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contributor authorNovak, David R.
contributor authorBailey, Christopher
contributor authorBrill, Keith F.
contributor authorBurke, Patrick
contributor authorHogsett, Wallace A.
contributor authorRausch, Robert
contributor authorSchichtel, Michael
date accessioned2017-06-09T17:36:23Z
date available2017-06-09T17:36:23Z
date copyright2014/06/01
date issued2013
identifier issn0882-8156
identifier otherams-87960.pdf
identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4231686
description abstracthe role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multiyear verification of human-generated short-range precipitation forecasts and medium-range maximum temperature forecasts from the Weather Prediction Center (WPC). Results show that human-generated forecasts improve over raw deterministic model guidance. Over the past two decades, WPC human forecasters achieved a 20%?40% improvement over the North American Mesoscale (NAM) model and the Global Forecast System (GFS) for the 1 in. (25.4 mm) (24 h)?1 threshold for day 1 precipitation forecasts, with a smaller, but statistically significant, 5%?15% improvement over the deterministic ECMWF model. Medium-range maximum temperature forecasts also exhibit statistically significant improvement over GFS model output statistics (MOS), and the improvement has been increasing over the past 5 yr. The quality added by humans for forecasts of high-impact events varies by element and forecast projection, with generally large improvements when the forecaster makes changes ≥8°F (4.4°C) to MOS temperatures. Human improvement over guidance for extreme rainfall events [3 in. (76.2 mm) (24 h)?1] is largest in the short-range forecast. However, human-generated forecasts failed to outperform the most skillful downscaled, bias-corrected ensemble guidance for precipitation and maximum temperature available near the same time as the human-modified forecasts. Thus, as additional downscaled and bias-corrected sensible weather element guidance becomes operationally available, and with the support of near-real-time verification, forecaster training, and tools to guide forecaster interventions, a key test is whether forecasters can learn to make statistically significant improvements over the most skillful of this guidance. Such a test can inform to what degree, and just how quickly, the role of the forecaster changes.
publisherAmerican Meteorological Society
titlePrecipitation and Temperature Forecast Performance at the Weather Prediction Center
typeJournal Paper
journal volume29
journal issue3
journal titleWeather and Forecasting
identifier doi10.1175/WAF-D-13-00066.1
journal fristpage489
journal lastpage504
treeWeather and Forecasting:;2013:;volume( 029 ):;issue: 003
contenttypeFulltext


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