Station-data-driven temperature forecasting in South Korea using convolutional neural networks

Numerical weather prediction (NWP) has the drawback of high computational costs, prompting researchers to explore deep learning for weather forecasting. One specific area under investigation is temperature prediction, for which diverse outcomes were produced depending on region, grid resolution, and deep learning algorithms. In this study, we developed station-observed temperature predictions using convolutional neural networks. Our approach involves two main steps: transforming irregularly distributed station temperatures to data on the regular meshes and processing mesh data using convolutional neural networks. The first step corrects observed temperatures through "mean decomposition" taking into account the yearly and daily variations and "height adjustment" through potential temperature. Next, training is performed using mesh data, with a network designed to predict temperature at time t+h based on data at time t. Our model was benchmarked against climatology, persistence models, and traditional NWP. The results demonstrate the superiority of our proposed approach, outperforming NWP up to 12 hours of lead time.