Bayesian Retrievals of Vertically Resolved Cloud Particle Size Distribution Properties

摘要: AbstractRetrievals of liquid cloud properties from remote sensing observations by necessity assume sufficient information is contained in the measurements, and prior knowledge cloudy state, to uniquely determine a solution. Bayesian algorithms produce retrieval that consists joint probability distribution function (PDF) given measurements knowledge. The posterior PDF provides maximum likelihood estimate, content specific effect observation forward model uncertainties, quantitative error estimates. It also test whether, which contexts, set able provide unique In this work, Markov chain Monte Carlo (MCMC) algorithm used sample for retrieved shallow clouds over Southern Ocean. Combined active passive spaceborne W-band radar vis...