Cloud-based Analytical Framework for Precipitation Research (CAPRi)

Abstract:

Researchers at the University of Alabama in Huntsville (UAH), in collaboration with NASA’s Marshall Space Flight Center (NASA/MSFC), are developing a Cloud-based Analytic Framework for Precipitation Research (CAPRi). To accomplish this task, CAPRi will host datasets from the Global Precipitation Measurement Validation Network (GPM VN) integrated with a Deep Learning framework to provide an analysis-optimized cloud data store with access via on-demand cloud-based serverless tools. CAPRi services will automate the generation of large volumes of high-quality training data required for successful development of Deep Learning models. Our research focus area will be to develop a Deep Learning model consisting of Convolutional Neural Networks (CNNs) to enhance the resolution of GPM data for improved identification of convective scale precipitation features, particularly outside the coverage of ground-based weather radar. This research will use extended CNNs to learn features that can infer high-resolution information from low-resolution variables, building on a prototype from previous collaborations with GPM mission scientists. The project team is currently investigating using image super-resolution technologies to improve the resolution of GPM Dual-frequency Precipitation Radar (DPR) products. 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 testing data for Deep Learning classifiers. CAPRi will automate the complex process of deriving training/test data sets from the GPM VN. As a science use test case, we will develop a 3-dimensional convective scale precipitation features demonstration database to support the precipitation science community using the new datasets.