Spatially and temporally coherent reconstruction of tropospheric NO₂ over China combining OMI and GOME-2B measurements

Tropospheric NO₂ columns retrieved from ozone monitoring instrument (OMI) are widely used, even though there is a significant loss of spatial coverage due to multiple factors. This work introduces a framework for reconstructing gaps in the OMI NO₂ data over China by using machine learning and an adaptive weighted temporal fitting method with NO₂ measurements from Global Ozone Monitoring Experiment–2B, and surface measurements. The reconstructed NO₂ has four important characteristics. First, there is improved spatial and temporal coherence on a day-to-day basis, allowing new scientific findings to be made. Second, the amount of data doubled, with 40% more data available. Third, the results are reliable overall, with a good agreement with Multi-AXis Differential Optical Absorption Spectroscopy measurements (R: 0.75–0.85). Finally, the mean of reconstructed NO₂ vertical columns during 2015 and 2018 is consistent with the original data in the spatial distribution, while the standard deviation decreases in most places over Mainland China. This novel finding is expected to contribute to both air quality and climate studies. (paper)