NOAA Warn-on-Forecast Research and Development
This NOAA grant funds research to improve storm warnings by developing a high-resolution weather prediction system for extreme events like floods and tornadoes.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: February 1, 2025 | May 1, 2025 | August 1, 2025 | November 1, 2025
Funding Amounts: Base stipend approximately $60,000/year plus $3,000 travel allowance; supplements available for certain doctorates; awards typically 2-3 years.
Summary: Supports postdoctoral and senior researchers to develop a high-resolution storm-scale ensemble prediction system for improved severe weather warnings including floods, tornadoes, and tropical cyclones.
Key Information: Open to U.S. citizens, permanent residents, and non-U.S. citizens; requires Ph.D. or equivalent; research conducted on-site at NOAA National Severe Storms Laboratory in Norman, OK.
Description
This fellowship opportunity, offered through the NRC Research Associateship Programs at the National Oceanic and Atmospheric Administration (NOAA), supports research under the Warn-on-Forecast initiative. The goal is to develop and enhance a storm-scale ensemble prediction system that provides next-generation severe storm warning capabilities. The system focuses on high-resolution (~0.5-1 km), regional-scale, on-demand, probabilistic ensemble data assimilation and forecasting for high-impact weather events such as extreme rainfall, flash floods, tornado outbreaks, severe convective thunderstorms causing aviation disruptions, and landfalling tropical cyclones.
Research areas include:
- Development and testing of storm-scale ensemble-based data assimilation techniques (e.g., ensemble Kalman filter, hybrid ensemble-variational methods) for probabilistic short-range forecasts of multi-hazard events.
- Improvement of probabilistic flash flood forecasts through coupled storm-scale atmospheric and distributed hydrological ensemble frameworks.
- Determination of model resolution requirements to predict storm characteristics like low-level rotations, extreme rainfall, damaging winds, and hail.
- Enhancing storm-scale weather prediction by reducing errors in planetary boundary layer and microphysical schemes via improved assimilation of observational data.
- Increasing ensemble reliability and spread by designing storm-scale ensemble systems using multiphysics and stochastic physics techniques.
- Developing forecast verification tools comparing forecasts with analyses and observational datasets.
The research supports the U.S. vision for a Weather Ready Nation and aims to integrate capabilities into operational National Weather Service systems.
