Analysis of Rain Drop Size Distribution to Elucidate the Precipitation Process using a Cloud Microphysics Conceptual Model and In Situ Measurement

Abstract

Abstract Raindrop size distribution (DSD) is an important parameter in rainfall research and can be used for quantitative precipitation estimation (QPE) in meteorology and hydrology. DSD also improves the understanding of the uncertainty of cloud microphysical processes (CMPs) such as ice-based and warm rain growth during climate change. Changes in CMPs impact the generation of precipitation. However, the estimation of CMPs based on in situ observation is difficult because of the complexity of microphysics processes, and most previous studies on the CMP involved approximations to predict the types of microphysical processes affecting precipitation generation based on in situ observations performed in real-time. Therefore, we developed a simple method for understanding the CMPs of precipitation generation using a conceptual model of CMPs and in situ observation DSD data. We employed previously observed DSD parameters and a CMP conceptual model of the DSD observation-based microphysical process. As case studies, we applied DSD observation data obtained in Korea and East Asia to estimate the CMPs. For example, the major CMP of megacities was vapor deposition in Beijing (< 1 mm h−1) and Seoul (< 5 mm h−1), as the strong updraft of the urban heat island effect in megacities results in increased liquid water content, leading to the formation of large number of supersaturated clouds at higher altitudes.