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Weather and atmospheric patterns are often persistent. The simplest weatherforecasting method is the so-called persistence model, which assumes that thefuture state of a system will be similar (or equal) to the present state.Machine learning (ML) models are widely used in different weather forecastingapplications, but they need to be compared to the persistence model to analysewhether they provide a competitive solution to the problem at hand. In thispaper, we devise a new model for predicting low-visibility in airports usingthe concepts of mixture of experts. Visibility level is coded as two differentordered categorical variables: cloud height and runway visual height. Theunderlying system in this application is stagnant approximately in 90% of thecases, and standard ML models fail to improve on the performance of thepersistence model. Because of this, instead of trying to simply beat thepersistence model using ML, we use this persistence as a baseline and learn anordinal neural network model that refines its results by focusing on learningweather fluctuations. The results show that the proposal outperformspersistence and other ordinal autoregressive models, especially for longer timehorizon predictions and for the runway visual height variable.

A mixture of experts model for predicting persistent weather patterns