Development of a Deep Learning-Based Weather Forecasting Model Using Short-Term Neighborhood Forecast Data

Weather forecasting plays a critical role in preventing natural disasters and improving convenience in daily life. However, traditional physics-based numerical weather prediction models have limitations in real-time and high-resolution predictions due to computational complexity and restricted computational resources. This study aims to enhance the predict skill of short-term weather forecasting by utilizing deep learning technologies. Particularly, this study attempts to seek developing methodologies to improve the skill of short-term rainfall forecasts produced by the Korea Meteorological Administration through artificial intelligence. By addressing systemic biases and errors in rainfall prediction data, this research aims to enhance predictive performance. Weather forecast data collected at 1-hour intervals—including temperature, wind speed, humidity, and precipitation—was preprocessed and used as input for the deep learning model. A deep neural network-based architecture was designed for building the forecast model. The model was trained, validated, and evaluated using data spanning the past three years. This study is expected to improve the skill of short-term weather forecasts while enhancing computational efficiency compared to conventional physics-based numerical weather prediction models. Furthermore, the proposed model demonstrates high potential for application in various fields, including disaster management, agriculture, and energy management.