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Neil A. Stuart and Richard H. Grumm

Abstract

Forecasting major winter storms is a critical function for all weather services. Conventional model-derived fields from numerical weather prediction models most frequently utilized by operational forecasters, such as pressure level geopotential height, temperature fields, quantitative precipitation forecasts, and model output statistics, are often insufficient to determine whether a winter storm represents a large departure from normal, or has the potential to produce significant snowfall. This paper presents a method, using normalized departures from climatology, to assist forecasters in identifying long-duration and potentially significant winter storms. The focus of this paper is on anomalous low- and upper-level wind anomalies associated with winter storms along the U.S. east coast.

Observed and forecast low-level (850 hPa) and upper-level (300 and 250 hPa) easterly wind anomalies are compared with a 30-yr (1961–90) reanalysis climatology. Anomalous easterly low-level winds are correlated with enhanced low-level forcing and frontogenesis. Strong low-level winds can also contribute to enhanced precipitation rates. Upper-level winds that are anomalously below normal, represented as easterly wind anomalies, are also correlated with systems that are cut off from the main belt of westerlies, which may result in slower movement of the system, leading to long-duration events. The proposed method of evaluating easterly wind anomalies is shown to assist in identifying potentially slow-moving storms with extended periods of enhanced precipitation.

To illustrate this method, winter storms on 25–26 December 2002 and 2–4 January 2003 will be compared with past historical winter storms. The results suggest that the low- and upper-level wind anomalies in the two recent snowstorms share common characteristics with several record snowstorms over the past 52 yr. Many of these storms were associated with easterly wind anomalies that departed significantly (2 or more standard deviations) from normal. The examination of climatic anomalies from model forecasts may assist forecasters in identifying significant winter storms in the short range (2–3 days) and potentially out to ranges as long as 7 days when ensemble forecast guidance is utilized.

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Neil A. Stuart, David M. Schultz, and Gary Klein

The Second Forum on the Future Role of the Human in the Forecast Process occurred on 2–3 August 2005 at the American Meteorological Society's Weather Analysis and Forecasting Conference in Washington, D.C. The forum consisted of three sessions. This paper discusses the second session, featuring three presentations on the cognitive and psychological aspects of expert weather forecasters. The first presentation discussed the learning gap between students (goal seekers) and teachers (knowledge seekers)—a similar gap exists between forecasters and researchers. In order to most effectively train students or forecasters, teachers must be able to teach across this gap using some methods described within. The second presentation discussed the heuristics involved in weather forecasting and decision making under time constraints and uncertainty. The final presentation classified the spectrum of forecasters from intuitive scientists to the disengaged. How information technology can best be adapted so as not to inhibit intuitive scientists from their mental modeling of weather scenarios is described. Forecasters must continuously refine their skills through education and training, and be aware of the heuristic contributions to the forecast process, to maintain expertise and have the best chance of ensuring a dynamic role in the future forecast process.

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Neil A. Stuart, Patrick S. Market, Bruce Telfeyan, Gary M. Lackmann, Kenneth Carey, Harold E. Brooks, Daniel Nietfeld, Brian C. Motta, and Ken Reeves
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Paul A. Hirschberg, Elliot Abrams, Andrea Bleistein, William Bua, Luca Delle Monache, Thomas W. Dulong, John E. Gaynor, Bob Glahn, Thomas M. Hamill, James A. Hansen, Douglas C. Hilderbrand, Ross N. Hoffman, Betty Hearn Morrow, Brenda Philips, John Sokich, and Neil Stuart

The American Meteorological Society (AMS) Weather and Climate Enterprise Strategic Implementation Plan for Generating and Communicating Forecast Uncertainty (the Plan) is summarized. The Plan (available on the AMS website at www.ametsoc.org/boardpges/cwce/docs/BEC/ACUF/2011-02-20-ACUF-Final-Report.pdf) is based on and intended to provide a foundation for implementing recent recommendations regarding forecast uncertainty by the National Research Council (NRC), AMS, and World Meteorological Organization. It defines a vision, strategic goals, roles and respon- sibilities, and an implementation road map to guide the weather and climate enterprise (the Enterprise) toward routinely providing the nation with comprehensive, skillful, reliable, and useful information about the uncertainty of weather, water, and climate (hydrometeorological) forecasts. Examples are provided describing how hydrometeorological forecast uncertainty information can improve decisions and outcomes in various socioeconomic areas. The implementation road map defines objectives and tasks that the four sectors comprising the Enterprise (i.e., government, industry, academia, and nongovernmental organizations) should work on in partnership to meet four key, interrelated strategic goals: 1) understand social and physical science aspects of forecast uncertainty; 2) communicate forecast uncertainty information effectively and collaborate with users to assist them in their decision making; 3) generate forecast uncertainty data, products, services, and information; and 4) enable research, development, and operations with necessary information technology and other infrastructure. The Plan endorses the NRC recommendation that the National Oceanic and Atmospheric Administration and, in particular, the National Weather Service, should take the lead in motivating and organizing Enterprise resources and expertise in order to reach the Plan's vision and goals and shift the nation successfully toward a greater understanding and use of forecast uncertainty in decision making.

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