Improving Visibility Forecasts in Denmark Using Machine Learning Post-processing

Abstract: Accurate fog prediction is an important task facing forecast centers since low visibility can affect anthropogenic systems, such as aviation. Therefore, this study investigates the use of Machine Learning classification algorithms for post-processing the output of the Danish Meteorological Institute’s operational Numerical Weather Prediction (NWP) model to improve visibility prediction. Two decision tree ensemble methods, XGBoost and Random Forest, were trained on more than 4 years of archived forecast data and visibility observations from 28 locations in Denmark. Observations were classified into eight classes, while models were optimized with resampling and Bayesian optimization. On an independent 15-month period, the Machine Learning methods show an improvement in balanced accuracy, F1-score, and Extremal Dependency Index compared to the NWP and persistence models. XGBoost performs slightly better. However, both methods suffer from an increase in overprediction of the low visibility classes. The models are also discussed regarding usability, coping with model changes and preservation of spatial features. Finally, the study shows how the interpretation of the post-processing models may be included operationally. Future research recommendations include incorporating more variables, using alternative class imbalance methods and further analyzing the models’ implementation and usage. Overall, the study demonstrates the potential of these models to improve visibility point forecasts in an operational setting.