For developing, deploying, and demonstrating the Weather Support to Deicing Decision Making (WSDDM) system at La Guardia and O'Hare International Airports. WSDDM is an integrated system that depicts short-term forecasts of snowfall rate, wind speed and direction, temperature, and humidity, all in an easy-to-interpret format. Last winter, this FAA-supported system was tested by four airlines at the two airports above, as well as by the New York Traffic Control Center at LaGuardia. One of the system's greatest strengths is its use of snow-gauge data to determine liquid equivalent snowfall rates - the most important factor that determines how often aircraft must be deiced. One study estimated that the annual cost savings to an airport using WSDDM would be more than $1 million. To support the many data sources and widely dispersed users, the WSDDM team created the largest, most complex networking system in RAP's history.

For the National Water Model, the first operational, high-resolution, hydrologic prediction model to be implemented across the continental U.S.

National Water Model: Research to Operations to Research

For developing a display system for the terminal Doppler weather radar

For the development of the Aviation Digital Data Service (ADDS). ADDS is a Web-based information service that makes sophisticated aviation weather products and associated flight-planning tools available to any on-line user. Through several modest grants, the system grew out of an ad-hoc Web site devoted to aviation weather. ADDS is now being used by major airlines, corporations, the armed services, and private aviators. The manager of meteorology for United Airlines said he could "not recall a more significant advance in the production and delivery of essential aviation products than ADDS."

For development of the Dynamic, Integrated Forecast (DICast) system. DICast provides automated weather forecasts for a broad variety of applications around the world. Through a number of commercial vendors across the Internet, DICast satisfies an estimated 50 million forecast requests each day. The system integrates a variety of forecast tools, including National Weather Service statistical output, to provide site- and time-specific forecasts of exceptional accuracy.

For the development of the road weather Maintenance Decision Support System. This decision support tool, which aims to improve the safety and efficiency of winter road maintenance operations, provides transportation managers with recommendations on road maintenance, as well as anticipated consequences of action or inaction.

Thomas Hopson (RAL and SERE*/ASP). Co-nominees include Hai-Ru Chang, Carlos Hoyos, Jun Jian, and Peter Webster (Georgia Institute of Technology); and Selvaraju Ramasamy and A.R. Subbiah (Asian Disaster Preparedness Centre).

For developing and implementing the Climate Forecasting Applications Program to provide operational flash flood forecasts for the country of Bangladesh. In 2007 the system provided skillful forecasts and warnings of two extremely large flooding events, leading to the early evacuation of thousands of vulnerable citizens.

John Hubbert (EOL), Mike Dixon (RAL), Scott Ellis (EOL), and Greg Meymaris (RAL) 

For the Real Time Ground Clutter Mitigation for Weather Radar. Ground clutter contaminates true precipitation echoes, making it difficult to interpret radar data and, therefore, provide accurate forecasts. The nominees have developed a real-time, radar signal processing solution to this serious problem, a fuzzy logic algorithm entitled, Clutter Mitigation Decision (CMD). The CMD will help improve the understanding of the atmosphere, increase societal resilience to weather, and help provide world-class state-of-the-art radar data through this successful research-to-operations enhancement.

Laurie Carson (RAL), Ming Hu (NOAA/ESRL), Xiang-Yu Huang (MMM), Louisa Nance (RAL), Kathryn Newman (RAL), Hui Shao (RAL), Don Stark (RAL), and Chunhua Zhou (RAL).

This team is responsible for establishing and maintaining the code management plan for the Gridpoint Statistical Interpolation (GSI) data assimilation (DA) system. GSI is a state-of-the-art DA system developed by numerous contributors throughout the U.S. It is currently the DA system used operationally within many of the weather forecasting systems run by the National Oceanic and Atmospheric Administration (NOAA) and the Air Force Weather Agency (AFWA). Given the distributed nature of the code development for GSI, as well as the need to ensure an effective transition from the research environment to the demands of 24x7 operations, NOAA and AFWA recognized the importance of rigorous code management for GSI. In 2009, they chose to work with staff at the Developmental Testbed Center (DTC), jointly operated by NCAR/RAL and NOAA/ESRL, to develop, maintain and support a code management framework for GSI. Since 2010, the nominated GSI Code Management Team (GSI CMT) has provided that framework for DA development, creating and managing a code repository for members of the research and operational communities working to advance the science of DA. The GSI team has facilitated and supported the release of five versions of GSI, each providing the community with the latest GSI capabilities, as well as complete documentation and user support. Use of the GSI system has grown dramatically in the past five years and there are now more than 800 registered users in 60 countries. The code management team has played a critical role in this success.