CAIG: Deep Learning-based Stochastic Models for Large-Scale Atmospheric Variability and Ex — NSF Award to University of California
Extreme weather events—such as heat waves, cold snaps, wildfires, and heavy rainfall—pose increasing risks to society. These events are often driven by shifts in powerful atmospheric jet streams, which typically flow from west to east across the midlatitudes but can sometimes meander dramatically north or south. While
| Award title | CAIG: Deep Learning-based Stochastic Models for Large-Scale Atmospheric Variability and Ex |
|---|---|
| Award ID | 2531008 |
| Awardee | University of California-Los Angeles |
| City | LOS ANGELES |
| State | CA |
| Amount obligated | $798,840 |
| Principal investigator | Gang Chen |
| Program | GEO CI - GEO Cyberinfrastrctre |
| Start date | 10/01/2025 |
| Abstract | Extreme weather events—such as heat waves, cold snaps, wildfires, and heavy rainfall—pose increasing risks to society. These events are often driven by shifts in powerful atmospheric jet streams, which typically flow from west to east across the midlatitudes but can sometimes meander dramatically north or south. While recent advances in AI have shown promise in improving weather forecasts, many AI models still struggle with long-term stability, limiting their effectiveness in predicting extreme |
| Source | NSF Awards |
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