A fast cloud geometrical thickness retrieval algorithm for single-layer marine liquid clouds using OCO-2 oxygen A-band measurements

Abstract Knowledge of cloud geometrical thickness (H) is of importance for the study of radiative balance and cloud microphysics. However, the retrieval of H remains challenging, especially for passive instruments. In this work, we derive a semi-analytical algorithm for retrieving H of single-layer liquid cloud based on oxygen A-band hyperspectral measurements from NASA's Orbiting Carbon Observatory-2 (OCO-2). In this algorithm, a high-order correction is introduced to the approximation formula to accurately calculate oxygen A-band hyperspectral cloud reflectance. The algorithm can retrieve H in real time as it does not require the use of the time-consuming radiative transfer model for radiation calculation during each retrieval. In addition, the algorithm currently requires cloud optical depth, cloud top height, and aerosol properties measured by other instruments as input. In idealized simulations using ten thousand A-band spectra spanning a range of cloud cases, the root mean squared error (RMSE) of the retrieval is approximately 2.0 hPa (for low clouds, 1 hPa is about 10 m). We also retrieve H based on millions of real OCO-2 observations and compare the retrieval results with the cloud product from CloudSat/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations). After abnormal samples are removed, the correlation coefficient is 0.716, the average bias is −15.6 hPa, and the RMSE is 27.4 hPa. The statistical results show that the absolute bias increases systematically with the reference cloud geometrical thickness, which may be caused by the unrealistic vertical homogeneous cloud assumption. The similar phenomenon was also found in comparison with OCO2CLD-LIDAR-AUX, a joint retrieval product based on OCO-2, CALIPSO, and accurate radiative transfer model. The fast algorithm shows a similar distribution of retrieved cloud bottom pressure and a smaller bias on cloud geometrical thickness retrieval compared to the OCO2CLD-LIDAR-AUX products.