Preprints
https://doi.org/10.5194/essd-2024-146
https://doi.org/10.5194/essd-2024-146
14 May 2024
 | 14 May 2024
Status: this preprint is currently under review for the journal ESSD.

A Global Daily High Spatial-temporal Coverage Merged Tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2

Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, and Jason Blake Cohen

Abstract. Remote sensing based on satellites can provide long-term, consistent, and global coverage of NO2 (an important atmospheric air pollutant) as well as other trace gases. However, satellite data often miss data due to factors including but not limited to clouds, surface features, and aerosols. Moreover, one of the longest continuous observational platforms of NO2 observations from space, OMI, has suffered from missing data over certain rows since 2007, significantly reducing spatial coverage. This work uses the OMI based OMNO2 product, as well as an NO2 product from GOME-2 in combination with machine learning (XGBoost) and spatial interpolation (DINEOF) method to produce a 16-year global daily high spatial-temporal coverage merged tropospheric NO2 dataset (HSTCM-NO2, https://doi.org/10.5281/zenodo.10968462, Qin et al., 2024), which increases the global spatial coverage of NO2 by ~60 % compared to the original OMINO2 data. The HSTCM-NO2 dataset is validated using upward looking observations of NO2 (MAX-DOAS), other satellites (TROPOMI), and reanalysis products. The comparisons show that HSTCM-NO2 maintains a good correlation with the magnitude of other observational datasets, except for under heavily polluted conditions (>6×1015 molec.cm-2). This work also introduces a new validation technique to validate coherent spatial and temporal signals (EOF) and validates that the HSTCM-NO2 are not only consistent with the original OMNO2 data, but in some parts of the world effectively fill in missing gaps and yield a superior result when analyzing long-range atmospheric transport of NO2. The few differences are also reported to be related to areas in which the original OMNO2 signal was very low, which has been shown elsewhere, but not from this perspective, further validating that applying a minimum cutoff to retrieved NO2 data is essential. The reconstructed data product can effectively extend the utilization value of the original OMNO2 data, and the data quality of HSTCM-NO2 can meet the needs of scientific research.

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Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, and Jason Blake Cohen

Status: open (until 29 Jun 2024)

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Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, and Jason Blake Cohen

Data sets

A Global Daily High Spatial-temporal Coverage Merged Tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2 Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, and Jason Blake Cohen https://doi.org/10.5281/zenodo.10968462

Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, and Jason Blake Cohen

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Short summary
Satellites have brought new opportunities for monitoring atmospheric NO2, although the results are limited by clouds and other factors, resulting in missing data. This work compensates proposes a new process to obtain reliable data products with high coverage by reconstructing the raw data from multiple satellites. The results are validated in terms traditional methods as well as variance-maximization, and demonstrate a good ability to reproduce known polluted and clean areas around the world.
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