NOAA Tropical Cyclones: Physical Processes and Forecast Improvements
NOAA seeks research to improve 1-7 day tropical cyclone forecasts by studying air-sea, vortex, turbulent processes, advancing observations/models, and using AI/ML.
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 (Next deadline)
Funding Amounts: $58,000 base stipend per year + $2,000 travel allowance; $4,000 supplements for doctorates in Physical Oceanography or Ocean Chemistry; typical tenure 2-3 years.
Summary: Supports postdoctoral and senior research on tropical cyclone physical processes and forecast improvements at NOAA, emphasizing lifecycle studies, advanced observations, modeling, and AI/ML applications.
Key Information: Open to U.S. citizens, permanent residents, and non-U.S. citizens; requires contacting NOAA research advisers prior to applying; relocation and health insurance benefits included.
Description
The Atlantic Oceanographic and Meteorological Laboratory Hurricane Research Division (HRD) under NOAA offers a research associateship focused on improving 1- to 7-day forecasts of tropical cyclone track, intensity, structure, precipitation, and impacts. This opportunity supports cutting-edge research combining models, theories, and observations, particularly data from research aircraft and NOAA's high-resolution hurricane modeling systems.
Research areas of interest span the entire tropical cyclone lifecycle—from genesis through decay, landfall, or extratropical transition—and include:
- Air-sea and boundary-layer processes: Study of dynamic and thermodynamic processes in the atmospheric boundary layer and adjacent upper ocean affecting cyclone structure and intensity.
- Vortex- and convective-scale processes and multiscale interactions: Understanding symmetric/asymmetric vortex dynamics, convective processes, and environmental interactions influencing track, structure, and intensity.
- Turbulent-scale processes: Analysis of aircraft in situ and remote sensing data to quantify turbulent fluxes, intensity, and mixing length for improved model parameterization.
- Existing and emerging observational technologies: Development and advancement of automated data processing, new observing technologies including uncrewed systems, remote sensing, and in situ instrumentation to better sample undersampled cyclone regions.
- Numerical weather prediction: Enhancements to the Hurricane Analysis and Forecast System (HAFS), including model numerics, grid configurations, physical parameterizations, post-processing, visualization, and process studies.
- Data assimilation: Research on assimilation methods, algorithm improvements, optimal use of observations (aircraft and satellite), model error representation, and parameter estimation, including Observing System Experiments (OSEs) and Observing System Simulation Experiments (OSSEs).
The program encourages innovative approaches including the use of artificial intelligence, machine learning, and cloud computing to advance tropical cyclone forecasting capabilities.
