  In our contemporary era, meteorological weather forecasts increasingly
incorporate ensemble predictions of visibility - a parameter of great
importance in aviation, maritime navigation, and air quality assessment, with
direct implications for public health. However, this weather variable falls
short of the predictive accuracy achieved for other quantities issued by
meteorological centers. Therefore, statistical post-processing is recommended
to enhance the reliability and accuracy of predictions. By estimating the
predictive distributions of the variables with the aid of historical
observations and forecasts, one can achieve statistical consistency between
true observations and ensemble predictions. Visibility observations, following
the recommendation of the World Meteorological Organization, are typically
reported in discrete values; hence, the predictive distribution of the weather
quantity takes the form of a discrete parametric law. Recent studies
demonstrated that the application of classification algorithms can successfully
improve the skill of such discrete forecasts; however, a frequently emerging
issue is that certain spatial and/or temporal dependencies could be lost
between marginals. Based on visibility ensemble forecasts of the European
Centre for Medium-Range Weather Forecasts for 30 locations in Central Europe,
we investigate whether the inclusion of Copernicus Atmosphere Monitoring
Service (CAMS) predictions of the same weather quantity as an additional
covariate could enhance the skill of the post-processing methods and whether it
contributes to the successful integration of spatial dependence between
marginals. Our study confirms that post-processed forecasts are substantially
superior to raw and climatological predictions, and the utilization of CAMS
forecasts provides a further significant enhancement both in the univariate and
multivariate setup.
