The Bayesian passive microwave retrievals of snowfall often rely on mathematical matching of the observed vectors of brightness temperature with an a priori database of precipitation profiles and their corresponding brightness temperatures. Mathematical proximity does not necessarily lead to consistent retrievals due to limited information content of passive microwave observations. This paper defines imposter (genuine) vectors of brightness temperature as those that are mathematically close but physically inconsistent (consistent) and characterizes them through the Silhouette Coefficient (SC) analysis. The Neyman–Pearson (NP) hypothesis testing is used to separate the imposter and genuine brightness temperatures based on their associated values of cloud ice (IWP) and liquid water path (LWP), given by coincidences of CloudSat Profiling Radar (CPR) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The study determines thresholds for IWP and LWP that allow optimal identification of imposter brightness temperatures of non-snowing and snowing clouds, which can mislead the passive microwave retrieval algorithms to falsely detect or miss the snowfall events. It is demonstrated that emission signal of supercooled liquid water in snowing clouds can lead to improved passive microwave retrieval of snowfall and conditioning the retrievals to the cloud IWP and LWP can result in marginal correction of the snowfall detection probability; however, reduce the probability of false alarm by 6%–8% over sea ice and open oceans.

降雪的贝叶斯被动微波反演通常依赖于观测到的亮温矢量与先验的数学匹配降水廓线数据库及其相应的亮温。由于被动微波观测的信息内容有限，数学上的接近不一定会导致一致的检索。本文将亮度温度的冒名顶替（真正）向量定义为那些在数学上接近但物理上不一致（一致）的向量，并通过轮廓系数（SC）分析对其进行表征。Neyman-Pearson (NP) 假设检验用于根据 CloudSat Profiling Radar (CPR) 和全球降水测量 (GPM) 微波成像仪 (GMI)。该研究确定了 IWP 和 LWP 的阈值，以允许最佳识别无雪和下雪云的冒名顶替亮度温度，这可能会误导被动微波反演算法错误地检测或错过降雪事件。证明了降雪云中过冷液态水的发射信号可以改善降雪的被动微波反演，调节对云IWP和LWP的反演可以对降雪检测概率进行边际校正；但是，将海冰和开阔海域的误报概率降低 6%–8%。证明了降雪云中过冷液态水的发射信号可以改善降雪的被动微波反演，调节对云IWP和LWP的反演可以对降雪检测概率进行边际校正；但是，将海冰和开阔海域的误报概率降低 6%–8%。证明了降雪云中过冷液态水的发射信号可以改善降雪的被动微波反演，调节对云IWP和LWP的反演可以对降雪检测概率进行边际校正；但是，将海冰和开阔海域的误报概率降低 6%–8%。