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

Kurzfassung

In this study, we develop an advanced retrieval algorithm for tropospheric NO2 columns from geostationary satellite spectrometers, and apply it to Geostationary Environment Monitoring Spectrometer (GEMS) measurements. The DLR GEMS NO2 retrieval algorithm follows the heritage from previous and existing algorithms used for GOME-2 and TROPOMI instruments, but improved approaches are applied to reflect the specific features of geostationary satellites, such as high temporal samplings, limited range of spatial coverage, larger zenith angles and high spatial resolution. To estimate the stratospheric contribution and describe the diurnal variation of stratospheric fields, an improved stratosphere-troposphere separation approach is developed using the CAMS global forecast (IFS cycle 48r1) model data and evaluated by comparing it to results obtained using the STREAM scheme. For the improved tropospheric air mass factor (AMF) calculation, sensitivity tests are performed using different ancillary data inputs. Notably, a cloud correction using cloud fractions retrieved from the DLR Optical Cloud Recognition Algorithm (OCRA) based on Loyola et al. (2018) improves the tropospheric NO2 column retrievals for clear-sky scenes. The a priori NO2 profiles from the CAMS forecast model, applied in the DLR GEMS algorithm, effectively capture variations in NO2 concentrations depending on emission patterns and meteorological conditions throughout the day with a high spatial and temporal resolution. 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. The hourly sampling and high spatial resolution of GEMS tropospheric NO2 columns demonstrate the capability for a detailed analysis of the diurnal evolution of NO2 burden and emission strengths over Asia from space. This retrieval algorithm can be easily adapted to other geostationary satellite instruments, such as TEMPO and Sentinel-4, enabling hourly monitoring and analysis across each continent in the future.