NOAA testbeds bring mature research into an operations-like environment, allowing forecasters and researchers to test and demonstrate products in real-world conditions to fine-tune tools and methods for operational use.
The FY25 NOFO supported five testbeds: Climate, Fire Weather, Hazardous Weather, Hurricane and Ocean, and Hydrometeorological. The priorities for each were:
- Climate Testbed: Improvements to data assimilation systems, developmental activities to accelerate the UFS Seasonal Forecast System (SFS), and activities leading to improvements to methods used to make subseasonal-to-seasonal outlooks.
- Fire Weather Testbed: Fire weather forecast verification and predictability and social science research and decision support services.
- Hazardous Weather Testbed: Concepts and techniques to improve convection-allowing model ensemble performance, post-processing and verification techniques for deterministic models/ensembles, new high temporal and spatial resolution observation datasets, and relevant social and behavioral science theoretical frameworks.
- Hurricane and Ocean Testbed: Near real-time 2D analysis of the near-surface wind and wave field in tropical cyclones (TCs), guidance on the best supplemental observing system strategies, TC guidance (0-7 days) and/or forecasters’ ability to interrogate guidance, interpret and integrate disparate real-time observational data, and enable forecasters to diagnose forecast model characteristics.
- Hydrometeorological Testbed: Probabilistic flash flood and winter precipitation forecasting, post-processing and verification for flash flood and winter precipitation forecasts, enhance forecaster use of probabilistic information, and atmospheric forcings for hydrologic models.
| Project Title | Principal Investigators | Project Description | Hazard Focus |
|---|---|---|---|
| Forecasting and Verification for the Fire Weather Testbed | Kyle Hilburn, Colorado State University | Demonstrate and validate WRF-SFIRE forecasts initialized with Next Generation Fire System (NGFS) ignition points through the Fire Weather Testbed. This will involve development of a Recommender tool to integrate the forecasts into Hazard Services, collecting user feedback, and statistical validation. | Fire Weather |
| Artificial Intelligence Weather Prediction Models for Operational Tropical Cyclone Forecasting | Kate Musgrave, Colorado State University | Evaluate AI weather prediction models for use in tropical cyclone (TC) forecasting applications and provide AI model-based TC guidance products (including track, intensity, environment, genesis, consensus, and ensemble-based) to the National Hurricane Center. | Tropical Cyclones |
| Modernizing Flight Planning Software for Hurricane Reconnaissance Missions | Jason Dunion, University of Miami Alan Brammer, Colorado State University | Develop next-generation aircraft flight planning software for use by NOAA’s National Hurricane Center and NOAA laboratories (e.g., AOML/HRD) that use NOAA aircraft to conduct research. This improved software will enhance flight planning capabilities and streamline flight planning, which will reduce errors and improve efficiency during high-impact weather events. | Tropical Cyclones |
| A Convection-allowing Ensemble System for Short-term Probabilistic Forecasts of Severe Convective Hazards Based on the MPAS Dynamic Core | Nusrat Yussouf, University of Oklahoma Craig Schwartz, NSF NCAR | Evaluate a Model Prediction Across Scales (MPAS)-based data assimilation (DA) and prediction system in HMT and HWT experiments for short-term probabilistic guidance of severe thunderstorms. Migrate from the Data Assimilation Research Testbed (DART) to Joint Effort for Data assimilation Integration (JEDI) software, which will be the backbone of future convective-scale DA systems. | Severe Weather, Flooding and Precipitation |
| Developing a Prototype Framework for Model Development, Diagnostics, and Experimental Real-Time Forecast Application Effort for Seasonal Forecast System (SFS) | Tao Zhang, University of Maryland | Develop and demonstrate the feasibility of a cost-effective framework for implementing the future versions of seasonal forecast systems to: • Replace the current version that was implemented in 2011 (Climate Forecast System version 2 – CFSv2) • Provide updates on a periodic basis | Relevant to All Hazards |
