Bayesian Cloud Property Retrievals for Shallow Liquid Clouds

 
Poster PDF

Authors

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

Category

Warm low clouds, including aerosol interactions

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. The ARM program has invested in a comprehensive suite of active and passive remote sensing instruments, as well as a systematic large eddy simulation (LES) modeling activity at the ARM SGP site. This project is producing retrievals of cloud particle size distributions and in-cloud vertical velocity in shallow warm clouds, and addresses the need for evaluation of current and future LES of shallow cumulus at the ARM SGP site. 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. MCMC is well proven for characterization of cloud properties and their uncertainties, but until recently was been used operationally on ARM data. Our project has tested the retrieval framework using cloud fields obtained from LES output, and is producing experimental Bayesian retrievals over the Eastern North Atlantic (ENA) site. This presentation provides an update on the progress of this project over the past year, including: (1) Use of scene-dependent prior information in Bayesian retrievals (2) An overview of results from synthetic retrievals using input from LES models with bin resolved microphysics. (3) Select results from retrievals of cloud water content and vertical motion over the ENA site (4) Quantitative estimates of uncertainty in retrievals of cloud property vertical profiles