The Contributions of Education and Experience to Forecast Skill

Paul J. Roebber Department of Geosciences, University of Wisconsin-Milwaukee, Wisconsin

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Lance F. Bosart Department of Atmospheric Science, State University of New York at Albany, Albany, New York

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Abstract

An analysis of nine semesters of temperature and precipitation forecasts at the State University of New York at Albany has been conducted with the goal of investigating the dependence of forecasting skill on education and experience. The results show that forecast skill is largely determined by experience. The relative advantage of highly experienced forecasters is secured by virtue of the larger set of cases from which they may draw upon: given a set of forecast information (e.g., moisture, winds and cloud cover), such a forecaster is in a better position to maximize linear consistency between that information and the expected evolution of surface temperature and precipitation (given similar conditions, make a similar forecast) than someone with less forecasting experience. However, the experienced forecaster also gains substantially by recognizing those instances in which these linear relationships no longer apply and by forecasting accordingly. Such instances can often be recognized using simple rules. Consequently, there is a rapid growth of skill with experience for initially inexperienced forecasters; this progression continues through several clearly defined stages and reflects the forecaster's increased ability to implement these simple forecasting strategies. The skill advantage of human forecasters over numerical guidance continues to diminish and now largely reflects the human ability to recognize occasional departures from the linear relationship between forecast information and future observations.

Abstract

An analysis of nine semesters of temperature and precipitation forecasts at the State University of New York at Albany has been conducted with the goal of investigating the dependence of forecasting skill on education and experience. The results show that forecast skill is largely determined by experience. The relative advantage of highly experienced forecasters is secured by virtue of the larger set of cases from which they may draw upon: given a set of forecast information (e.g., moisture, winds and cloud cover), such a forecaster is in a better position to maximize linear consistency between that information and the expected evolution of surface temperature and precipitation (given similar conditions, make a similar forecast) than someone with less forecasting experience. However, the experienced forecaster also gains substantially by recognizing those instances in which these linear relationships no longer apply and by forecasting accordingly. Such instances can often be recognized using simple rules. Consequently, there is a rapid growth of skill with experience for initially inexperienced forecasters; this progression continues through several clearly defined stages and reflects the forecaster's increased ability to implement these simple forecasting strategies. The skill advantage of human forecasters over numerical guidance continues to diminish and now largely reflects the human ability to recognize occasional departures from the linear relationship between forecast information and future observations.

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