Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, Jason Blake Cohen
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引用次数: 0
Abstract
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.
Earth System Science DataGEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
18.00
自引率
5.30%
发文量
231
审稿时长
35 weeks
期刊介绍:
Earth System Science Data (ESSD) is an international, interdisciplinary journal that publishes articles on original research data in order to promote the reuse of high-quality data in the field of Earth system sciences. The journal welcomes submissions of original data or data collections that meet the required quality standards and have the potential to contribute to the goals of the journal. It includes sections dedicated to regular-length articles, brief communications (such as updates to existing data sets), commentaries, review articles, and special issues. ESSD is abstracted and indexed in several databases, including Science Citation Index Expanded, Current Contents/PCE, Scopus, ADS, CLOCKSS, CNKI, DOAJ, EBSCO, Gale/Cengage, GoOA (CAS), and Google Scholar, among others.