Toward Better Understanding of Microphysical Processes and Resulting Precipitation Physics: A merger of observations and cloud models

Abstract

Understanding microphysical processes in the warm, mixed, and ice phases of clouds and how these processes impact surface precipitation rates and storm dynamics is critical to validating model parameterizations in cloud resolving models (CRMs), and ultimately in global climate models. However, leveraging routine observations to achieve this goal can be difficult owing to differences in parameter space, spatial and temporal scales and sample sizes between observations and numerical simulations. Relating observations to dominant microphysical processes contributing to surface precipitation not only provides a way to study the variability of such processes, but also a means for validating microphysics schemes implemented in models.
The work centers on a synergistic examination of precipitation processes from the perspectives of observations and CRMs toward the goal of improving model microphysical parameterizations and remote sensing of cloud processes. This will be accomplished through two synergistic objectives: 1) Provide an understanding of observed drop-size distribution (DSD) variability in terms of cloud microphysical processes; 2) Assess the impacts of DSD assumptions on precipitation microphysics in cloud-resolving models. The second objective will have two parts, first looking at what is the sensitivity of cloud processes simulated using bulk microphysics schemes to the assumed rain gamma shape parameter, and what impact does the assumed rain shape parameter have on surface precipitation and cloud dynamics? Secondly, what impact does the rain shape parameter have in simulating modes of DSD variability that are evident in observations? In what manner do poor or uninformed a priori choices of the shape parameter impact the simulated DSD variability?
By linking precipitation formation processes to characteristic rain DSDs in the first objective, we can then infer microphysical processes from routine measurements from ARM instruments, providing a means to monitor long-term trends of precipitation characteristics. In turn we will study the sensitivity of bulk microphysics simulations to assumed rain drop-size distribution parameters, specifically the gamma shape parameter through objective 2, and seek to compare model-retrieved variability in surface precipitation parameters to observations. This will help to quantify the uncertainty in the assumptions about using a fixed shape parameter for multiple storm morphologies and locations. Finally, by comparing the co-variance of DSD parameters in simulations using bulk and bin microphysics models to observations, we can better understand how well rain DSD variability is represented in cloud-resolving models.
This work will provide a means for relating cloud processes to routinely available DOE ARM observations, allowing not only for studies of cloud processes, but also for future comparisons with cloud-resolving, regional, and global climate models. Additionally, through a better understanding of the sensitivity of precipitation processes and associated dynamical feedbacks to the fixed-shape parameter assumption in bulk microphysics schemes, we can reduce uncertainties in precipitation processes by providing specific guidance on the most appropriate values to use for different environments and storm morphologies. Lastly, through comparisons between model simulated DSD variability and observations, we can begin to understand if models are able to fully capture the natural variability in rain DSDs. Ultimately, this complimentary effort seeks to better understand microphysical processes through observations and improve cloud-resolving and regional model simulations by reducing the uncertainty in assumptions related to precipitation physics.