# Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to Tropopmi
## 3 Results and Validation
### Tropospheric Vcd  
Figure 7 presents the tropospheric NO\({}_{2}\) VCD over region 3 on 6 July 2022 (top), the weekly average of 16-22 July 2022 (middle), and the monthly average of 27 June-27 July 2022 (bottom). The first, second, and third columns show the spatial distributions from the GF5B and DQ1 EMI-II and TROPOMI. The fourth column illustrates the fit between the EMI-II and TROPOMI data. On the daily time scales, the correlation coefficient (R\({}^{2}\)) between the EMI-II and TROPOMI spatial distributions exceeds 0.57. On a weekly average time scale, the R\({}^{2}\) is greater than 0.72. On a monthly average time scale, the R\({}^{2}\) is greater than 0.78. The high correlation between the EMI-II and TROPOMI in both the spatial distribution and temporal variation indicates that the EMI-II has similar capabilities to those of the TROPOMI in characterizing the spatial and temporal features of tropospheric NO\({}_{2}\). The tropospheric NO\({}_{2}\) results are slightly higher than those of the TROPOMI. The uncertainties associated with the retrieval of tropospheric NO\({}_{2}\) primarily arise from three factors: (a) the uncertainty in the total VCD, (b) the uncertainty in stratosphere-troposphere separation, and (c) the uncertainty in calculating the tropospheric AMF. While the uncertainty of the total VCD has been analyzed in Section 3.4, this section will concentrate on the analysis of the latter two aspects. In the STREAM STS algorithm, three critical weighting factors influence the uncertainty of the results. First, the calculation of \"pollution weight\" relies on the EMI total VCD results and the multi-year monthly averaged climatological tropospheric NO\({}_{2}\) dataset from the TROPOPOMI. Utilizing the climatological dataset from the EMI can improve the accuracy of the \"pollution weight\". Second, the calculation of \"cloud weight\" employs the TROPOMI cloud fraction and cloud pressure data. Due to differences in the spatial resolution, a single EMI pixel may encompass multiple TROPOMI pixels. The development of dedicated EMI  
Figure 6: Seasonal averages of the GF5B and DQ1 EMI-II NO\({}_{2}\) VCDs in four regions with TROPOMI. cloud products is expected to improve the accuracy of \"cloud weight\" calculations. Finally, the calculation of the \"tropospheric residual weight\" requires a \"latitudinally corrected\" initial stratospheric concentration, which may introduce unquantifiable uncertainties. The parameters utilized in the calculation of the tropospheric AMF include geometric parameters, surface parameters, NO\({}_{2}\) profiles, and aerosol and cloud parameters. The measurement geometry is well defined and, thus, does not significantly contribute to AMF errors. Surface parameters and NO\({}_{2}\) profiles are sourced from the GEOS-CF CTM; therefore, the error introduced is negligible. Consequently, the tropospheric AMF primarily depends on aerosol characteristics and cloud cover. The radiative transfer simulations conducted by [PERSON] et al. (2004) [PERSON] et al. (2004) demonstrated that cloud parameter retrievals are highly sensitive to aerosol presence. Compared to a purely molecular scattering atmosphere, higher cloud radiative fractions and lower cloud pressures are observed when aerosols are present [PERSON] et al. (2004). They also highlighted a strong agreement between explicit aerosol corrections and corrections made through cloud parameters. Aerosol events with high concentrations are rare on the global scale. In China, the spatiotemporal distribution of aerosols has been extensively studied. High levels of aerosol pollution are concentrated in the North China Plain, Weihe Plain, Chengdu-Chongqing region, and southern Xinjiang, primarily driven by urban activities and dust transport. As a study on the retrieval algorithm widely used for global NO\({}_{2}\) monitoring, this paper does not directly use aerosol parameters when calculating the tropospheric AMF, but instead indirectly considers the impact of aerosols through cloud parameters.