Novel Verification Techniques for Warn-on-Forecast Ensembles
This grant offers access to storm forecast data for researchers to develop new ways to verify storm-scale forecasts and assess thunderstorm predictability.
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 (Close date)
Funding Amounts: Base stipend approx. $60,000/year plus $3,000 travel allowance; $24,000 supplement for Electrical Engineering doctorates; experience-based supplements available. Typical award duration 2-3 years.
Summary: Supports postdoctoral and senior researchers to develop novel storm-scale verification and predictability metrics using experimental Warn-on-Forecast ensemble data from NOAA's NSSL.
Key Information: Open to U.S. citizens, permanent residents, and non-U.S. citizens; requires contacting Research Adviser prior to applying; relocation and health insurance benefits included.
Description
This fellowship opportunity invites research proposals focused on developing and improving storm-scale verification techniques and predictability metrics tailored to individual storms. The goal is to enhance the evaluation of very short-range, storm-scale ensemble forecasts, particularly those generated by the experimental Warn-on-Forecast (WoF) system developed by NOAA's National Severe Storms Laboratory (NSSL).
Traditional verification methods often produce misleading results when applied to thunderstorm predictability; thus, storm-based verification techniques are critical for accurate assessment. The successful applicant will gain access to several years of warm season forecast data from the NSSL WoF System to support their research.
Key research themes include:
- Storm-scale verification methodologies
- Thunderstorm predictability metrics
- Evaluation of ensemble forecast sensitivity
- Post-processing and object-based verification techniques
The research is expected to contribute to improved understanding and operational evaluation of severe weather forecasting systems.
