Synthesizing the Vertical Structure of Tropical Cirrus by Combining CloudSat Radar Reflectivity With In Situ Microphysical Measurements Using Bayesian Monte Carlo Integration

Abstract

Creating synthetic ice cloud profiles that are distributed according to realistic probability density functions is fundamentally essential for submillimeter wave radiometer studies. In this paper, we develop an algorithm to create synthetic ice cloud profiles by combing in situ microphysics measurements and spaceborne radar reflectivity observations using Bayesian Monte Carlo Integration (MCI). We first conduct comprehensive studies of the in situ data from the Tropical Composition, Cloud and Climate Coupling (TC4) experiment based on a bimodal particle size distribution (PSD) scheme and the realistic particle scattering properties for different crystal habits. Ice water content and radar reflectivity simulations from the in situ PSD are compared with the in situ measurements qualitatively and quantitatively. We then evaluate the Bayesian MCI by applying the algorithm to the Cloud Radar System (CRS) during the TC4 campaign and compare the brightness temperature simulations for the retrievals with the Compact Scanning Submillimeter Imaging Radiometer (CoSSIR) measurements. Finally, we apply the Bayesian MCI to the Cloud Profiling Radar (CPR) measurements on CloudSat, and we use the cumulative distribution functions (CDFs) and empirical orthogonal functions (EOFs) to preserve the one-point statistics and the two-point statistics that allow for the creation of any number of synthetic ice cloud profiles that are statistically consistent with the Bayesian retrieval results. The synthesized profiles are a useful characterization of the ice cloud vertical structure that combines two disparate data sources, and the synthesis algorithm constitutes an essential initial step for producing critical information in submillimeter radiometry studies.

Assessing synergistic radar and radiometer capability in retrieving ice cloud microphysics based on hybrid Bayesian algorithms

“…The bimodal PSD scheme which approximates both small and large particle distribution modes by gamma functions is used to fit the in situ data, and the ice cloud parameters, including IWC, NC, and particle size are derived. More details about TC4 in situ analysis could be found in Liu and Mace (2020). A multi-variant Gaussian distribution in temperature, ln(IWC), and ln(NC) is used to capture the in situ statistics, using the prior idea that the microphysical parameters are approximately lognormally distributed.…”