Retrieving co-occurring cloud and precipitation properties of warm marine boundary layer clouds with A-Train data

In marine boundary layer (MBL) clouds the formation of precipitation from the cloud droplet distribution in the presence of variable aerosol plays a fundamental role in determining the coupling of these clouds to their environment and ultimately to the climate system. Here the degree to which A-Train satellite measurements can diagnose simultaneously occurring cloud and precipitation properties in MBL clouds is examined. Beginning with the measurements provided by CloudSat and Moderate Resolution Imaging Spectroradiometer (including a newly available microwave brightness temperature from CloudSat), and a climatology of MBL cloud properties from past field campaigns, an assumption is made that any hydrometeor volume could contain both cloud droplet and precipitation droplet modes. Bayesian optimal estimation is then used to derive atmospheric states by inverting a measurement vector carefully accounting for uncertainties due to instrument noise, forward model error, and assumptions. It is found that in many cases where significant precipitation coexists with cloud, due to forward model error driven by uncertainties in assumptions, the uncertainty in retrieved cloud properties is greater than the variance in the prior climatology. It is often necessary to average several thousand (hundred) precipitating (weakly precipitating) profiles to obtain meaningful information regarding the properties important to microphysical processes. Regardless, if such process level information is deemed necessary for better constraining predictive models of the climate system, measurement systems specifically designed to accomplish such retrievals must be considered for the future.