Author: John Beck, Sara Graves, Matt Smith, Todd Berendes – University of Alabama Huntsville
Title: Adapt Precipitation Super-Resolution Techniques for Operational Flood Forecasting
Abstract: Researchers at UAH’s Information Technology and Systems Center (ITSC) have developed a Cloud-based Analytic Framework (CAPRi) for conducting super-resolution imagery of precipitation datasets from the Global Precipitation Measurement Mission Validation Network (GPM VN) by using deep learning models. These models, consisting of Convolutional Neural Networks (CNNs), are used to enhance the resolution of GPM Dual-frequency Precipitation Radar (DPR) data for improved identification of convective scale precipitation features and rain-rate estimates, particularly outside the coverage of ground-based weather radars. UAH/ITSC is supporting NOAA’s Cooperative Institute for Research to Operations in Hydrology (CIROH) by expanding its super-resolution precipitation research to produce an enhanced rain rate product from space. Results will be tested as an additional dataset for input into to NOAA’s National Water Model (NWM) for improved operational hydrologic forecasting. Positive impacts are expected to be the greatest in areas where ground-based radar precipitation data are lacking (i.e., CONUS mountainous regions, off-shore coastal areas, and NWM southern-Alaska domain). This poster will highlight our current progress and showcase the user interface.