A real-time, high-resolution framework for nationwide hourly ozone prediction in South Korea using integrated satellite, meteorological, and surface observations

Accurate real-time prediction of surface ozone at high spatial and temporal resolution is critical for air-quality management, exposure assessment, and public health protection. However, developing an operational hourly ozone prediction system at the national scale remains challenging due to the need for continuous data acquisition, integration of heterogeneous data sources, and computationally efficient modeling frameworks. This study presents a real-time, data-driven framework for high-resolution hourly ozone prediction across South Korea by constructing an automated, nationwide atmospheric database that integrates satellite, meteorological, and ground-based observations.

We establish an operational data pipeline that collects and processes near-real-time meteorological observations from the Korea Meteorological Administration’s Operational Data Assimilation and Model (ODAM), atmospheric information derived from the GK2A geostationary satellite, and surface ozone measurements from the AIRKOREA monitoring network. All data streams are ingested on an hourly basis and systematically harmonized through temporal synchronization and spatial alignment to generate high-resolution predictors covering the entire Korean Peninsula. This integrated database enables consistent representation of rapidly evolving meteorological conditions, atmospheric composition, and surface-level air quality.

Using the constructed real-time database, we develop a data-driven prediction model to estimate hourly surface ozone concentrations, with AIRKOREA observations used as the target variable. The modeling framework is designed with operational feasibility in mind, supporting continuous updates, automated preprocessing, and near-real-time inference without reliance on computationally expensive chemical transport models. The resulting system provides high-resolution ozone predictions that capture fine-scale spatiotemporal variability at the national level.

Model evaluation demonstrates that integrating geostationary satellite data with real-time meteorological and surface observations substantially enhances the prediction of hourly ozone variability compared to single-source or static-input approaches. The proposed framework highlights the advantages of real-time, high-resolution, and nationwide data integration for operational ozone forecasting in South Korea. Beyond ozone, this scalable and extensible system provides a foundation for real-time prediction of additional atmospheric pollutants and supports the development of next-generation data-driven air-quality forecasting services.