Abstract Accurate retrieval of aerosol and cloud properties is essential for improving both climate data records and trace gas observations by satellites. The Global Observing Satellite for Greenhouse gases and Water cycle (GOSAT-GW), launched on 29 June 2025, carries the Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3), which is designed to provide high-spectral-resolution measurements in the visible (~ 450 nm), O 2 A band, and near-infrared (~ 1600 nm) regions. To prepare for operation, an aerosol and cloud retrieval algorithm was developed using Tropospheric Monitoring Instrument (TROPOMI) Level 1B data as a proxy. The algorithm retrieves the aerosol optical depth (AOD) and cloud optical depth (COD) from band 1 only from TANSO-3, and estimates the aerosol layer height (ALH) and cloud layer height (CLH) using O 2 –O 2 absorption at 477 nm. Lookup tables were generated with the linearized pseudo-spherical vector Discrete Ordinate Radiative Transfer, and additional corrections were applied to account for relative humidity, temperature dependence, and systematic offsets in the O 2 –O 2 absorption band. During the airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) 2024 campaign, the AODs showed good agreement with AERONET (correlation coefficient (R) = 0.781, root mean square error (RMSE) = 0.265), while comparison with Pandora SMART-s retrievals showed more moderate agreement ( R = 0.444, RMSE = 0.378). ALH retrievals agreed with High Spectral Resolution Lidar-2 (HSRL-2) data within ± 1 km for AODs greater than 0.5, while under low-aerosol loading conditions the retrieved ALH became less sensitive and tended to converge toward near-surface values. COD retrievals were consistent with TROPOMI operational products, while CLH retrievals based on the O 2 –O 2 absorption band were on average about 1 km lower than O 2 A band-based results, likely due to differences in the vertical sensitivity of the two absorption bands. With further refinement in surface reflectance treatment, aerosol classification, and cloud phase detection, the algorithm will contribute to the effective use of GOSAT-GW observations for atmospheric research and air quality applications.
Lim et al. (Wed,) studied this question.