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 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 $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 $328,081 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) will support research at Columbia University to understand the drivers of the extreme marine heatwaves that occurred globally in 2023. The project will use observational data, climate models, and artificial intelligence to investigate the roles of surface heat, clouds, and ocean currents in causing these marine heatwaves, which...
This Project Grant award of $199,316.00 from the National Science Foundation's (NSF) Geosciences Program (CFDA 47.050) aims to advance the understanding of how extreme weather events, such as heavy rainfall and flooding, may change in response to future climate scenarios. The project, titled "EMBRACE-AGS-SEED: Harnessing the Power of Machine Learning to Generate Ensembles of Regional Climate Projections," will evaluate whether artificial intelligence and machine learning can provide...
The National Science Foundation Office of International Science and Engineering awarded a $200,000 Project Grant to the University of Chicago to develop responsible artificial intelligence systems to support effective climate change policymaking through 2026. Funded under the CFDA 47.079 International Science and Engineering program, this collaborative research between U.S. and Australian researchers aims to bridge information gaps between publicly available climate change data and actionable...
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 $322,555 federal Project Grant award from the National Science Foundation's (NSF) Mathematical and Physical Sciences program supports research to develop statistical and machine learning methods for studying the dynamics of weather and climate extremes. The three-year project, with a completion date of July 31, 2025, will focus on three key areas: climate model validation, changepoint estimation for extremes, and integration of multi-model climate ensembles. The research aims to enhance...
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...