Inference of Precipitation in Warm Stratiform Clouds Using Remotely Sensed Observations of the Cloud Top Droplet Size Distribution

Drizzle is a common feature of warm stratiform clouds and it influences their radiative effects by modulating their physical properties and lifecycle. An important component of drizzle formation are processes that lead to a broadening of the droplet size distribution (DSD). Here, we examine observations of cloud and drizzle properties retrieved using colocated airborne measurements from the Research Scanning Polarimeter and the Third Generation Airborne Precipitation Radar. We observe a bimodal DSD as the aircraft transects drizzling open-cells whereby the larger mode reaches a maximum size near cloud center and the smaller mode remains relatively constant in size. We review similarities between our observations with droplet growth processes and their connections with precipitation onset. We estimate droplet sedimentation using the cloud top DSD and find a correlation with rain water path of 0.82. We also examine how changes in liquid water paths and droplet concentrations may act to enhance or suppress precipitation. Plain Language Summary Low clouds play a central role in regulating Earth's climate by reflecting a portion of incoming sunlight back to space. When clouds rain, the amount of sunlight reflected back to space is altered because the distribution and amount of water within a cloud is modified. Detecting the presence of rain using passive instruments is challenging. In this study, we use a multi-angular polarimeter and radar instruments to investigate how droplets at cloud top relate to rainfall that occurs lower in the cloud. We observe a pattern in droplet sizes that appears to be related to rainfall formation, and we discuss commonalities this pattern has with rainfall formation processes. We investigate several key cloud properties and how they can be used to determine rainfall rates. This work may help future passive space-based instruments determine if a cloud is raining and improve the accuracy of cloud property retrievals.