Based on an optimal estimation method, an algorithm was developed to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO₂) using Shortwave Infrared (SWIR) channels, referred to as the Yonsei CArbon Retrieval (YCAR) algorithm. The performance of the YCAR algorithm is here examined using simulated radiance spectra, with simulations conducted using different Aerosol Optical Depths (AODs), Solar Zenith Angles (SZAs) and aerosol types over various surface types. To characterize the XCO₂ retrieval algorithm, reference tests using simulated spectra were analysed through a posteriori XCO₂ retrieval errors and averaging kernels. The a posteriori XCO₂ retrieval errors generally increase with increasing SZA. However, errors were found to be small (<1.3 ppm) over vegetation surfaces. Column averaging kernels are generally close to unity near the surface and decrease with increasing altitude. For dust aerosol with an AOD of 0.3, the retrieval loses its sensitivity near the surface due to the influence of atmospheric scattering, with the peak of column averaging kernels at ~800 hPa. In addition, we performed a sensitivity analysis of the principal state vector elements with respect to XCO₂ retrievals. The reference tests with the inherent error of the algorithm showed that overall XCO₂ retrievals work reasonably well. The XCO₂ retrieval errors with respect to state vector elements are shown to be <0.3 ppm. Information on aerosol optical properties is the most important factor affecting the XCO₂ retrieval algorithm. Incorrect information on the aerosol type can lead to significant errors in XCO₂ retrievals of up to 2.5 ppm. The XCO₂ retrievals using the Thermal and Near-infrared Sensor for carbon Observation (TANSO)-Fourier Transform Spectrometer (FTS) L1B spectra were biased by 2.78 ± 1.46 ppm and 1.06 ± 0.85 ppm at the Saga and Tsukuba sites, respectively. This study provides important information regarding estimations of the effects of aerosol properties on the CO₂ retrieval algorithm. An understanding of these effects can contribute to improvements in the accuracy of XCO₂ retrievals, especially combined with an aerosol retrieval algorithm.

The spatio-temporal characteristics of the clearness index (K_T) were investigated using daily global solar irradiance measurements (290–2800 nm) for the period of 2000–2014 at 21 sites in Korea, a complex region in East Asia with a distinct monsoon season and heavy aerosol loading year-round. The annual mean K_T value for all sites is 0.46, with values of 0.63 and 0.25 for clear and overcast skies, respectively. The seasonal variations in monthly average K_T show a minimum of 0.37 in July at all sites except for Jeju, where the value was 0.29 in January. The maximum value (K_T = 0.51) is observed in October, followed by a secondary peak (K_T = 0.49) during February–April. The lowest K_T value (K_T = 0.42) was observed at both the Seoul and Jeju sites, and the highest (K_T = 0.48) in the southeastern regions. Increases in average K_T exceeding 4% per decade were observed in the middle and southeastern regions, with the maximum (+8% per decade) at the Daegu site. Decreasing trends (<−4% per decade) were observed in the southwestern regions, with the maximum (−7% per decade) at the Mokpo site. Cloud amount, relative humidity, and aerosol optical depth together explained 57% of the variance in daily mean K_T values. The contributions of these three variables to variations in K_T are 42%, 9% and 6%, respectively. Thus, the variations in K_T in Korea can be primarily attributed to the presence of clouds and water vapor, with relatively weak aerosol effects.

In East Asia, where aerosol concentrations are persistently high throughout the year, most satellite CO₂ retrieval algorithms screen out many measurements during quality control in order to reduce retrieval errors. To reduce the retrieval errors associated with aerosols, we have modified YCAR (Yonsei Carbon Retrieval) algorithm to YCAR-CAI to retrieve X_CO2 from GOSAT FTS measurements using aerosol retrievals from simultaneous Cloud and Aerosol Imager (CAI) measurements. The CAI aerosol algorithm provides aerosol type and optical depth information simultaneously for the same geometry and optical path as FTS. The YCAR-CAI X_CO2 retrieval algorithm has been developed based on the optimal estimation method. The algorithm uses the VLIDORT V2.6 radiative transfer model to calculate radiances and Jacobian functions. The X_CO2 results retrieved using the YCAR-CAI algorithm were evaluated by comparing them with ground-based TCCON measurements and current operational GOSAT X_CO2 retrievals. The retrievals show a clear annual cycle, with an increasing trend of 2.02 to 2.39 ppm per year, which is higher than that measured at Mauna Loa, Hawaii. The YCAR-CAI results were validated against the Tsukuba and Saga TCCON sites and show an root mean square error of 2.25, a bias of −0.81 ppm, and a regression line closer to the linear identity function compared with other current algorithms. Even after post-screening, the YCAR-CAI algorithm provides a larger dataset of X_CO2 compared with other retrieval algorithms by 21% to 67%, which could be substantially advantageous in validation and data analysis for the area of East Asia. Retrieval uncertainty indicates a 1.39 to 1.48 ppm at the TCCON sites. Using Carbon Tracker-Asia (CT-A) data, the sampling error was analyzed and was found to be between 0.32 and 0.36 ppm for each individual sounding.

Nitrogen oxides (NO_x) are key precursors of tropospheric ozone and particulate matter. The sparse local observations make it challenging to understand NO_x cycling across the Tibetan Plateau (TP), which plays a crucial role in regional and global atmospheric processes. Here, we utilized Geostationary Environment Monitoring Spectrometer (GEMS) data to examine the tropospheric NO₂ vertical column density (Ω_NO2) spatiotemporal variability over TP, a pristine environment marked with natural sources. GEMS observations revealed that the Ω_NO2 over TP is generally low compared with surrounding regions with significant surface emissions, such as India and the Sichuan basin. A spatial decreasing trend of Ω_NO2 is observed from the south and center to the north over Tibet. Unlike the surrounding regions, the TP exhibits opposing seasonal patterns and a negative correlation between the surface NO₂ and Ω_NO2. In the Lhasa and Nam Co areas within Xizang, the highest Ω_NO2 in spring contrasts with the lowest surface concentration. Diurnally, a midday increase in Ω_NO2 in the warm season reflects some external sources affecting the remote area. Trajectory analysis suggests strong convection lifted air mass from India and Southeast Asia into the upper troposphere over the TP. These findings highlight the mixing interplay of nonlocal and local NO_x sources in shaping NO₂ variability in a high-altitude environment. Future research should explore these transport mechanisms and their implications for atmospheric chemistry and climate dynamics over the TP.