Statistical post-processing of visibility ensemble forecasts

To be able to produce accurate and reliable predictions 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 with other variables, such as temperature or wind speed. Hence, some form of calibration is strongly advised, which usually means estimation of the predictive distribution of the weather quantity at hand either by parametric or nonparametric 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 covering two slightly overlapping domains in Central and Western Europe and two different time periods, we investigate the predictive performance of locally, semi-locally and regionally trained proportional odds logistic regression (POLR) and multilayer perceptron (MLP) neural network classifiers. We show 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. The predictive performance of proportional odds logistic regression and multilayer perceptron neural network classifiers for statistical post-processing of visibility ensemble forecasts of the European Centre for Medium-Range Weather Forecasts is investigated in the situation when observations are reported in discrete values. We show 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, proportional odds logistic regression models are superior to their machine learning-based counterparts.image