Empirical dynamical modeling for studies of seasonal-to-interannual prediction and predictability
Develop data-driven models, using techniques like deep learning and Koopman mode decomposition, to improve seasonal climate predictions and understand predictability limits.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: May 1, 2025 | August 1, 2025 | November 1, 2025 | February 1, 2026
Funding Amounts: Base stipend approximately $70,000 with experience-based supplements; includes $4,000 travel allowance; typical tenure 2-3 years.
Summary: Postdoctoral and senior researchers can develop advanced data-driven models to improve seasonal-to-interannual climate prediction and understand climate predictability limits at NOAA's Physical Sciences Laboratory.
Key Information: Open to U.S. citizens, permanent residents, and non-U.S. citizens; relocation and health insurance benefits included; requires contacting research advisers prior to application.
Description
This fellowship opportunity at NOAA's Physical Sciences Laboratory (PSL) in Boulder, CO, supports research focused on empirical dynamical modeling to enhance seasonal-to-interannual climate prediction and understanding of climate predictability. The program encourages development of sophisticated data-driven approaches, including deep learning, Linear Inverse Models (LIMs), and emerging nonlinear techniques such as transfer operators and Koopman mode decomposition.
Recent advances in deep learning have revolutionized weather forecasting by leveraging large datasets with high temporal resolution. However, challenges remain for climate time scales due to limited independent samples. Empirical dynamical models developed at PSL have been instrumental in studying phenomena like El Niño-Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO). This research aims to create more accurate reduced-order models that capture essential dynamical processes, improving forecast skill on time scales of months to years.
The fellowship also emphasizes understanding climate extremes affecting water availability, providing valuable insights for NOAA's core partners.
Keywords
- Empirical dynamical models
- Koopman operators
- Climate predictability
- Linear inverse models
- Machine learning
