Although initially designed for precipitation retrieval improvement for the Global Precipitation Measurement (GPM) mission, the CDRD also has many other scientific uses. In this paper distributions are analyzed between the many available CDRD parameters, for example microwave frequencies vs. rain rate and snow rate, dynamic tags and radiative transfer parameters vs. latitude, ocean vs. land differentiation, and more. CDRD derived distributions are unique and useful because of the vast number of randomly spaced simulated variables globally, which eliminates bias towards a particular region or storm type. Interesting distributions are compared with observations. These distributions are presented and their significance is discussed.
One particular distribution we focus on is the relationship between snow rates and microwave brightness temperatures. Snow is a very challenging quantity to accurately remotely sense from space. Our distributions have shown that these relationships tend to follow a normal distribution gradually shifting to a bi-modal distribution due to land influences. The mean of these distribution shifts as frequencies decrease. These distributions provide the potential to accurately use microwave radiance measures to estimate snowfall. Another challenging quantity to estimate is precipitation influenced by topography. We focus on the 3-D relationship between topography height, brightness temperatures, and precipitation rates to better understand the potential for this type of retrieval. Correlations between CDRD variables are presented in this paper. Understanding of CDRD distributions and correlations are important for the upcoming Global Precipitation Measurement (GPM) mission because of the increased spatial coverage. Land surface and snow will be two of the most significant challenges for GPM retrieval purposes. This paper helps clarify which variables are potentially useful for GPM retrievals.