A Machine Learning-Based Multiple Cloud Vertical Structure Parameter Prediction Algorithm Only Using OCO-2 Oxygen A-Band Measurements

Abstract

Measurements of the global cloud vertical structure (CVS) are critical to better understanding the effects of the CVS on climate. Current CVS algorithms based on OCO-2 have to be combined with cloud top height products from CALIPSO and CloudSat, which are no longer available after these two satellites left A-Train in 2018. In this paper, we derive a machine learning-based algorithm using only OCO-2 oxygen A-band hyperspectral measurements to simultaneously predict the cloud optical depth (COD), cloud top pressure (p_top), and cloud pressure thickness (CPT) of single-layer liquid clouds. For validation of real observations, the root mean square errors (RMSEs) of the COD, p_top, and CPT are 7.31 (versus the MYD06_L2), 35.06 hPa, and 26.66 hPa (versus the 2B-CLDCLASS-LIDAR). The new algorithm can also predict CVS parameters trained with p_tops from CALIPSO/CloudSat or CODs from MODIS. Controlled experiments show that known p_tops are more conducive to CPT prediction than known CODs, and experiments with both known CODs and p_tops obtain the best accuracy of RMSE = 20.82 hPa. Moreover, a comparison with OCO2CLD-LIDAR-AUX products that rely on CALIPSO shows that our CVS predictions only using OCO-2 measurements have better CODs for all clouds, better p_tops for clouds with a p_top < 900 hPa, and better CPTs for clouds with a CPT > 30 hPa.