An Overview of NSSL R2O Transitions Supporting the NEXRAD Program

Starting with the establishment of the National Severe Storms Laboratory (NSSL) 60 years ago, a primary mission of the laboratory has been to develop and improve weather radar technology, and to perform research and application activities to improve the utility of the data by the meteorological community. Since the formal establishment of the Next Generation Radar (NEXRAD) program in the 1980s, a key research and development focus of NSSL has been on improving the data and products produced by the operational Weather Surveillance Radar-1988 Doppler (WSR–88D) systems that make up the NEXRAD network. The NEXRAD system is managed and overseen by the Radar Operations Center (ROC), with NSSL scientists and engineers working in partnership with the ROC to manage and execute two current NEXRAD research-to-operations (R2O) projects. First, the original Technology Transfer project that began in 1987 develops radar applications and algorithms to support improving upon existing requirements. Second, the Radar Product Improvement (RPI) project was later established to improve data quality (via signal processing techniques) and enhance dual-pol capabilities to satisfy the need for the continued infusion of new capabilities supporting new and emerging requirements.

This presentation will provide an overview of the history of the RPI and Technology Transfer R2O projects, and discuss some of the key successes of these efforts that have resulted in some of the most significant improvements in the weather radar data quality and products that are available today to the weather community and for broader societal applications. Some of the most operationally significant milestones and R2O transitions achieved by these programs will be highlighted, starting with improvements made to base data through development of innovative signal processing techniques. The major improvements to quantitative precipitation estimation through NSSL’s research and development of new techniques that leverage dual-polarization data will be described, followed by a summary of severe weather algorithm developments, culminating in the most recent developments such as the new tornado detection and prediction algorithms that leverage machine learning techniques.