Assessing the Effect of Bias Correction on GEMS Level-3 Ozone, NO₂, and AOD Products Using Polar-Orbiting Satellite References over East Asia

Abstract

As global climate change intensifies, the demand for satellite-based Level 3 data for long-term climate monitoring and analysis has been growing. However, the limited observation lifespan of a single satellite makes it difficult to ensure continuous data records suitable for climate trend analysis. Therefore, it is necessary to reconstruct climate data with multi-decadal consistency by integrating multiple generations of satellite observations. In this study, a Level 3 algorithm was developed using data from the Geostationary Environmental Monitoring Spectrometer (GEMS), which was launched in 2020. To this end, NO₂ and O₃ data retrieved from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5P satellite, and AOD data from the MODerate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites were used to correct spatiotemporal biases. The Level 3 data agreed well with both reference satellites and ground observations, and the hourly ozone data generated using the same algorithm showed strong consistency with Pandora measurements. This result confirms the stability of the bias correction approach and demonstrates its capability for analyzing diurnal variations using satellite data. Furthermore, this approach is expected to establish a foundation for producing high-resolution climate data using geostationary satellite observations and contribute to securing temporal consistency through the integration of multiple satellite datasets. This will enable continuous monitoring and reliable long-term trend analysis of air quality and climate change over East Asia.