Harmonized tropospheric NO2 column monitoring for LEO and GEO constellations

Abstract

Over the past few decades, the global distribution and trends of atmospheric NO2 have been monitored from space by low-earth-orbiting (LEO) satellite instruments, such as the Global Ozone Monitoring Experiment (GOME), SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY), Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment-2 (GOME-2), and Tropospheric Monitoring Instrument (TROPOMI). These LEO measurements have significantly enhanced our understanding of global tropospheric NO2 levels and their long-term trends, but are limited to provide observations only once per day at specific local times. To address this limitation, a constellation of geostationary orbiting satellite sensors (GEO) is being deployed, including the Geostationary Environment Monitoring Spectrometer (GEMS) over Asia, Tropospheric Emissions: Monitoring of Pollution (TEMPO) over North America, and two Sentinel-4 (S4) missions over Europe. Although the GEO monitoring system has limited spatial coverage, it provides multiple observations per day, enabling detailed monitoring of diurnal variations in NO2 due to changes in emissions and chemical reactions throughout the day. As described, LEO (global daily monitoring) and GEO (regional hourly monitoring) each have distinct advantages and limitations. Therefore, by complementing each other and combining their strengths, a synergetic effect can be achieved in monitoring atmospheric compositions including NO2. However, obtaining consistent data on total, stratospheric and tropospheric NO2 columns from LEO and GEO satellite missions remains challenging since their current operational algorithms use different retrieval approaches (e.g. stratosphere-troposphere separation, cloud corrections) and auxiliary datasets. In this study, we aim to develop a harmonized retrieval system for tropospheric NO2 columns applicable to both LEO and GEO satellite measurements. The DLR NO2 retrieval algorithm for LEO and GEO constellations (NO2_LGC) consists of three key steps: (1) the spectral retrieval of total NO2 slant columns, (2) the separation of slant columns into stratospheric and tropospheric contributions using the Copernicus Atmosphere Monitoring Service (CAMS) forecast model data, and (3) the conversion of tropospheric slant columns into vertical columns using air mass factors (AMFs). Based on Seo et al. (2024), a consistent retrieval approach is applied across current LEO (e.g. TROPOMI) and GEO (e.g. GEMS and TEMPO) observations, using identical forecast model, cloud corrections, and auxiliary data. This harmonized retrieval algorithm for the LEO+GEO constellation provides more consistent NO2 data records from TROPOMI, GEMS and TEMPO and will be extended to future GEO and LEO missions including Sentinel-4 and Sentinel-5. In addition, we demonstrate the advantages of harmonized LEO and GEO NO2_LGC retrievals, particularly for analyzing NO2 in regions with high pollution levels and downwind pollution outflows.