This federal Project Grant award from the National Oceanic and Atmospheric Administration's (NOAA) Small Business Innovation Research (SBIR) Program provides $1,298,986 to Climate Forecast Applications Network, LLC (CFAN) to develop an innovative weather forecasting system. The proposed "XTREMECAST" innovation integrates global ensemble weather forecasts with an AI-driven post-processing model to provide skillful probabilistic forecasts of compound extreme weather events at extended...
This National Science Foundation (NSF) Technology, Innovation, and Partnerships (CFDA 47.084) Project Grant, awarded on February 15, 2024 for $275,000.00, supports the development of a weather forecasting and climate prediction tool for subseasonal forecasting, extreme weather events, and long-term climatological changes. The project aims to establish the feasibility of utilizing physics-informed machine learning to create probabilistic models of crucial climatological parameters and extreme...
This Project Grant award from the National Science Foundation (NSF) under the Technology, Innovation, and Partnerships (TIP) program (CFDA 47.084) provides $275,000 to Sunairio Inc. to develop a high-fidelity climate simulation engine powered by generative adversarial networks. The goal is to create a broad (1,000 outcomes), hyperlocal (less than 3 km) climate simulation archive that can be used by power grid planners and energy investors to better understand forward-looking risks to grid...
This $202,000 Project Grant awarded by the National Science Foundation's Geosciences Program (CFDA 47.050) supports the development of data-driven methods for predicting extreme weather events under climate change. The grant aims to create neural network models capable of capturing the statistics of extreme events, such as heat waves and droughts, in future climate projections. The work builds on previous research on modeling chaotic systems and addressing the spectral bias in neural networks....
This $449,321 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) supports the development of a novel deep learning-based atmospheric emulator that can accurately model climate dynamics and extreme weather events. The key innovations are: (A) incorporating physical constraints into the deep learning architecture to preserve small-scale physical consistency, and (B) enabling numerical stability analysis to ensure long-term integration of atmospheric...
This Project Grant award for $290,285, funded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), aims to develop an advanced cyberinfrastructure that integrates artificial intelligence (AI) with diverse space weather data to improve forecasting of extreme space weather events. The key products and services to be delivered include: A homogenized dataset of vector magnetograms and open-access computational tools to process...
This $274,912 Project Grant awarded by the National Science Foundation (NSF) under the CFDA 47.084 Technology, Innovation, and Partnerships (TIP) program supports the development of a machine learning model by Salient Predictions Inc. to improve forecasting of extreme heat or cold events 1-month in advance. The broader impact of this project is to help industries such as energy and agriculture increase protective measures and improve resiliency in the face of extreme temperature events, by...
This Project Grant award from the National Science Foundation (NSF) Division of Information and Intelligent Systems, under the Computer and Information Science and Engineering program (CFDA 47.070), will support the development of machine learning methods for estimating the risk of adverse weather and climate events, such as rapid changes in solar power generation and flooding. The University of Hawaii at Manoa, as the prime contractor, will apply advances in generative artificial intelligence...
This $199,339 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program to the California State University San Marcos Corp (CSUSM) will develop novel AI and machine learning models for supervisory control of wind farm connections to the electric grid for stability monitoring. The project aims to create innovative AI/ML models that can directly analyze raw power data to enable accurate fault prediction and detection, which...
This $1,300,000 Cooperative Agreement award from the Department of Energy's Office of Electricity Delivery and Energy Reliability under the Electricity Research, Development and Analysis (CFDA 81.122) program will address the need to improve grid reliability and resilience in the face of increased severe weather risks and integration of offshore wind. The project will leverage novel observations from the WFIP-3 project in the region to develop atmospheric and grid integration tools for...