Using Bayesian Cloud Property Retrievals to Study Shallow Convective Clouds

Cloud processes remain a major source of uncertainty in regional and global modeling of the Earth's climate system. Cloud microphysics and vertical motion lie at the heart of this uncertainty, as they (1) govern the rates of conversion between phases of water, (2) modulate the amount of water transported vertically from the boundary layer to all levels of the troposphere, and (3) determine the properties of particles at cloud top and in the cloud interior. The availability of long-term estimates of microphysical properties (e.g., water content, characteristic size) and in-cloud vertical motion with well-characterized uncertainties is key to our efforts to evaluate and improve the representation of clouds and precipitation in numerical models.

Recent work has demonstrated that microphysical retrievals based on Bayesian methodologies have promise for producing robust estimates of cloud particle size distribution properties, and for returning quantitative measures of uncertainty in the retrievals. These methods also yield an assessment of information contained in observations from one or more different measurement platforms, and can be used to establish measurement accuracy criteria. Over the last five years, the ARM program has invested in a comprehensive suite of active and passive remote sensing instruments at several sites around the globe. In a recent DOE-supported project, Bayesian techniques have been used to produce retrievals of liquid cloud vertical profiles over the DOE ARM Eastern North Atlantic (ENA) site. Retrievals have robust quantitative estimates of uncertainty, and utilize ARM scanning and vertically pointing Doppler radar measurements. The methodology is fully Bayesian, and utilizes a Markov chain Monte Carlo (MCMC) algorithm to produce both an optimal estimate and robust estimates of uncertainties. The retrieval development conducted in the first two years of this project constitutes one of the first attempts to develop microphysical and dynamical retrievals in shallow cumulus using ground-based observations. We propose, in this one-year extension of our current project, to utilize the retrieved cloud parameters to quantify shallow cumulus variability, co-variability and dependency on large-scale environmental conditions and mesoscale organization. In so doing, we will address the following set of science questions:

1. What is the vertical structure of the liquid water content in shallow cumulus clouds with and without precipitation, and what is its relationship to environmental parameters?

2. What are the variance and co-variance of microphysical (e.g., liquid water content, rain rate) and dynamical (e.g., vertical air motion and turbulent kinetic energy) parameters in shallow cumulus clouds at different scales?

3. What are the links between precipitation properties and cloud dynamics in shallow cumulus clouds?