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Paul Herzegh, Gerry Wiener, Richard Bateman, James Cowie, and Jennifer Black

Abstract

Low cloud ceilings and poor visibility claim the lives of more general aviation (GA) pilots and passengers than any other cause of weather-related GA accidents. Experience shows that instrument-rated pilots as well as those rated only for visual flight are vulnerable to low ceiling and visibility (C&V), making total avoidance the most powerful strategy available to a GA pilot dealing with these hazards. The weather awareness needed for avoidance begins with the recognition of current conditions. This article outlines how fusion of surface, satellite, and terrain data yields a graphical analysis product that enables GA pilots, dispatchers, and weather briefers to better visualize the areal distribution of recent and current C&V conditions across the contiguous U.S. The product is available at www.aviationweather.gov/adds/cv and indicates ceilings less than 1,000 ft above ground level, visibilities less than 3 statute miles, and regions where terrain obscuration is possible. The product is also viewable in the context of interactive geographic information system data via the experimental Helicopter Emergency Medical Services Tool available at http://weather.aero/tools/desktopapps/hemstool. The authors summarize verification results and outline work toward a next-generation product that incorporates the use of model forecast data and weather camera imagery to improve information in data-sparse regions. This next-generation product is in development for initial use in Alaska.

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Sue Ellen Haupt, Branko Kosović, Tara Jensen, Jeffrey K. Lazo, Jared A. Lee, Pedro A. Jiménez, James Cowie, Gerry Wiener, Tyler C. McCandless, Matthew Rogers, Steven Miller, Manajit Sengupta, Yu Xie, Laura Hinkelman, Paul Kalb, and John Heiser

Abstract

As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results.

Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed.

This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

Open access