Over 30 Years of Artificial Intelligence Use in Meteorology in Slovenia

In recent years, artificial intelligence has been widely used in the field of meteorology. However, it is perhaps less known that AI-based tools have been used for over 30 years in the modeling of pollutant dispersion in the atmosphere.

In the early 1990s, Slovenia and other parts of Europe faced significant air pollution problems caused by SO₂ emissions from coal-fired power plants without desulfurization systems. Due to the highly complex terrain, simple physical Gaussian dispersion models were not suitable for reconstructing the dispersion of SO₂ in the atmosphere, while complex numerical Lagrangian particle dispersion models were still in development.

Therefore, as early as 1992, we modeled air pollution using a Multilayer Perceptron Artificial Neural Network (MLPANN). We developed the world's first comprehensive method for selecting training samples, features, and the topology of the neural network, enabling the model to learn from measured meteorological variables and pollutant concentrations around the power plant to predict SO₂ concentrations at a selected location in advance.

This type of artificial neural network remains the foundation for various derived structures used for machine learning from data even today.

In the following decades, we expanded the use of MLPANN to ozone prediction. Researchers from other Slovenian research groups also worked on this topic. Meanwhile, the use of these and similar tools in pollutant dispersion modelling began to grow on a global scale.

Together with Brazilian researchers, we successfully applied MLPANN for predicting diffuse solar radiation. In this field, we also managed to develop a spatially transferable model, which is not a common capability for artificial neural network-based models.

For the classification of wind fields based on measurements from ground-based meteorological stations, we used a Kohonen neural network.

Later, we added another tool called "Gaussian processes" (which, like MLPANN, is a universal approximator) and the tool "decision trees." We expanded the use to point-based forecasting of basic meteorological variables. In recent years, with high computational capabilities available even without supercomputers, we have been using these tools for surrogate models that can represent or predict pollutant concentration fields in the atmosphere, not only for individual points but for entire areas around pollution sources.

Other Slovenian groups from Slovenian Meteorological Agency, the Department of Meteorology, and the Faculty of Computer Science have, in recent years, used machine learning for global medium-range forecasts of daily averages of meteorological variables and for post-processing weather forecasts from physical models.

In the presentation, we will explain the basic principles of building models based on artificial neural networks in a way that is understandable to laypeople. For experts in the field, we will showcase numerous examples of their application. For the latter group, we hope these examples will inspire others to expand the use of these modern tools even further.