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Claire H. Jarvis and Neil Stuart

Abstract

This paper explores the derivation and selection of a comprehensive set of continuous topographic and land cover–related variables to guide the interpolation of daily maximum and minimum temperatures over England and Wales, for an entire annual cycle to a resolution of 1 km. The work draws on and updates historical topoclimatic modeling through use of digital elevation data and land cover data, using the modeling capabilities of geographical information systems. The influential guiding variables under a variety of dominant weather patterns were identified and used to assist with the interpolation of an annual sequence of daily maxima and minima for 1976. North map coordinate (“northing”), elevation, and coastal and urban effects were found to be particularly significant variables in explaining the variation in U.K. daily minimum temperature. Urban factors have not previously been thoroughly investigated, despite the high density of population in England and Wales. Analysis of the residuals from data withheld from the partial thin plate spline interpolation suggests that the incorporation of coastal shape and situation, land cover, and soils data might further improve the modeling of local-scale influences on maximum and minimum temperature. They also suggest that the results achieved (rms errors of 0.8°C for maxima and 1.14°C for minima) may be close to the limits of accuracies achievable at 1-km resolution given the density of temperature observation data and standard exposure of the observing network used.

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Claire H. Jarvis and Neil Stuart

Abstract

In a comparative experiment, the sequence of daily maximum and minimum temperatures for 1976 was interpolated over England and Wales to a resolution of 1 km using partial thin plate splines, ordinary kriging, trend surface, and an automatic inverse-distance-weighted method of interpolation. A “level playing field” for comparing the estimation accuracies was established through the incorporation of a consistent set of guiding variables in all interpolators. Once variables were included to guide the interpolators, differences in estimation accuracy among partial thin plate splines, ordinary kriging, and inverse distance weighting results were not significant although the performance of trend surface analysis was poorer. Best accuracies were achieved using partial thin plate splines, with jackknife cross-validation root-mean-square errors of 0.8°C for an annual series of daily maximum temperatures and 1.14°C for daily minimum temperatures. The results from this study suggest that sole reliance on the selection of guiding variables can be a less efficient means of achieving the required accuracies than the placing of greater reliance on empirical techniques of interpolation that can account for known autocorrelation in the temperature data. The use of guiding variables narrows the gap between performance of the different interpolation methods. In general, however, more sophisticated interpolators such as kriging and splining require fewer guiding covariates to achieve similar estimation accuracies. Day-to-day variability in the interpolation accuracies confirms the need for increased adaptability in the manner in which the guiding variables are incorporated in the interpolation process.

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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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Richard Turner, Xiaogu Zheng, Neil Gordon, Michael Uddstrom, Greg Pearson, Rilke de Vos, and Stuart Moore

Abstract

Wind data at time scales from 10 min to 1 h are an important input for modeling the performance of wind farms and their impact on many countries’ national electricity systems. Planners need long-term realistic (i.e., meteorologically spatially and temporally consistent) wind-farm data for projects studying how best to integrate wind power into the national electricity grid. In New Zealand, wind data recorded at wind farms are confidential for commercial reasons, however, and publicly available wind data records are for sites that are often not representative of or are distant from wind farms. In general, too, the public sites are at much lower terrain elevations than hilltop wind farms and have anemometers located at 10 m above the ground, which is much lower than turbine hub height. In addition, when available, the mast records from wind-farm sites are only for a short period. In this paper, the authors describe a novel and practical method to create a multiyear 10-min synthetic wind speed time series for 15 wind-farm sites throughout the country for the New Zealand Electricity Commission. The Electricity Commission (known as the Electricity Authority since 1 October 2010) is the agency that has regulatory oversight of the electricity industry and that provides advice to central government. The dataset was constructed in such a way as to preserve meteorological realism both spatially and temporally and also to respect the commercial secrecy of the wind data provided by power-generation companies.

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Suzanne Rosier, Sam Dean, Stephen Stuart, Trevor Carey-Smith, Mitchell T. Black, and Neil Massey
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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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