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Hybrid Forecasting of Wind for Air Pollution Dispersion over Complex Terrain

M. Perne1,2 *, J. Kocijan1,3, M. Z. Božnar4, B. Grašič4, and P. Mlakar4

  1. Jožef Stefan Institute, Ljubljana 1000, Slovenia
  2. Quantectum, prognoza potresov, d.o.o., Ljubljana 1000, Slovenia
  3. University of Nova Gorica, Nova Gorica 5000, Slovenia
  4. MEIS d.o.o., Mali Vrh pri Šmarju, Šmarje-Sap 1293, Slovenia

*Corresponding author. Tel.: +386 1-4773800; fax: +386 5-6205200. E-mail address: (M. Perne).


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.

Keywords: atmospheric dispersion model, dynamic systems, Gaussian process, hybrid model, system identification, wind forecasting

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