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.

中文翻译：

---

基于OMI和GOME-2的2007-2022年全球每日高时空覆盖合并对流层NO2数据集(HSTCM-NO2)

摘要。基于卫星的遥感可以提供_二氧化氮（一种重要的大气污染物）以及其他微量气体的长期、一致和全球覆盖。然而，卫星数据经常由于云、地物、气溶胶等因素而丢失数据。此外，自 2007 年以来，最长的 NO ₂太空连续观测平台之一OMI 自 2007 年以来就出现了某些行数据丢失的问题，从而大大减少了空间覆盖范围。本工作使用基于OMI的OMNO2产品以及GOME-2的NO ₂产品结合机器学习（XGBoost）和空间插值（DINEOF）方法产生16年全球每日高时空覆盖合并对流层NO ₂数据集（HSTCM-NO ₂ ，​​https://doi.org/10.5281/zenodo.10968462，Qin et al., 2024），与原始 OMINO2 数据相比， NO ₂的全球空间覆盖范围增加了约 60% 。 HSTCM-NO _{2数据集使用 NO 2} (MAX-DOAS)、其他卫星 (TROPOMI)的仰视观测和再分析产品进行验证。比较表明，除重度污染条件下（>6×10 ¹⁵ molecularc.cm ^-2）外，HSTCM-NO ₂与其他观测数据集的量值保持良好的相关性。这项工作还引入了一种新的验证技术来验证相干空间和时间信号（EOF），并验证 HSTCM-NO ₂不仅与原始 OMNO2 数据一致，而且在世界某些地区有效填补了缺失的空白和产量在分析 NO ₂的远距离大气传输时取得了优异的结果。据报道，少数差异也与原始 OMNO2 信号非常低的区域有关，这已在其他地方显示，但不是从这个角度来看，进一步验证对检索的 NO ₂数据应用最小截止值是至关重要的。重建的数据产品可以有效扩展原始OMNO2数据的利用价值，HSTCM-NO ₂的数据质量能够满足科学研究的需要。