Machine Learning Analysis of Fog and Mist Climatology at Pula Airport, Croatia

The climatological characteristics of fog and mist at Pula Airport (Croatia) in the northeastern Adriatic were examined, with a focus on applying machine learning techniques to identify large-scale atmospheric patterns associated with their occurrence. Although the published study includes conventional statistical analyses, the primary focus of this presentation is the application of the Growing Neural Gas (GNG) algorithm—an unsupervised neural network model—for clustering synoptic conditions linked to fog and mist formation.

For this study, high-resolution 10-meter wind and mean sea level pressure (MSLP) data from the ERA5 reanalysis (ECMWF) were used to represent synoptic-scale variability over multiple decades. The GNG algorithm was applied to these spatio-temporal fields to identify recurring circulation patterns and characterize their relationship with fog and mist events observed at the airport.

The GNG algorithm constructs a topological map of high-dimensional input data, dynamically adapting its structure by inserting new units in response to data complexity. This adaptability makes it particularly effective for detecting subtle, evolving patterns in atmospheric fields. In this study, GNG successfully identified pressure configurations—particularly quasi-non-gradient conditions—historically associated with fog and mist formation.

A notable result is the observed decline in the frequency of these favorable synoptic patterns, which correlates with a decreasing trend in fog and mist occurrence at the site. This trend appears to be linked to rising sea surface and near-surface air temperatures, reducing the potential for moisture transport from the sea. These findings demonstrate the value of interpretable machine learning techniques in climatological research and provide insight into ongoing changes in low-visibility weather phenomena in coastal regions.