Project Grant 2530920
- The National Science Foundation (NSF) awarded a $783,920 project grant under its Geosciences Program (CFDA 47.050) to Colorado State University (CSU) for the project "COLLABORATIVE RESEARCH: CAIG: UNDERSTANDING RADIATIVE FEEDBACKS, OCEAN HEAT UPTAKE, AND ENERGY CONSERVATION TO IMPROVE ML-CLIMATE EMULATIONS." The project aims to develop climate emulators using machine learning to extract statistical relationships from observational data and physics-based climate simulations, with 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 $252,059 Project Grant award from the National Science Foundation's Geosciences program (CFDA 47.050) aims to develop a machine learning-based "emulator" that can efficiently predict water isotope patterns in fully-coupled global climate models. The goal is to build a powerful tool that can enhance climate science, hydrology, and understanding of the Earth's past and future climate by leveraging water isotope data. The University Corporation for Atmospheric Research (UCAR),...
- This National Science Foundation (NSF) Geosciences Program (CFDA 47.050) Project Grant award of $319,907 to the University of Maryland, College Park aims to build a machine-learning-based "emulator" that can efficiently predict water isotope patterns in climate models. The three-year project, starting November 1, 2025, seeks to develop a smart shortcut to incorporate water isotopes, which act as natural recorders of climate history, into complex climate simulations. This...
- 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...
- 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 National Science Foundation (NSF) Project Grant award under the Geosciences Program (CFDA 47.050) provides $300,000 in funding to the Massachusetts Institute of Technology (MIT) from November 15, 2024 to October 31, 2027. The award supports the development of machine learning-powered "surrogate models" to increase the computational speed and efficiency of geophysical models used for air pollution and climate research. Key project objectives include: Creating simplified,...
- 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 $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...
- This Project Grant award of $798,840 from the National Science Foundation's Geosciences Program (CFDA 47.050) supports research at the University of California, Los Angeles (UCLA) to develop deep learning-based stochastic models for improving forecasts of high-impact weather events such as heat waves, cold snaps, wildfires, and heavy rainfall. The project aims to integrate physics-based atmospheric science with advanced machine learning techniques to enhance the ability to predict these...
This Project Grant award from the National Science Foundation's Geosciences Program (CFDA 47.050) provides $410,837 to Boston University to conduct collaborative research aimed at improving the performance of climate models used to simulate weather and climate. The research addresses two key shortcomings of current climate models: their computational intensity and lack of incorporation of observational data. The project will develop climate emulators that use machine learning to extract statistical relationships from observations and physics-based simulations, with the goal of creating more computationally efficient and data-driven climate models. Specifically, the work will evaluate how well these emulators capture the underlying physics of the climate system by simulating the response to regional temperature variations. The research seeks to advance understanding of coupled climate dynamics, including the effects of regional temperature changes on the global energy balance and precipitation patterns. This award supports the project from January 1, 2026 through December 31, 2028.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $410.8k | 7/16/25 |