Estimation of Ice Number Concentration in Stratiform Mix-phase Clouds with Cloud Radar Reflectivity Measurements

Measurement of ice number concentration (N_ice) in clouds is important but still challenging. Stratiform mixed-phase clouds (SMCs) provide a simple scenario for estimating ice number concentration from remote sensing measurements. The simple ice generation and growth pattern in SMCs offers opportunities to use cloud radar reflectivity (Ze) measurements and other cloud properties to quantitatively infer the ice number concentration. To quantitatively understand the strong temperature dependency of ice habit and growth rate, we develop a 1-D ice growth model to calculate the ice diffusional growth along its fall trajectory in SMCs. The 1-D model simulated Ze profiles are compared with ACRF ground-based radar observations. Combining Ze measurements and 1-D ice growth model simulations, we develop a method to retrieve N_ice in SMCs at given cloud top temperature (CTT) and liquid water path (LWP). Evaluations of the retrieved N_ice with in situ measurements and with the simulations of a 3-D cloud-resolving model with bin microphysical physics scheme show that the retrieved N_ice are within an uncertainty of a factor of 2, statistically.

Collocated CALIPSO lidar and CloudSat radar measurements provide reliable detections of SMCs globally. The algorithms developed are applied to four years of space-borne radar measurements to retrieve N_ice in middle level SMCs. The long-term and global-coverage of the datasets enable to study N_ice characteristics in SMCs in a statistical view and their geographical variations, which might be related to different background aerosol conditions. The dataset also can be used to evaluate existing N_ice parameterizations or to develop new parameterizations.