RII Track-2 FEC: Precise Regional Forecasting via Intelligent and Rapid Harnessing of National Scale Hydrometeorological Big Data

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Global warming has emerged as a stark problem of national importance, as it results in more frequent extreme weather and climate events that cause rising economic loss and adverse societal impacts on numerous sectors, such as agriculture, transportation, water resource management, urban planning, among others. For better observations and numerical models on weather and climate parameters to improve forecasting accuracy, this project addresses precise regional forecasting via intelligent and rapid harness on national scale hydrometeorological Big Data. It aims to improve meteorological and hydrologic forecasts at target regions of interest by integrating massive atmospheric data sets with gathered surface data for finer temporal and spatial predictions, containing both fundamental research and experimental activities. Its solution approach is innovative by leveraging the actual gathered data as feedback to make prediction models generate better products with multiple near-term time horizons. Better regional prediction results from harnessing Big Data intelligently and rapidly via utilizing (1) a collection of proposed simple neural network models (called modelets) and (2) multiple accelerating methodologies developed or under development by the research team members. The modelet-based solutions for improving weather prediction spatially and temporally are applicable to all regions in the nation, with easy portability. They are being undertaken synergistically by jurisdictional collaboration across five universities in Louisiana, Alabama, and Kentucky, plus U.S. Geological Survey, enabling broad engagement at the frontiers of discovery and innovation in science and engineering related to accelerating data analytics, meteorology, and hydrology. Besides promoting the progress of science, this multidisciplinary project advances the national prosperity and welfare by curbing potential disruption due to global warming. The project also includes comprehensive efforts for (1) building future leadership through collaboration and supervision of junior investigators for their career advances, (2) enriching educational materials on the focused disciplines and strengthening student research to boost workforce development, and (3) aggressively recruiting and engaging underrepresented participants to support diversity.Better observations and numerical models on weather and climate parameters improve forecasting accuracy, able to suppress the uptrend in economic loss and societal impacts caused by disasters pertinent to extreme weather and climate events, as a result of global warming. This multidisciplinary project involves both fundamental research and experimental activities, built upon and expanding earlier work of team members in the disciplines of computer science and engineering, meteorology, hydrology, and electrical & computer engineering. It deals with the technical challenges of intelligent and rapid harness on national scale hydrometeorological Big Data for precise meteorological and hydrological forecasting regionally, with anticipated outcomes likely to push the frontiers of intelligent bigdata harness by NNs (neural networks) and of speedy data processing through various methodologies. Intelligent bigdata harness results from proper end-to-end simple NN models (called modelets), which are trained inventively by huge datasets obtained continuously from both near-ground observations (via Mesonet stations or water gauges) and geo-gridded predictions based on computing physical atmospheric equations (via the Weather Research and Forecasting model with High-Resolution Rapid Refresh). Various methodologies for accelerating bigdata harness are under development and to be evaluated thoroughly during the project years, including (1) effective computer system DRAM expansion, (2) execution resilience enhancement, and (3) high-compute density support by SC (stochastic computing)-based accelerators and by GPGPUs with desirable scheduling policies for modelet training and inference. The modelet-based solutions for precise regional forecasting via intelligent and rapid (PREFER) bigdata harness improve weather prediction spatially and temporally for wide applications to all regions in the nation, with easy portability to offer short-term and fine spatial resolution prediction. They aim to address the important problems of meteorological forecast applications (e.g., landfalling of severe thunderstorms or tropical systems), flood warning alert enhancement, backwater wetland storage capacity investigation for river flood mitigation, among others. This PREFER work inspires and nourishes cross-disciplinary research and jurisdictional collaboration across five universities in Louisiana, Alabama, and Kentucky, plus U.S. Geological Survey, helping to integrate research and education while advancing discovery and understanding in the scientific contents of interwoven project activities. It contains comprehensive efforts for (1) lifting career development of junior investigators to build future leadership, (2) enriching educational materials on the focused disciplines and supporting student research to spur workforce development, (3) engaging active participation from underrepresented groups, and (4) disseminating project outcomes and software tools widely.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.