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Zhongqi Jing and Gerry Wiener

Abstract

The Doppler velocity dealiasing problem has been discussed for many years. Because aliasing is easily identified by detecting abrupt changes in the data field, most existing algorithms use this technique to correct aliased data. Such algorithms are typically based on local expansion methods. Such methods make a dealiasing decision for each gate based on the information of its dealiased neighbors and thus can be sensitive to scattered incorrect data. This paper introduces a new approach that attempts to find all dealiased values for a given dataset by solving a linear system involving the entire dataset and thus avoiding local expansion. Because the solution is global, the new technique is conceptually simple and displays good performance on a number of test cases. The new technique described here was implemented to support real-time dealiasing in an operational setting.

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Michael Dixon and Gerry Wiener

Abstract

A methodology is presented for the real-time automated identification, tracking, and short-term forecasting of thunderstorms based on volume-scan weather radar data. The emphasis is on the concepts upon which the methodology is based. A “storm” is defined as a contiguous region exceeding thresholds for reflectivity and size. Storms defined in this way are identified at discrete time intervals. An optimization scheme is employed to match the storms at one time with those at the following time, with some geometric logic to deal with mergers and splits. The short-term forecast of both position and size is based on a weighted linear fit to the storm track history data. The performance of the detection and forecast were evaluated for the summer 1991 season, and the results are presented.

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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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Amanda R. S. Anderson, Michael Chapman, Sheldon D. Drobot, Alemu Tadesse, Brice Lambi, Gerry Wiener, and Paul Pisano

Abstract

The 2010 Development Test Environment Experiment (DTE10) took place from 28 January to 29 March 2010 in the Detroit, Michigan, metropolitan area for the purposes of collecting and evaluating mobile data from vehicles. To examine the quality of these data, over 239 000 air temperature and atmospheric pressure observations were obtained from nine vehicles and were compared with a weather station set up at the testing site. The observations from the vehicles were first run through the NCAR Vehicle Data Translator (VDT). As part of the VDT, quality-checking (QCh) tests were applied; pass rates from these tests were examined and were stratified by meteorological and nonmeteorological factors. Statistics were then calculated for air temperature and atmospheric pressure in comparison with the weather station, and the effects of different meteorological and nonmeteorological factors on the statistics were examined. Overall, temperature measurements showed consistent agreement with the weather station, and there was little impact from the QCh process or stratifications—a result that demonstrated the feasibility of collecting mobile temperature observations from vehicles. Atmospheric pressure observations were less well matched with surface validation, the degree of which varied with the make and model of vehicle. Therefore, more work must be done to improve the quality of these observations if atmospheric pressure from vehicles is to be useful.

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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.

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