B0527
Title: Discrete post-processing of visibility ensemble forecasts using machine learning
Authors: Sandor Baran - University of Debrecen (Hungary) [presenting]
Maria Nagy-Lakatos - University of Debrecen (Hungary)
Abstract: Accurate and reliable prediction of visibility has crucial importance in aviation meteorology, as well as in water- and road transportation. Nowadays, several meteorological services provide ensemble forecasts of visibility; however, the skill, and reliability of visibility predictions are far reduced compared to other variables, such as temperature or wind speed. Hence, some form of calibration is strongly advised, which usually means the estimation of the predictive distribution of the weather quantity at hand either by parametric or non-parametric approaches, including machine learning-based techniques. As visibility observations - according to the suggestion of the World Meteorological Organization - are usually reported in discrete values, the predictive distribution for this particular variable is a discrete probability law, hence calibration can be reduced to a classification problem. Based on visibility ensemble forecasts of the European Centre for medium-range weather forecasts, the predictive performance of locally, semi-locally and regionally trained proportional odds logistic regression (POLR) and multilayer perceptron (MLP) neural network classifiers are investigated. It is shown that while climatological forecasts outperform the raw ensemble by a wide margin, post-processing results in further substantial improvement in forecast skill and in general, POLR models are superior to their MLP counterparts.
