Evaluation of GEMS NO ₂ Retrieval Algorithm Version 2.0 and 3.0 Using TROPOMI and Pandora Observations

The Geostationary Environment Monitoring Spectrometer (GEMS), onboard the Geostationary Korea Multi-Purpose Satellite-2B (GEO-KOMPSAT-2B) satellite, provides hourly measurements of nitrogen dioxide (NO) over East Asia, enabling continuous monitoring of air quality and pollution transport.To ensure data reliability, the GEMS NO retrieval algorithm has undergone iterative improvements.This study presents a comprehensive comparison and evaluation of the GEMS NO Version 2.0 (V2.0) and Version 3.0 (V3.0) data products using independent satellite (TROPOspheric Monitoring Instrument, TROPOMI) and ground-based Pandora observations.V3.0 introduces key updates, including revised a priori NO vertical profiles, revised tropopause pressure, recalculated air mass factors (AMFs), and updated auxiliary inputs such as aerosol optical depth, cloud, ozone, and surface reflectance.Pixel-by-pixel intercomparison between V2.0 and V3.0 shows strong overall correlation, while regression slopes below unity indicate reduced total NO columns in V3.0 due to the removal of the overestimated tropospheric signal.Stratospheric columns increased substantially, correcting the unrealistic underestimation observed in V2.0 and yielding more physically consistent vertical distributions.Comparison with TROPOMI demonstrates that V3.0 reduces systematic overestimation over industrial regions such as northeastern China and the Korean Peninsula, achieving improved spatial agreement and higher correlations.Validation with Pandora measurements across six East Asian sites (Beijing, Seoul, Busan, Bangkok, Tsukuba, and Tokyo) confirms that V3.0 decreases regional biases and root-mean-square errors (RMSE) by approximately 20 %, with enhanced correlation coefficients.Overall, the algorithmic refinements in GEMS NO V3.0 lead to improved accuracy, stability, and physical realism in both tropospheric and stratospheric retrievals.The results demonstrate the enhanced capability of GEMS for diurnal and seasonal monitoring of air quality across East Asia, supporting its use for emission estimation, pollution transport analysis, and long-term trend assessment.