Retrieving global single-layer liquid cloud thickness from OCO-2 hyperspectral oxygen A-band

Cloud geometrical thickness (CGT) retrieved from satellite observations is vital in the investigation of cloud microphysics and radiative balance. However, there are few algorithms designed for retrieving CGT over land by using passive remote sensing observations. This is due to the inherent difficulties in correctly retrieving atmospheric profiles because of inadequate knowledge of land surface reflection. A passive algorithm for retrieving CGT in single-layer liquid clouds over land and ocean has been developed in this paper. The retrieval method utilizes OCO-2 (Orbiting Carbon Observatory-2) hyper-spectral oxygen A-band (O2A) observations and employs an O2A white-sky albedo estimation method to correct for the disturbances from land surface reflected radiation under cloudy conditions. The retrieval is achieved in near real-time through the use of a fast radiative transfer approximation scheme and is as accurate as using a full radiative transfer model. The new algorithm retrieves CGTs with a correlation coefficient of 0.619 as compared to results from satellite-based lidar-radar products that have been adjusted for outliers. The retrieval of the cloud geometrical thickness exhibits a mean bias of 5 hPa (46 m) and a root-mean-square error of 40hPa (393 m). The validation uncertainties arise from inaccurate inputs, including land surface albedo estimates, as well as uncertainties and errors in the reference CGT. In addition, there are non-negligible biases in retrieval results due to cloud vertical inhomogeneity and three-dimensional radiative transfer effects at cloud edges.