Probabilistic assessment of cloud fraction using Bayesian blending of two data sets: a feasibility study

Poster PDF

Description

We describe and evaluate a novel method to blend two observed cloud fraction data sets through Bayesian posterior estimation. The research reported here is a feasibility study designed to explore the method. In this proof-of-concept study, we illustrate the approach using specific observational data sets from the U. S. Department of Energy Atmospheric Radiation Measurement Climate Research Facility’s Southern Great Plains (SGP) site in the central United States, but the method is quite general and is readily applicable to other data sets. The total sky imager (TSI) observations are used to determine the prior distribution. A regression model and the Active Remote Sensing of Clouds (ARSCL) value-added product radar/lidar observations are used to determine the likelihood function. The posterior estimate is a probability density function (PDF) of the cloud fraction (CF) whose mean is used as the optimal blend of the two observations. The data at hourly, daily, 5-day, monthly, and annual time scales are considered. Some physical and probabilistic properties of the cloud fractions are explored from radar/lidar, camera, and satellite observations and from simulations using the Community Atmosphere Model (CAM5). Our results imply that (1) the Beta distribution is a reasonable model for the cloud fraction for both short- and long-time means, the 5-day are skewed right, and the annual data are almost normally distributed, and (2) the Bayesian method developed successfully yields PDF of CF, rather than a deterministic CF value and is feasible to blend the TSI and ARSCL data with capability of bias correction.