Monday, 7 July 2014
Stratiform mixed-phase clouds (SMCs) provide a simple scenario for estimating ice number concentrations (Ni) from remote sensing measurements. To quantitatively understand the strong temperature dependence of ice habit and growth rate, a 1-D ice growth model is developed to calculate the ice diffusional growth along its fall trajectory in SMCs. The 1-D ice growth model simulated Ze profiles are evaluated with Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) ground-based radar observations. Algorithms are developed to retrieve Ni in SMCs by combining cloud radar reflectivity (Ze) measurements and 1-D ice growth model simulations at given cloud top temperature (CTT) and liquid water path (LWP). Evaluations of the retrieved Ni in SMCs with in situ measurements and with the simulations from a 3-D cloud-resolving model with bin microphysical physics scheme show that the retrieved Ni are within an uncertainty of a factor of 2, statistically. The algorithms developed are applied to collocated CloudSat radar and CALIPSO lidar measurements and long-term ACRF ground-based remote sensing measurements to retrieve Ni in SMCs. The long-term and global-coverage of the datasets enable us to study Ni characteristics in SMCs in a statistical view and their geographical variations, which might be related to different surrounding aerosol conditions. The retrieved Ni from long-term radar measurements is compared with previous Ni parameterizations. In addition, the large dataset of retrieved Ni is used to evaluate and improve Ni parameterizations in global climate models.
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