The objective of the proposed research is to improve, enhance, and continue the support for GPM algorithms, conduct validation and also offer a major enhancement to the classification module from DPR,…

Colorado State University's Sponsored Programs Division was awarded a $473,450 grant by a civilian federal agency for research to support the Global Precipitation Measurement (GPM) mission through algorithm development and validation. The work focuses on enhancing the Dual-frequency Precipitation Radar (DPR) classification module by adding three-dimensional capabilities to retrieved precipitation products. Key objectives include validating version 7 products, testing dual-frequency products across inner and outer swath profiles, studying internal consistency between products, and developing advanced hydrometeor classification techniques to identify precipitation types such as snow, hail, and graupel at global and regional scales. The research will leverage space-borne observations from the GPM platform combined with ground-based dual-polarization radar data and in-situ observations conducted from the Fort Collins, Colorado location.

The project runs through February 28, 2027, and is structured as a competitive research grant with no set-aside designation. The work builds upon Colorado State University's existing leadership role within the DPR algorithm development group, where the institution currently manages the classification module and hydrometeor type development. The research will correlate microphysical information derived from precipitation observations with space-borne radar profiles, ultimately improving GPM's microphysical retrieval algorithms and vertical profile hydrometeor classification capabilities at the global scale to enhance the utility of GPM data for precipitation science and forecasting applications.

The objective of the proposed research is to improve, enhance, and continue the support for GPM algorithms, conduct validation and also offer a major enhancement to the classification module from DPR, namely add a third dimension to the retrieved products. We will continue to support analysis, to study precipitation microphysical parameters such as hydrometeor and precipitation types at global and regional scales to derive microphysical inferences of precipitation variations at these scales, with the main goal of algorithm support, validation and enhancement for the classification module for DPR. These studies will be conducted, using space-borne observations from the GPM platform as well as ground dual-polarization radars and in-situ observations. Some of the most important tasks of the current proposal are a) validate the performance of the new products introduced in V7 that will be released later this year, b) test and validate the existing and new dual-frequency products for inner and outer swath of the DPR profile, c) study internal consistency and contrast between products and d) provide a significant leap by adding the third dimension to the classification module. In order to make an impact on the use of GPM observations and retrievals, the microphysical information must be correlated to the space-borne observations. The specific activities in that direction include: a) simulation of GPM precipitation radar profiles for categories of microphysical significance, b) the development of techniques to generate maps of hydrometeor types such as snow, hail and graupel and compare them against ground dual-polarization radar based retrievals, and c) making use of the advances in steps (a), (b) to improve GPM microphysical retrieval algorithms, specially vertical profile hydrometeor classification at a global scale. Currently our team plays a key role in the DPR algorithm development group, and is responsible for the classification module, and hydrometeor type development (that includes the snow index module, graupel and hail products), that has been released and operational. We will continue to test, validate and refine the classification module and introduce the vertical profile of hydrometeors. The PI and the team, propose to continue to play that role and be a part of the international DPR algorithm team, and in addition, play a significant role in the analysis of ground radar and other in-situ sampling infrastructure for the cross-validation of hydrometeor classification as well as mixed-phase precipitation studies with space-borne measurements focusing on the modules under discussion. GPM precipitation radar observations, combined with the knowledge of precipitation microphysics observed on the ground worldwide, provide a sound basis for modeling the precipitation observations for space-borne systems. We propose the use of observations-based simulations as well as ground-based dual polarization and dual-frequency radar observations to develop data-based models to evaluate the space-borne dual-frequency radar observations. Continuous educational outreach associated with the research will be an integral feature of this proposal.