## 7 Conclusions  
In this study, an UV-visible algorithm to provide AOD and SSA simultaneously was developed using the OE method. The AOD and SSA were estimated based on an ALH optimized to fit the calculated spectrum to observations. Because of the large spatial and temporal variations of aerosol properties, a priori states for AOD and SSA were retrieved for six heights by using a two-channel inversion method. Then, these a priori states were used to solve the Levenberg Marquardt equation. To analyze the performance of the algorithm, retrieved AOD and SSA were compared with AERONET values for 3 years from 2005 to 2007. The retrieved AOD showed reasonable agreement with observations, with a correlation coefficient of 0.71. However, a regression slope of 0.56 with y-intercept of 0.24 and a general overestimation of SSA for HAF aerosols indicate that the AAE is typically overestimated. In addition, the ALH showed good agreement with the CALIOP \(Z_{\text{aer}}\) when the AOD (at 443 nm) is higher than 0.8 and the SSA (at 443 nm) is lower than 0.93. The reference test (see Section 5) revealed that the inversion errors in AOD and SSA are negligible, regardless of errors in a priori assumptions for ALH but the error in ALH due to errors in a priori assumptions increased from 0.02 to 1.81 km when the reference ALH was 1.0 km, under NA-aerosol conditions. To reduce this uncertainty, a yearly-averaged ALH from CALIOP level 2 products was adopted as an a priori ALH. Uncertainties in model parameters such as aerosol type and surface reflectance caused significant errors in AOD, SSA and ALH. The underestimation of surface reflectance resulted in an overestimation of AOD, an overestimation of SSA and an underestimation of ALH. When the surface reflectance error was \(-40\%\), the median error reached 0.52 for AOD, \(-0.012\) for SSA and \(-1.18\) km for ALH. The error caused by the misclassification of aerosols was more significant than that of surface reflectance. Uncertainty in AAE affected the estimated spectral distribution of radiance, which led to spectral fitting errors. In general, the overestimation of AAE resulted in an underestimation of AOD, overestimation of SSA and overestimation of ALH. It should be noted that conditions exist that do not follow the general relationship between retrieval error and uncertainty in model parameters. The relationship is not linear because each state vector simultaneously contributes to the radiance spectrum and retrieval errors from other sources can cancel out the effects of errors in model parameters. Cloud contamination error is another contributor to retrieval errors and climatological surface reflectance may contain uncertainties that result in the underestimation of AOD. Despite these uncertainties, the UV algorithm has demonstrated the ability to retrieve aerosol absorptivity, which plays a crucial role in the radiative forcing of aerosols and thus climate change. The estimation of AOPs over Asia can contribute to an understanding of the role of Asian aerosols in climate change, as well as direct effects on public health. Results will contribute to the estimate of aerosol effects on regional climate change and improve air quality forecasting via a data assimilation system. An improvement in results from the UV algorithm is expected once GEMS measurements are available, as these will have a finer spatial resolution (3.5 km (NS) \(\times\) 8 km (EW) at Seoul). Furthermore, continuous monitoring of aerosol properties together with their precursor measurements can be expanded to the global scale by combining the measurements of the NASA TEMPO and the ESA Sentinel-4, which have specifications similar to those of GEMS. The GEO constellation of GEMS, TEMPO and Sentinel-4 will significantly improve the understanding of air quality over the Northern Hemisphere.