AI-Driven Forecasting for Morning Fog Expansion (Sea of Clouds)

Abstract

Abstract This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine-learning technique, the gradient boosting method, was adopted as the AI algorithm. The Miyoshi Basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October, November, and December 2018–2021. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high Area Under the Curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dew-point temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE.