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

IF 11.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY
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.
基于 OMI 和 GOME-2 的 2007-2022 年全球每日高时空覆盖对流层 NO2 合并数据集 (HSTCM-NO2)
摘要基于卫星的遥感可提供长期、一致和全球覆盖的二氧化氮(一种重要的大气污染物)以及其他痕量气体数据。然而,卫星数据经常会因包括但不限于云层、地表特征和气溶胶等因素而丢失数据。此外,作为从太空观测二氧化氮的持续时间最长的观测平台之一,OMI 自 2007 年以来一直存在某些行数据缺失的问题,大大缩小了空间覆盖范围。这项工作利用基于 OMI 的 OMNO2 产品以及来自 GOME-2 的 NO2 产品,结合机器学习(XGBoost)和空间插值(DINEOF)方法,生成了一个 16 年全球每日高时空覆盖率合并对流层 NO2 数据集(HSTCM-NO2,https://doi.org/10.5281/zenodo.10968462,Qin 等,2024 年),与原始 OMINO2 数据相比,NO2 的全球空间覆盖率提高了约 60%。HSTCM-NO2 数据集利用 NO2 的上视观测数据(MAX-DOAS)、其他卫星(TROPOMI)和再分析产品进行了验证。比较结果表明,除了在严重污染条件下(6×1015 摩尔/厘米-2),HSTCM-NO2 与其他观测数据集的大小保持着良好的相关性。这项工作还引入了一种新的验证技术来验证相干的空间和时间信号(EOF),并验证了 HSTCM-NO2 不仅与原始 OMNO2 数据一致,而且在世界某些地区有效地填补了缺失的空白,在分析 NO2 的长程大气传输时产生了更优越的结果。据报道,少数差异也与原始 OMNO2 信号非常低的地区有关,这在其他地方也有显示,但从这个角度看却没有,这进一步验证了对检索的 NO2 数据应用最小截止值是至关重要的。重建后的数据产品可有效扩展原始 OMNO2 数据的利用价值,HSTCM-NO2 的数据质量可满足科学研究的需要。
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来源期刊
Earth System Science Data
Earth System Science Data GEOSCIENCES, 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.
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