• Bates, D., Maechler M. , and Bolker B. , cited 2012: lme4: Linear mixed-effects models using S4 classes. R package version 0.999999-0. [Available online at http://CRAN.R-project.org/package=lme4.]

  • Bond, N. A., and Mass C. F. , 2009: Development of skill by students enrolled in a weather forecasting laboratory. Wea. Forecasting, 24, 11411148.

    • Search Google Scholar
    • Export Citation
  • Bosart, L. F., 1975: SUNYA experimental results in forecasting daily temperature and precipitation. Mon. Wea. Rev., 103, 10131020.

  • Bosart, L. F., 1983: An update on trends in skill of daily forecasts of temperature and precipitation at the State University of New York at Albany. Bull. Amer. Meteor. Soc., 64, 346354.

    • Search Google Scholar
    • Export Citation
  • Cervato, C., Gallus W. A. Jr., Boysen P. , and Larsen M. , 2009: Today's forecast: Higher thinking with a chance of conceptual growth. Eos, Trans. Amer. Geophys. Union, 90, 174175, doi:10.1029/2009EO200002.

    • Search Google Scholar
    • Export Citation
  • Cervato, C., Gallus W. A. Jr., Boysen P. , and Larsen M. , 2011: Dynamic weather forecaster: Results of the testing of a collaborative, on-line educational platform for weather forecasting. Earth Sci. Inf., 4, 181189.

    • Search Google Scholar
    • Export Citation
  • Faraway, J., 2006. Extending the Linear Model with R. Chapman and Hall/CRC, 301 pp.

  • Gedzelman, S. D., 1978: Forecasting skill of beginners. Bull. Amer. Meteor. Soc., 59, 13051309.

  • Lepper, M. R., and Cordova D. I. , 1992: A desire to be taught: Instructional consequences of intrinsic motivation. Motiv. Emotion, 16, 187208.

    • Search Google Scholar
    • Export Citation
  • Lippa, R. A., Collaer M. L. , and Peters M. , 2010: Sex differences in mental rotation and line angle judgments are positively associated with gender equality and economic development across 53 nations. Arch. Sex. Behav., 39, 990997.

    • Search Google Scholar
    • Export Citation
  • Olson, D. A., Junker N. W. , and Korty B. , 1995: Evaluation of 33 years of quantitative precipitation forecasts at the NMC. Wea. Forecasting, 10, 498511.

    • Search Google Scholar
    • Export Citation
  • Prensky, M., 2001: Digital Game-Based Learning. McGraw-Hill, 442 pp.

  • R Core Team, cited 2012: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Available online at http://www.R-project.org.]

  • Roebber, P. J., and Bosart L. F. , 1996: The contributions of education and experience to forecast skill. Wea. Forecasting, 11, 2140.

  • Sanders, F., 1973: Skill in forecasting daily temperature and precipitation: Some experimental results. Bull. Amer. Meteor. Soc., 54, 11711179.

    • Search Google Scholar
    • Export Citation
  • Yarger, D. N., Gallus W. A. , Taber M. , Boysen J. P. , and Castleberry P. , 2000: A forecasting activity for a large introductory meteorology course. Bull. Amer. Meteor. Soc., 81, 3139.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 35 29 3
PDF Downloads 9 7 2

Weather Forecasting as a Learning Tool in a Large Service Course: Does Practice Make Perfect?

View More View Less
  • 1 Department of Geological and Atmospheric Sciences, Iowa State University, Ames, Iowa
  • | 2 Department of Statistics, Iowa State University, Ames, Iowa
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

Each spring roughly 200 students, mostly nonmajors, enroll in the Introduction to Meteorology course at Iowa State University and are required to make at least 25 forecasts throughout the semester. The Dynamic Weather Forecaster (DWF) forecasting platform requires students to forecast more than just simple “numeric” forecasts and includes questions on advection, cloudiness, and precipitation factors that are not included in forecast contests often used in meteorology courses. The present study examines the evolution of forecasting skill for students enrolled in the class in spring 2010 and 2011 and compares student performance with that of an “expert forecaster.” The expert forecasters were chosen from meteorology students in an advanced forecasting course who showed exemplary forecasting skill throughout the previous semester. It is shown that these introductory students improve in forecast skill over only the first 10–15 days that they forecast, a number smaller than the 25 days found in an earlier study examining meteorology majors in an upper-level course. The skill of both groups plateaus after that time. An analysis of two types of questions in the DWF reveals that students do have skill slightly better than that of a persistence forecast when predicting parameters traditionally used in forecasting contests, but fail to outperform persistence when predicting more complex atmospheric processes like temperature advection and factors influencing precipitation such as moisture content and instability. The introduction of a contest “with prizes” halfway through the semester in 2011 was found to have at best mixed impacts on forecast skill.

Corresponding author address: William A. Gallus Jr., 3025 Agronomy, Iowa State University, Ames, IA 50011. E-mail: wgallus@iastate.edu

Abstract

Each spring roughly 200 students, mostly nonmajors, enroll in the Introduction to Meteorology course at Iowa State University and are required to make at least 25 forecasts throughout the semester. The Dynamic Weather Forecaster (DWF) forecasting platform requires students to forecast more than just simple “numeric” forecasts and includes questions on advection, cloudiness, and precipitation factors that are not included in forecast contests often used in meteorology courses. The present study examines the evolution of forecasting skill for students enrolled in the class in spring 2010 and 2011 and compares student performance with that of an “expert forecaster.” The expert forecasters were chosen from meteorology students in an advanced forecasting course who showed exemplary forecasting skill throughout the previous semester. It is shown that these introductory students improve in forecast skill over only the first 10–15 days that they forecast, a number smaller than the 25 days found in an earlier study examining meteorology majors in an upper-level course. The skill of both groups plateaus after that time. An analysis of two types of questions in the DWF reveals that students do have skill slightly better than that of a persistence forecast when predicting parameters traditionally used in forecasting contests, but fail to outperform persistence when predicting more complex atmospheric processes like temperature advection and factors influencing precipitation such as moisture content and instability. The introduction of a contest “with prizes” halfway through the semester in 2011 was found to have at best mixed impacts on forecast skill.

Corresponding author address: William A. Gallus Jr., 3025 Agronomy, Iowa State University, Ames, IA 50011. E-mail: wgallus@iastate.edu
Save