A general algorithm to retrieve cloud top properties by incorporating spectral characteristics and lidar measurements

Although clouds and their properties are critical to radiation budget and weather change, the cloud products derived from passive radiometers remain significant uncertainties due to the complex variations of clouds and the limited spectral characterization of existing algorithms. In this study, a general algorithm is proposed to retrieve cloud top height (CTH), cloud top temperature (CTT) and cloud top pressure (CTP) simultaneously by establishing a look-up table (LUT) between lidar measurements and the cloud-sensitive spectral characteristics. Validated by an independent year, the algorithm has achieved accurate retrievals under both daytime and nighttime conditions, with an averaged Root Mean Square Error (RMSE) of 1.70 km, 9.0 K and 118 hPa for CTH, CTT and CTP, respectively. The above RMSEs are much lower than those reported for other algorithms proposed in recent years, and have decreased by about 40 % compared to the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) products, which indicates the better performance of the proposed algorithm. The algorithm’s superior performance and independence from auxiliary data make it a promising approach for characterizing the spatio-temporal patterns of global cloud layers.