Tropospheric NO₂ retrieval algorithm for geostationary satellite instruments: applications to GEMS

Abstract. In this study, we develop an advanced retrieval algorithm for tropospheric nitrogen dioxide (NO₂) from the geostationary satellite instruments and apply it to Geostationary Environment Monitoring Spectrometer (GEMS) observations. Overall, the algorithm follows previous heritage for the polar orbiting satellites GOME-2 and TROPOMI, but several improvements are implemented to account for specific features of geostationary satellites.

The DLR GEMS NO₂ retrieval employs an extended fitting window compared to the current fitting window used in GEMS operational v2.0 NO₂ retrieval, which results in improved spectral fit quality and lower uncertainties. For the stratosphere-troposphere separation in GEMS measurements, two methods are developed and evaluated: (1) STRatospheric Estimation Algorithm from Mainz (STREAM) as used in the DLR TROPOMI NO₂ retrieval and adapted to GEMS, and (2) estimation of stratospheric NO₂ columns from the Copernicus Atmosphere Monitoring Service (CAMS) forecast Cy48R1 model data, which introduce full stratospheric chemistry as it will be used in the operational Sentinel-4 NO₂ retrieval. While STREAM provides hourly estimates of stratospheric NO₂, it has limitations in describing small-scale variations and exhibits systematic biases near the boundary of the field of view. In this respect, the use of estimated stratospheric NO₂ columns from the CAMS forecast model profile demonstrates better applicability by describing not only diurnal variation but also small-scale variations.

For the improved air mass factor (AMF) calculation, sensitivity tests are performed using different input data. In our algorithm, cloud fractions retrieved from the Optical Cloud Recognition Algorithm (OCRA) adapted to GEMS level 1 data are applied instead of GEMS v2.0 cloud fraction. OCRA is used operationally in TROPOMI and Sentinel-4. Compared to GEMS level 2 cloud fraction which is typically set to around 0.1 for clear-sky scenes, OCRA sets cloud fractions close or at 0. The OCRA-based cloud corrections result in increased tropospheric AMFs and decreased tropospheric NO₂ vertical columns, leading to better agreement with results from existing TROPOMI observations. The effects of surface albedo on GEMS tropospheric NO₂ retrievals are assessed by comparing the GEMS v2.0 background surface reflectance (BSR) and TROPOMI Lambertian-equivalent reflectivity (LER) climatology v2.0 product. The differences between the two surface albedo products and their impact on tropospheric AMF are particularly pronounced over snow/ice scenes during winter. A priori NO₂ profiles from the CAMS forecast model, applied in the DLR GEMS algorithm, effectively capture variations in NO₂ concentrations throughout the day with high spatial resolution and advanced chemical mechanism, which demonstrates its suitability for geostationary satellite measurements.

The retrieved DLR GEMS tropospheric NO₂ columns show good capability to capture hotspot signals at the scale of city clusters and describe spatial gradients from city centers to surrounding areas. Diurnal variations of tropospheric NO₂ columns over Asia are well described through hourly sampling of GEMS. Evaluation of DLR GEMS tropospheric NO₂ columns against TROPOMI v2.4 and GEMS v2.0 operational products show overall good agreement. The uncertainty of DLR GEMS tropospheric NO₂ vertical columns varies based on observation scenarios. In regions with low pollution levels such as open ocean and remote rural areas, retrieval uncertainties typically range from 10 % to 30 %, primarily due to uncertainties in slant columns. For heavily polluted regions, uncertainties in tropospheric NO₂ columns are mainly driven by errors in tropospheric AMF calculations. Notably, the total uncertainty in GEMS tropospheric NO₂ columns is most significant in winter, particularly over heavily polluted regions with low-level clouds below or near the NO₂ peak.