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Weather forecasting as a learning tool in a large service course: Does practice make perfect?

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Monday, 24 January 2011
Weather forecasting as a learning tool in a large service course: Does practice make perfect?
Washington State Convention Center
Elizabeth J. Suess, Iowa State University, Ames, IA; and C. Cervato, W. A. Gallus Jr., and J. M. Hobbs
Manuscript (498.5 kB)

Each spring at Iowa State University, the 200 or more students taking the Introduction to Meteorology course are required to make at least 25 forecasts throughout the semester. The forecast activity, known as the Dynamic Weather Forecaster (DWF), is designed so that students for the most part need to do more than just input numbers found online from other forecasts. They are asked to forecast the 12z and 18z temperatures, the 18z wind speed and direction, and whether or not precipitation will occur during the following day, along with the potential of clouds, fronts, and advection to affect the temperature at both times, and the influence of moisture content, fronts, and atmospheric instability on the precipitation potential. Students in the class come from a variety of majors and include approximately 20-30 first-year meteorology majors. Previous studies (Cervato et al., EOS, 90, 175-176, 2009) have shown that students that begin forecasting early and submit more than the required forecasts overall perform better in the class than students who start forecasting later in the semester or submit just the required number of forecasts. However, we cannot demonstrate that the forecasting per se is the direct cause of the improved performance; it could simply be that students who start forecasting early are more engaged and dedicated and would do well in the class no matter what. This study examines the evolution of forecasting skills in the students enrolled in this class in spring 2010 throughout the semester and compares their performance with the one of an ‘expert forecaster' to eliminate biases related to weather. The expert forecasters were two upper-level meteorology students who showed exemplary forecasting skills in an advanced forecasting course. The data will be analyzed to determine if forecasting accuracy tends to improve the more that students forecast, and if trends are consistent among different majors, academic status (e.g., freshmen versus seniors), and final course grade earned. Other studies (Bond and Mass, Wea. & Forecasting, 24, 1141-1148, 2009) have showed that forecasting skills in meteorology majors enrolled in an advanced forecasting course improve steadily for the first 25 forecasts and then stabilize. We will seek to find out if the same trend is true also for non-majors and majors in an introductory level course.