Probing the Limits of Atmospheric Predictability with a Deep Learning Framework — NSF Award to University of Washington (WA, $337,
Weather forecasts are believed to be inherently limited by the growth of small errors present at the initial time, with all skill lost by about two weeks. This long-held belief derives from experiments with traditional models that represent the laws of physics for the atmosphere. Recently, forecast models based on mach
| Award title | Probing the Limits of Atmospheric Predictability with a Deep Learning Framework |
|---|---|
| Award ID | 2501400 |
| Awardee | University of Washington |
| City | SEATTLE |
| State | WA |
| Amount obligated | $337,147 |
| Principal investigator | Gregory Hakim |
| Program | Climate & Large-Scale Dynamics |
| Start date | 08/15/2025 |
| Abstract | Weather forecasts are believed to be inherently limited by the growth of small errors present at the initial time, with all skill lost by about two weeks. This long-held belief derives from experiments with traditional models that represent the laws of physics for the atmosphere. Recently, forecast models based on machine learning (ML) have emerged with skill comparable to the physics-based models. Since the ML models do not solve to solve physical equations but only learn from data, they provid |
| Source | NSF Awards |
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