Work

TROPOMI Level 3 tropospheric NO 2 dataset with advanced uncertainty analysis from the ESA CCI+ ECV precursor project

Year: 2025

Type: article

Abstract: . We introduce the new ESA Climate Change Initiative TROPOspheric Monitoring Instrument (TROPOMI) global monthly Level 3 (L3) dataset of tropospheric nitrogen dioxide (NO2) for May 2018 to December 2021. The dataset provides spatiotemporally averaged tropospheric NO2 columns, associated averaging kernels and L3 uncertainties at spatial resolutions of 0.2°, 0.5°, and 1.0° on a monthly timescale (https://doi.org/10.21944/CCI-NO2-TROPOMI-L3). To improve our understanding of what fraction of the L2 uncertainty cancels when averaging over space or time (i.e. the random component of the L2 uncertainty) and what fraction persists despite the averaging (systematic component), we first determine spatial and temporal error correlations for all sources of uncertainty in the L2 retrieval. Spatial error correlations arise from the stratosphere-troposphere correction, and from coarse-gridded albedo climatologies used in the L2 air mass factor calculation. We find the temporal error correlation in both the stratospheric uncertainty and the air-mass factor uncertainty to be 30 %. Using these estimates, the L3 uncertainty budget has been established for every grid cell based on input L2 uncertainties and new methods to estimate spatial and temporal representativeness uncertainties and to propagate measurement uncertainties through space and time. The total relative uncertainty in the resulting Level 3 dataset is in the range of 15–20 % in polluted areas, which is significantly lower than in separate Level 2 orbit retrievals, and brings the tropospheric NO2 data to within the GCOS "goal" and "breakthrough" requirements. Validation of the (sub-)columns confirms better correlation and reduced dispersion in the differences between satellite and ground-based reference data for the L3 data w.r.t. the underlying L2, albeit with a more pronounced negative bias in the tropospheric columns at pollution hot spots, most probably related to stronger spatial smearing.

Source: Earth system science data

DOI: 10.5194/essd-17-4627-2025