Project Grant 2530610
- This $383,203 federal Project Grant awarded by 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 from climate model variables. The University of Pittsburgh is the primary awardee on this 3-year collaborative research project, working with partners to leverage isotope-enabled global climate model simulations. The goal is to create a smart shortcut that can rapidly...
- 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 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...
- 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...
- The National Science Foundation awarded a $500,000 Project Grant from the Geosciences program (CFDA 47.050) to the University of Maryland, College Park. The grant will fund a collaborative research project titled "CAIG: Reliable Generative Downscaling for Geoscience Data" from October 1, 2025 to September 30, 2028. The project aims to develop a transformative AI-based framework for generating high-fidelity, physically consistent, and uncertainty-calibrated geoscience data. This will...
- 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 Project Grant award from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) provides $1,276,110 to the University Corporation for Atmospheric Research (UCAR) to develop the Isotope-Enabled Community Earth System Model (ICESM). The project aims to improve modeling of the water cycle and enable new scientific insights across atmospheric dynamics, hydrology, and climate change research. Key objectives include...
- The National Science Foundation (NSF) awarded a $660,984 Project Grant under the Geosciences program (CFDA 47.050) to Brown University. The grant, titled "CAIG: Navigating the Climate Science Deluge: Training Language Models to Assist in Comprehensive Assessments," aims to develop an artificial intelligence (AI) climate science chatbot assistant to help researchers more efficiently review and summarize the growing body of climate research literature. The project will create new methods...
- 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...
- This three-year, $511,918 project grant from the National Science Foundation's Geosciences program (CFDA 47.050) funds research to develop a new framework for advancing climate model representations of cloud and convective processes. The grantee, Columbia University, will conduct fully-coupled, isotope-enabled climate simulations for the Last Glacial Maximum, Mid-Holocene, and Pre-Industrial periods using an ensemble of cloud and convective parameter sets. Paleoclimate data assimilation will...
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 interdisciplinary collaboration between climate scientists and AI experts will produce open-source code and data to advance climate science, hydrology, and understanding of Earth's past and future climate patterns. The project also includes plans to share research findings through university courses, training programs, and K-12 STEM outreach.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $319.9k | 7/15/25 |