MEIS Storitve za Okolje

In case of an unplanned emission event from a nuclear power plant, the local population can be protected more efficiently when valid atmospheric dispersion model results are available. Atmospheric dispersion models use local meteorological variables as inputs. When atmospheric dispersion in the future is being predicted, a forecast of the local meteorological variables is needed. The most important variable in atmospheric dispersion modelling is wind, and accurately predicting ground level winds presents a challenge to numerical weather prediction models. We therefore develop hybrid models for forecasting local ground level wind at a single location where the terrain is complex and the average wind is weak and fluctuating. Wind speed and direction are modelled as west-east and south-north wind components. Each model is composed of a numerical weather prediction model and a Gaussian process statistical model that uses numerical weather predictions as some of its inputs and is trained on historical data to predict the output component. The most advanced Gaussian process models studied are of Gaussian process nonlinear autoregressive model with exogenous input (GPNARX) type. In addition to numerical weather predictions, they also use local meteorological variables, including the output variable, as their inputs. Numerical weather prediction results based on large scale information and fundamental knowledge of the system are thus supplemented by local measurements that better reflect the effects of local topography and land use. The models are tested by prediction and simulation. The wind components predicted by more advanced models are more accurate than raw or post-processed numerical weather prediction results. As an example, a model predicting 2D wind vector 1.5 h in advance achieves a NRMSE of 0.214 if it uses all the immediately available information. This is better than both the persistence model with NRMSE of 0.188 and post-processed NWP with NRMSE of 0.164. This demonstrates that hybrid modelling provides the best weather information for short-term and medium-term atmospheric dispersion forecasting. While the method is motivated by nuclear emission sources, it could also be applied to other pollution.

The Šoštanj exercise of the Modelling and Data for Radiological Impact Assessments I Urban Environments Working Group took advantage of a set of measurement data from a 1991 tracer experiment to test atmospheric dispersion models for emissions from point sources over complex terrain. The data set included emissions of SO2 from the stacks of the Šoštanj Thermal Power Plant in Slovenia, measurements of the SO2 at a number of locations in the surrounding area up to 7 km from the plant, and meteorological data from several monitoring stations, all as measured half-hour average values. Two sets of meteorological conditions were modelled: (a) a simple situation with a strong wind blowing from a point source directly towards a monitoring station; and (b) a complex situation involving a temperature inversion and convective mixing. The modelling results enable the assessment of the capabilities of various dispersion models in handling both complex terrain and complex meteorological situations.