Constraining Bulk Microphysics Warm-Rain Autoconversion Rates with Radar Reflectivity and Cloud Droplet Effective Radius Data.

Although warm-rain clouds are arguably better understood than ice-phase or mixed-phase clouds, Global Climate Models (GCMs) still have significant precipitation biases related to warm-rain when compared to satellite observations. One of the sources of uncertainty is the autoconversion rate (i.e. the rate of conversion of cloud water to rain water) calculated by the microphysics schemes employed by the GCMs. In this work the autoconversion rate formulations by Kessler (1969) and Khairoutdinov and Kogan (2000) are tested in Large Eddy Simulations (LES) of stratocumulus (DYCOMS) and cumulus (RICO).

Cloud and rain water content predicted by the two schemes is converted to cloud top effective radius (r_eff) and radar reflectivity (Z) and then compared to results from a Lagrangian (i.e. particle tracking) microphysics scheme. The Lagrangian scheme can fully resolve the size distribution of cloud droplets and rain drops and is treated as a proxy for observational data. The coefficients of the two autoconversion rates are changed to test their sensitivity and to find the best fit to the Lagrangian scheme data.

The r_eff and Z values are used for comparison because these quantities can also be measured by satellite instruments. The short term goal of this work is to test the autoconversion rate formulations commonly used in GCMs against the Lagrangian scheme results for different cloud regimes. The broader goal is to set up a framework for a systematic, data driven evaluation of cloud microphysics parameterizations used in GCMs, based on LES and in the future also satellite observations.