Improved NO2 column retrievals for geostationary satellites: application to GEMS and TEMPO

Abstract

In this study, we address key challenges in NO2 retrievals from geostationary earth orbit (GEO) satellite observations through two major methodological improvements: (1) enhanced NO2 slant column retrieval techniques and (2) refined stratosphere–troposphere separation methods. The improved retrieval algorithm is applied to observations from the GEMS and TEMPO mission. For NO2 slant column retrievals, we perform a round-robin intercomparison of several approaches, including classical Differential Optical Absorption Spectroscopy (DOAS), covariance-based DOAS, DOAS combined with Principal Component Analysis (PCA), and a hybrid PCA+Covariance-based DOAS method (XDOAS). These advanced techniques are designed to mitigate striping artifacts and improve retrieval accuracy in spatially inhomogeneous scenes, addressing a key limitation of GEO observations, which often lack continuous coverage of clean reference regions. For stratosphere–troposphere separation, challenges arising from limited clean reference areas and pronounced diurnal variability are investigated using two approaches: an advanced reference-sector method and stratospheric NO2 fields derived from forecasts of the Copernicus Atmosphere Monitoring Service (CAMS). The enhanced retrieval algorithm is applied to GEMS and TEMPO observations and evaluated against the corresponding operational products (GEMS v4.0.1 and TEMPO v4.0), as well as NO2 measurements from TROPOMI. Results show that the proposed methodology effectively addresses challenges associated with high-frequency GEO observations and the limited availability of clean background sectors, leading to improved retrieval accuracy and reduced uncertainties. The improved GEO NO2 products strengthen the interpretation of atmospheric chemistry facilitating a more reliable characterization of air pollution evolution and diurnal variability in air quality.