defined emission intervals (daily, half-daily, 3-hourly and hourly emission segment lengths) and in order to perform a blind test actual emission values were not provided to them. Despite the quite different settings of the two atmospheric transport modelling challenges there is common evidence that for long-range atmospheric transport using temporally highly resolved emissions and highly space-resolved meteorological input fields has no significant advantage compared to using lower resolved ones. As well an uncertainty of up to 20% in the daily stack emission data turns out to be acceptable for the purpose of a study like this. Model performance at individual stations is quite diverse depending largely on successfully capturing boundary layer processes. No single model-meteorology combination performs best for all stations. Moreover, the stations statistics do not depend on the distance between the source and the individual stations. Finally, it became more evident how future exercises need to be designed. Set-up parameters like the meteorological driver or the output grid resolution should be pre-scribed in order to enhance diversity as well as comparability among model runs.

Integrated stochastic weather and production simulation modeling

High penetration of intermittent renewable generators can substantially increase the variability and uncertainty in power system operations. Energy storage and demand response have been proposed as resources that can be used to mitigate this uncertainty and variability. This paper describes planning system that couples a stochastic weather model, renewable generation models that are driven by the weather, a stochastic production simulation model, and a system stability model. The system is used to simulate operation of the California grid with 33% variable renewable generation in the year 2020. The values of energy storage and demand response are estimated by identifying the avoided costs of the conventional hydro and fossil resources that they displace when providing regulation, load following, and energy arbitrage functions. The impacts on system stability are also assessed.