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.

中文翻译：

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通过将CloudSat雷达反射率与使用贝叶斯蒙特卡洛积分进行的原位微物理测量相结合来合成热带卷云的垂直结构

创建根据实际概率密度函数分布的合成冰云剖面对于亚毫米波辐射计研究至关重要。在本文中，我们开发了一种算法，该算法通过使用贝叶斯蒙特卡洛积分（MCI）结合原位微物理测量和星载雷达反射率观测来创建合成的冰云剖面。我们首先根据双峰粒度分布（PSD）方案和针对不同晶体习惯的实际粒子散射特性，对热带成分，云与气候耦合（TC4）实验中的原位数据进行全面研究。将原位PSD的冰水含量和雷达反射率模拟定性和定量地与原位测量进行比较。然后，我们通过在TC4战役期间将算法应用于云雷达系统（CRS）来评估贝叶斯MCI，并使用紧凑型扫描亚毫米波成像辐射计（CoSSIR）测量来比较检索的亮度温度模拟。最后，我们将贝叶斯MCI应用于CloudSat上的Cloud Profiling Radar（CPR）测量，并使用累积分布函数（CDF）和经验正交函数（EOF）来保存单点统计信息和两点统计信息，允许创建任何数量的合成冰云剖面，这些剖面在统计上与贝叶斯检索结果一致。合成的剖面是冰云垂直结构的有用特征，它结合了两个不同的数据源，