Collaborative Research: CAIG: Reliable Generative Downscaling for Geoscience Data — NSF Award to University of Maryland, College P
High-resolution geoscience data are essential for understanding and predicting extreme weather events, yet producing such data remains a major challenge due to limitations in observational infrastructure and computational cost. This project introduces a transformative AI-based framework to overcome these barriers by ge
| Award title | Collaborative Research: CAIG: Reliable Generative Downscaling for Geoscience Data |
|---|---|
| Award ID | 2530596 |
| Awardee | University of Maryland, College Park |
| City | COLLEGE PARK |
| State | MD |
| Amount obligated | $500,000 |
| Principal investigator | Haizhao Yang |
| Program | GEO CI - GEO Cyberinfrastrctre |
| Start date | 10/01/2025 |
| Abstract | High-resolution geoscience data are essential for understanding and predicting extreme weather events, yet producing such data remains a major challenge due to limitations in observational infrastructure and computational cost. This project introduces a transformative AI-based framework to overcome these barriers by generating high-fidelity, physically consistent, and uncertainty-calibrated geoscience data. These enhanced datasets will empower better decision-making in disaster preparedness, eme |
| Source | NSF Awards |
$799/mo
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