• Agresti, A., 1990: Categorical Data Analysis. John Wiley and Sons, 558 pp.

  • Akaike, H., 1973: Information theory and an extension of the maximum likelihood principle. Proc. Second Int. Symp. on Information Theory, Budapest, Hungary, Akademiai Kaido, 267–281.

    • Search Google Scholar
    • Export Citation
  • Applequist, S., , Gahrs G. E. , , Pfeffer R. L. , , and Niu X-F. , 2002: Comparison of methodologies for probabilistic quantitative precipitation forecasting. Wea. Forecasting, 17 , 783799.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brier, G. W., 1950: Verification of forecasts expressed in terms of probability. Mon. Wea. Rev., 78 , 13.

  • Buizza, R., , Hollingsworth A. , , Lalaurette F. , , and Ghelli A. , 1999: Probabilistic predictions of precipitation using the ECMWF Ensemble Prediction System. Wea. Forecasting, 14 , 168189.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, J., , Mullen S. L. , , and Sanders F. , 1997: Short-range ensemble forecasting of quantitative precipitation. Mon. Wea. Rev., 125 , 24272459.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ebert, E. E., 2001: Ability of a poor man's ensemble to predict the probability and distribution of precipitation. Mon. Wea. Rev., 129 , 24612480.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elsner, J. B., , and Schmertmann C. P. , 1994: Assessing forecast skill through cross validation. Wea. Forecasting, 9 , 619624.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Glahn, H. R., , and Lowry D. A. , 1972: Use of model output statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11 , 12031211.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hamill, T. M., , and Colucci S. J. , 1998: Evaluation of the Eta/RSM ensemble probabilistic precipitation forecasts. Mon. Wea. Rev., 126 , 711724.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MathSoft Inc., 1999: S-Plus 2000 Guide to Statistics. Vol. 2. Data Analysis Products Division, MathSoft, 582 pp.

  • Murphy, A. H., 1973: A new vector partition of the probability score. J. Appl. Meteor., 12 , 595600.

  • Schwarz, G., 1978: Estimating the dimension of a model. Ann. Stat., 6 , 461464.

  • Wedderburn, R. W. M., 1976: On the existence and uniqueness of the maximum likelihood estimates for certain generalized linear models. Biometrika, 63 , 2732.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weisberg, S., 1985: Applied Linear Regression. John Wiley and Sons, 324 pp.

  • Weiss, N. A., , and Hassett M. J. , 1991: Introductory Statistics. Addison-Wesley, 834 pp.

  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 164 164 1
PDF Downloads 5 5 0

Improved Results for Probabilistic Quantitative Precipitation Forecasting

View More View Less
  • 1 Geophysical Fluid Dynamics Institute and Meteorology Department, The Florida State University, Tallahassee Florida
  • | 2 Department of Statistics, The Florida State University, Tallahassee, Florida
© Get Permissions
Restricted access

Abstract

As a follow-up to a recent paper by the authors in which various methodologies for probabilistic quantitative precipitation forecasting were compared, it is shown here that the skill scores for linear regression and logistic regression can be improved by the use of alternative methods to obtain the model order and the coefficients of the predictors. Moreover, it is found that an even simpler, and more computationally efficient, methodology, called binning, yields Brier skill scores that are comparable to those of logistic regression. The Brier skill scores for both logistic regression and binning are found to be significantly higher at the 99% confidence level than the ones for linear regression.

In response to questions that have arisen concerning the significance test used in the authors' previous study, an alternative method for determining the confidence level is used in this study and it is found that it yields results comparable to those obtained previously, thereby lending support to the conclusion that logistic regression is significantly more skillful than linear regression.

Current affiliation: Air Force Combat Climatology Center, Asheville, North Carolina

Corresponding author address: Gregory E. Gahrs, Geophysical Fluid Dynamics Institute, The Florida State University, 18 Keen Bldg., Tallahassee, FL 32306-4360. Email: gahrs@gfdi.fsu.edu

Abstract

As a follow-up to a recent paper by the authors in which various methodologies for probabilistic quantitative precipitation forecasting were compared, it is shown here that the skill scores for linear regression and logistic regression can be improved by the use of alternative methods to obtain the model order and the coefficients of the predictors. Moreover, it is found that an even simpler, and more computationally efficient, methodology, called binning, yields Brier skill scores that are comparable to those of logistic regression. The Brier skill scores for both logistic regression and binning are found to be significantly higher at the 99% confidence level than the ones for linear regression.

In response to questions that have arisen concerning the significance test used in the authors' previous study, an alternative method for determining the confidence level is used in this study and it is found that it yields results comparable to those obtained previously, thereby lending support to the conclusion that logistic regression is significantly more skillful than linear regression.

Current affiliation: Air Force Combat Climatology Center, Asheville, North Carolina

Corresponding author address: Gregory E. Gahrs, Geophysical Fluid Dynamics Institute, The Florida State University, 18 Keen Bldg., Tallahassee, FL 32306-4360. Email: gahrs@gfdi.fsu.edu

Save