The overarching goal of our group's research under the NASA TRMM and PMM programs is to understand the physical differences between different precipitation systems and quantify their effects on satellite estimation, emphasizing those (such as the passive microwave algorithms) of greatest importance for the Global Precipitation Mission.
The principal set of tools our group has used is our "precipitation feature" (PF) database, now comprising 8 years of Version 6 TRMM data and counting. The unique feature of the database is that every PF (about 50 million) is first defined by using a contiguity test so that we know the size and rain volume of each one, and then assign many other observed properties. In this way, we can quickly ask and answer a variety of questions. We have used the database to discover that MCSs have specific large biases between algorithms, the global distribution of extremely intense convective storms, and the properties of PFs with lightning. We propose a major improvement and expansion of this database. We will improve its ease of use and share it widely.
As we move into the GPM era, it is especially important to understand the error characteristics of the algorithms in specific meteorological regimes. This proposal aims at using the growing Utah PF database to analyze observed properties and algorithm biases as a function of regime, for example, convective storms over mountains and complex terrain. We will continue to expand our existing research over tropical oceans, using data from the Kwajalein field program in conjunction with cloud resolving models to evaluate the algorithms' ability to simulate convective intensity, convective-stratiform separation, and snow and graupel content (models have too much). In addition to KWAJEX, we have a newly obtained database with the best forcing data since GATE in the Darwin Australia region, where NASA can leverage a substantial investment from DOE/ARM and the Australian BMRC. Are anvil cirrus properties closely related to the properties of their convective source? We need to find out.
This proposal also aims to use the Utah PF database approach to provide an objective basis for selecting specific types of cold season precipitation events where precipitation algorithms disagree. Storm systems with low freezing levels present a difficult set of challenges, especially over snow-covered or elevated terrain. There are ample opportunities to study such events with the TRMM database, with the goal of improving algorithms for GPM.
This research fits into several of the categories of the announcement, including:
1.1 (Precipitation Variability especially topics 1,2,4),
1.2 (Algorithms, Validation), including topics 1-5),
1.4 (Weather Forecast Capabilities, especially topic 3).
