Convection-permitting to convection-resolving data assimilation and numerical weather prediction
Research opportunities exist at NOAA/GSL to improve data assimilation and weather prediction models for hazardous weather, focusing on the Rapid Refresh Forecast System.
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 (Application Deadline)
Funding Amounts: $70,000 base stipend plus $4,000 travel allowance; stipend increases with experience; typical fellowship duration 2-3 years.
Summary: Postdoctoral and senior research fellowship at NOAA/GSL focused on advancing convection-permitting and convection-resolving data assimilation and numerical weather prediction models, including contributions to the Rapid Refresh Forecast System.
Key Information: Open to U.S. citizens, permanent residents, and non-U.S. citizens; relocation and health insurance benefits included; requires contacting a research adviser prior to application.
Description
This fellowship opportunity is offered by the Assimilation and Verification Innovation Division (AVID) at NOAA's Global Systems Laboratory (GSL) in Boulder, CO. It supports postdoctoral and senior researchers aiming to improve the accuracy and timeliness of hazardous weather analysis and prediction through advanced data assimilation and numerical weather prediction at high temporal frequency and spatial resolution.
The research focuses on convection-permitting and convection-resolving scales, critical for severe weather, aviation, hydrology, and energy applications. A key project is the development of the Rapid Refresh Forecast System (RRFS), a regional, rapidly updating model within NOAA’s Unified Forecast System (UFS).
Research topics include:
- Incorporation of new observational data sources such as Unmanned Aircraft Systems (UAS) and enhanced satellite data.
- Enhancements to existing observation inputs like radar and aircraft data.
- Development and application of advanced data assimilation techniques, including ensemble and ensemble-variational methods, particle filters, and machine learning approaches.
Awardees will have access to state-of-the-art facilities and mentorship, with opportunities to contribute to operational weather prediction systems.
