Abstract
Statistical methods are widely applied in visibility forecasting. In this article, further improvements are explored by extending the standard probabilistic neural network approach. The first approach is to use several models to obtain an averaged output, instead of just selecting the overall best one, while the second approach is to use deterministic neural networks to make input variables for the probabilistic neural network. These approaches are extensively tested at two sites and seen to improve upon the standard approach, although the improvements for one of the sites were not found to be of statistical significance.
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Bremnes, J.B., Michaelides, S.C. (2007). Probabilistic Visibility Forecasting Using Neural Networks. In: Gultepe, I. (eds) Fog and Boundary Layer Clouds: Fog Visibility and Forecasting. Pageoph Topical Volumes. Birkhäuser Basel. https://doi.org/10.1007/978-3-7643-8419-7_15
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DOI: https://doi.org/10.1007/978-3-7643-8419-7_15
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