This Project Grant award for $100,351 was provided by the National Science Foundation's Geosciences Program (CFDA 47.050) to New York University (NYU) with a performance period from October 1, 2024 to September 30, 2027. The award supports collaborative research to develop AI emulator tools for improving the estimation of rare and extreme climate events, such as heat waves and cold snaps. The key products and services to be delivered include: Developing AI Dynamic Galerkin Approximation (AI-DGA)...
This federal Project Grant award for $449,321.00, provided by the National Science Foundation (NSF) through its Computer and Information Science and Engineering (CISE) Program (CFDA 47.070), supports the development of physically-consistent and long-term stable deep learning-based atmospheric emulators. The key innovations focus on preserving small-scale physical consistency in deep learning models and enabling stability analysis to generate reliable long-term climate projections. The award...
This $348,127 National Science Foundation (NSF) award under the Geosciences program (CFDA 47.050) supports collaborative research to develop new physics-informed machine learning and stochastic modeling techniques for improving magnetohydrodynamics (MHD) simulations of the interaction between the solar wind and Earth's magnetosphere. The University of California, Los Angeles (UCLA) will lead this 3-year project, which aims to enhance the performance of global magnetosphere MHD models used for...
This Project Grant award from the National Oceanic and Atmospheric Administration (NOAA) Small Business Innovation Research (SBIR) Program, CFDA 11.021, provides $1,298,986 to Climate Forecast Applications Network, LLC to develop a high-impact innovation for weather forecasting. The innovation integrates global ensemble weather forecasts with an AI-driven post-processing model of extreme weather indices (XTREMECAST). This will enable skillful probabilistic forecasts of compound extreme weather...
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 $518,425 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop a physics-informed graph neural network-based approach for forecasting snow water equivalent (SWE), which is critical for effective water resource management in the Western United States. The project will integrate artificial intelligence techniques, specifically physics-informed neural networks, to create more accurate and reliable SWE forecasts by capturing the detailed...
This five-year $409,104 National Science Foundation project grant supports research at Cornell University to develop physics-informed machine learning techniques for climate adaptation pathways modeling in eco-hydrologic systems. Specifically, the grantee will apply innovative machine learning approaches to process-guided climate simulation, hydrologic prediction, and sub-seasonal to seasonal forecasting to examine climate change mechanisms influencing the Lake Ontario system. The grantee will...
This $284,443 Project Grant award, provided by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), will support the development of advanced AI emulator tools for improved estimation of rare and extreme climate events, such as heat waves and cold snaps. The award, granted to the University of Chicago, will focus on creating mathematical tools to leverage AI methods to enhance the analysis of rare climate phenomena. Specifically,...
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) 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...