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Collaborative Science, Technology, and Applied Research (CSTAR) Program

The CSTAR Program represents a NOAA/NWS effort to create a cost-effective transition from basic and applied research to operations and services through collaborative research between operational forecasters and academic institutions which have expertise in the environmental sciences. These activities engage researchers and students in applied research of interest to the operational meteorological community and improve the accuracy of forecasts and warnings of environmental hazards by applying scientific knowledge and information to operational products and services.

CSTAR program basics:

  • One to three-year projects--maximum funding level $150K/year
  • Applied research and education projects involve collaboration between operational forecasters and university scientists.
  • Proposals must address NWS science needs and priorities that have the potential to be applied nationally through the Operational Proving Ground (OPG).

Currently Funded CSTAR Projects

University of Oklahoma (July 2013-2016)

A Partnership to Develop, Conduct, and Evaluate Real-time Advanced Data Assimilation and High- Resolution Ensemble and Deterministic Forecasts for Convective-scale Hazardous Weather (Xue, Kong, Brewster, and Jung)

SUNY Stony Brook (Sept 2013-2016)

An Evaluation and Application of Multi-Model Ensembles in Operations for High Impact Weather over the Eastern U.S. (Colle and Chang)

University of Utah (Sept 2013-2016)

Advancing Analysis, Forecast, and Warning Decision Support Capabilities for High-Impact Weather Events (Steenburgh and Horel)

SUNY Albany (Sept 2013-2016)

Collaborative Research with the National Weather Service on the Occurrence and Prediction of High-Impact Precipitation Events in the Northeastern U.S. (Corbosiero and Torn)

Florida State University (Sept 2013-2016)

Improved Forecasting of Extreme Rainfall Events Associated with Tropical Cyclones (Fuelberg and Hart)

University of Washington (Sept 2013-2016)

Application of Dense Surface Observations for High-Resolution Ensemble-Based Analysis and Prediction (Mass and Hakim)

Penn State University (May 2014-2017)

Understanding and Improving the Full Hydrometeorological Forecasting Chain Using Multimodel Ensembles (Mejia and Duffy)

North Carolina State University (May 2014-2017)

Improving Understanding and Prediction of High Impact Weather Associated with Low-Topped Severe Convection in the Southeastern U.S. (Parker, Lackmann, and Xie)

Florida Institute of Technology (July 2014-2017)

An Ensemble-Based Approach to Forecasting Surf, Set-Up, and Surge in the Coastal Zone (Lazarus and Weaver)

Penn State University (May 2014-2017)

Improving Warning Decision Support for Convective Storm Events in the Eastern United States (Markowski and Rigg)

Iowa State University (May 2014-2017)

The Use of Radar Data Assimilation in High-resolution WRF Runs for Improved Short-term QPF for Flood Forecasting, Convective Morphology Prediction, and Probability of Precipitation Guidance (Gallus and Franz)

Texas Tech University (May 2014-2017)

Development of Probabilistic and Sensitivity-Based Forecast Tools to Improve High-Impact Forecasting Guidance at the NWS (Ancell and Weiss)



STI Funding Opportunities

Round 1 of the Research to Operations Initiative

National Weather Service
Office of Science and Technology
Last ModifiedJuly 16, 2014
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