Cloud-based Analytic Framework for Precipitation Research (CAPRi)

Researchers at the University of Alabama in Huntsville (UAH) Information Technology and Systems Center (ITSC) and Earth Systems Science Center (ESSC), in collaboration with NASA Marshall Space Flight Center (NASA/MSFC), propose to leverage cloud-native technologies explored in the AIST-2016 VISAGE (Visualization for Integrated Satellite, Airborne and Ground-based data Exploration) project to develop a Cloud-based Analytic framework for Precipitation Research (CAPRi). CAPRi will host Global Precipitation Measurement Validation Network (GPM-VN) data integrated with a Deep Learning framework to provide an analysis-optimized cloud data store and access via on-demand cloud-based serverless tools. CAPRi services will automate the generation of large volumes of high-quality training data for successful deployment of Deep Learning models. The research focus will be to develop a Deep Learning application to enhance the resolution of GPM data for improved identification of convective scale precipitation features, particularly outside the range of ground-based weather radar. This resolution enhancement technology is called super-resolution or downscaling. Deep Learning Convolutional Neural Networks (CNNs) will be used to learn features that can infer high-resolution information from low-resolution variables, building on a prototype developed by the PI of this proposal in collaboration with GPM mission scientists. The research focus addresses NASA AIST program's Analytic Center Framework (ACF) thrust Water and Energy Cycle domain. To conduct this research, we have assembled a multi-disciplinary team that includes leaders in Machine Learning/Deep Learning, atmospheric/earth science, precipitation, data analytics, remote sensing, and information technology: PI Dr. John Beck (UAH/ITSC), Deep Learning and remote sensing lead; Co-I Dr. Patrick Gatlin (NASA/MSFC), precipitation science and GPM GV subject matter expert; Co-I Mr. Todd Berendes (UAH/ITSC), GPM VN network and cloud-based tools and analytics lead; Co-I Dr. Geoffrey Stano (UAH/ITSC) atmospheric science and information technology; Co-I Ms. Anita LeRoy (UAH/ESSC) precipitation features subject matter expert (SME); and collaborator Dr. Walt Petersen (NASA/MSFC), GPM subject matter expert. The GPM-VN, having already identified and extracted coincident low-resolution satellite radar and high-resolution ground radar observations of a variety of precipitation events, provides an ideal source of training and test data for this proposal. We propose to develop cloud-based tools within CAPRi to automate the development of this type of training data directly from the VN and results will be used within an extended CNN architecture to downscale the spatial resolution of satellite-based GPM DPR precipitation products from ~5km to ~1km radar resolution and thus advance precipitation measurements from space. CAPRi will simplify the access to and the processing of these data, leading to practical applicability of these techniques to improve rainfall retrieval estimates from GPM DPR data in areas where ground-based scanning radar data and reliable precipitation gauge observations are lacking. As a science use test case, CAPRi will be used to develop a precipitation features demonstration database to support the precipitation science community. In prior research, Co-I LeRoy developed a method to identify convective scale precipitation features from a larger scale precipitation features database (developed at the University of Utah). This research will be expanded to identify 3D convective scale precipitation features in the GPM era, leveraging the VN database of precipitation observations from GPM including super-resolution GPM data to improve the identification and refine the scale of 3D precipitation features beyond the scope of the VN.

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