Machine learning-based discrete post-processing of visibility ensemble forecasts

In aviation meteorology, as well as in water and road transportation, the accurate and
reliable prediction of visibility is of utmost importance. Despite various meteorological
services offering ensemble forecasts for visibility, the predictive accuracy and reliability for
this parameter are notably lower compared to variables like temperature or wind speed.
Therefore, it is strongly recommended to implement some form of calibration, typically
involving the estimation of the predictive distribution through parametric or non-parametric
methods, including machine learning techniques. The World Meteorological Organization
suggests that visibility observations should be reported in discrete values, turning the
predictive distribution into a discrete probability law. Consequently, the calibration process
can be simplified to a classification problem. This study investigates the predictive
performance of locally, semi-locally, and regionally trained proportional odds logistic
regression (POLR) and multilayer perceptron (MLP) neural network classifiers using
visibility ensemble forecasts from the European Centre for medium-range weather
forecasts. The findings reveal that while climatological forecasts surpass the raw
ensemble, post-processing leads to a substantial improvement in forecast skill. Overall,
POLR models exhibit superiority over their MLP counterparts.

Reference
Baran, S., Lakatos, M., Statistical post-processing of visibility ensemble forecasts.
Meteorol. Appl. 30 (2023), paper e2157, doi:10.1002/met.2157.

*Research is supported by the ÚNKP-23-3 New National Excellence Program of the
Hungarian Ministry for Culture and Innovation from the source of the National Research,
Development and Innovation Fund and the Hungarian National Research, Development
and Innovation Office under Grant No. K142849.