This $225,351 Project Grant award, funded by the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) program (CFDA 47.070), will support the development of AI emulator tools for improving the estimation of statistics for rare and extreme climate events, such as heat waves and cold spells. The project, led by New York University (NYU), will focus on creating novel methods to leverage AI techniques to better model and predict the impacts of these...
This $798,840 federal Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) supports research to enhance forecasting capabilities for high-impact weather events like heat waves, cold snaps, wildfires, and heavy rainfall. The project aims to integrate physics-based atmospheric science with state-of-the-art machine learning techniques, generating stochastic models to better predict midlatitude weather patterns and extreme events. This research will contribute...
This Project Grant award of $500,000.00 from the National Science Foundation's Geosciences Program (CFDA 47.050) is funding the development of a transformative AI-based framework for generating high-fidelity, physically consistent, and uncertainty-calibrated geoscience data. The goal is to overcome limitations in observational infrastructure and computational cost to produce enhanced datasets that can improve decision-making for disaster preparedness, emergency response, and infrastructure...
This federal Project Grant award of $443,674 from the National Science Foundation's Geosciences Program (CFDA 47.050) will support research to develop advanced artificial intelligence (AI) models for predicting the initiation and evolution of thunderstorms. The project aims to enable significant breakthroughs in understanding convection initiation dynamics and improving the predictability of hazardous weather events like heavy rainfall, strong winds, hail, and tornadoes. Key objectives...
This $397,337 Project Grant from the National Science Foundation's Geosciences program (CFDA 47.050) supports research into the drivers and impacts of warm-season climate extremes in North America under a changing climate. The awardee, Northern Illinois University, will use high-resolution global climate modeling to examine how factors across various spatial scales influence extreme events like floods, heat waves and droughts. Researchers will simulate past and potential future warm seasons to...
The National Science Foundation (NSF) awarded a $300,000 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to the University of Massachusetts (UMass) to investigate distributed machine learning for climate modeling and weather forecasting. Through this EAGER (EArly-concept Grants for Exploratory Research) award, UMass will leverage existing testbeds, including the CASA radar network, to support the development of a National Discovery Cloud for Climate...
The National Science Foundation (NSF) awarded a $497,419 Project Grant under the Computer and Information Science and Engineering (CFDA 47.070) program to Colorado State University (CSU). The grant, titled "CAIG: Toward a Deeper Understanding of Cloud Processes and Future Storm Modes Using AI," aims to develop novel algorithms that leverage artificial intelligence (AI) and advanced mathematics to gain a deeper understanding of cloud formation and hazardous weather patterns in the...
This Project Grant award from the National Science Foundation (NSF) under the Computer and Information Science and Engineering (CFDA 47.070) program provides $449,321 to the University of California, Santa Cruz (UCSC) to develop AI-based atmospheric emulators that can reliably model climate change and extreme weather events. The project aims to create physically-consistent and numerically stable deep learning models to simulate atmospheric dynamics at a fraction of the computational cost of...
This $399,162 Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) aims to develop interpretable, stable, and mass-conserving artificial intelligence (AI) models to improve the computational speed and efficiency of geoscientific models, such as those used for air pollution and climate research. The project will create simpler "surrogate" machine learning models for key components like atmospheric chemistry and wildfire plume rise, allowing for...
This $284,443 Project Grant award was provided by the National Science Foundation (NSF) through its Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) to the University of Chicago. The project, titled "COLLABORATIVE RESEARCH: CAIG: DEVELOPING AI EMULATOR TOOLS FOR EXTREME EVENTS WITH APPLICATION TO HEAT WAVES AND COLD SNAPS", aims to develop advanced mathematical tools and AI emulation techniques to improve the estimation of rare climate events,...