Bias‐Corrected GEMS Total Column Ozone Dataset Over East Asia for Long‐Term Analysis

ABSTRACT This study developed the GEMS Level‐3 GT‐O3T (GEMS TROPOMI‐referenced Total Column Ozone) algorithm to construct a consistent long‐term ozone dataset by correcting GEMS biases using TROPOMI as a reference. The algorithm generates daily 0.25° gridded TROPOMI and GEMS total ozone datasets and applies monthly ratio‐based correction factors to minimise spatiotemporal discrepancies. Compared with TROPOMI, the RMSE decreased from 14.29 DU to 3.83 DU in 2023 and from 15.63 DU to 4.35 DU in 2024. The systematic underestimation observed in the original GEMS product, particularly over mid‐ and high‐latitude regions and across seasonal cycles, was significantly mitigated. Validation with Pandora measurements further demonstrated the effectiveness of the correction, with the mean bias error (MBE) decreasing from 12.21 DU to −2.32 DU. OMI, TROPOMI, and corrected GEMS data based on the GT‐O3T algorithm were then combined to create a merged record covering 2005–2024. The merged dataset closely reproduced Dobson observations with enhanced temporal continuity and reduced regional bias, particularly after 2023 when GEMS data were included.