Bayesian Cloud Property Retrievals from ARM Active and Passive Measurements

 

Authors

Derek J. Posselt — University of California, Los Angeles
Marcus van Lier-Walqui — Columbia University
Jasmine Remillard — McGill University
Pavlos Kollias — Stony Brook University

Category

Microphysics (cloud, aerosol and/or precipitation)

Description

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 a key element in 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 PSD properties, and for returning quantitative measures of uncertainty in retrievals. Over the last five years, the ARM Facility has invested in a comprehensive suite of active and passive remote-sensing instruments and has recently begun a systematic large-eddy simulation (LES) modeling activity at the ARM Southern Great Plains (SGP) site. This project is producing retrievals of cloud particle size distributions and in-cloud vertical velocity in shallow warm clouds, and will address the need for evaluation of current and future LES of shallow cumulus at the ARM SGP site. The methodology is fully Bayesian, and uses a Markov chain Monte Carlo (MCMC) algorithm to produce both an optimal estimate and robust estimates of uncertainties. MCMC is well proven for characterization of cloud properties and their uncertainties, but has not been used operationally on ARM data. As such, the first set of experiments tests the retrieval framework using cloud fields obtained from LES output over the SGP site. This presentation provides an update on the progress of this new project, including: (1) descriptions of the forward radiative transfer models and retrieval methodology, and (2) initial results from synthetic retrievals using input from LES models with bin-resolved microphysics.