• MOS;
  • ensemble forecasts;
  • reforecasts;
  • generalized linear models


Statistical post-processing of dynamical forecasts, using the Model Output Statistics (MOS) approach, continues to be an essential component of weather forecasting. Even in the current era of ensemble forecasting, ensemble-MOS methods are used to transform raw ensemble forecasts into well-calibrated probability forecasts. Logistic regression has been found to be an especially useful method for this purpose for predictands, such as precipitation amounts, that are distinctly non-Gaussian. However, the usual implementation of logistic regression fits separate forecast equations for different predictand thresholds, yielding finite sets of threshold probabilities rather than full forecast probability distributions. Furthermore, these individual threshold probabilities are not constrained to be mutually consistent, so that negative probabilities may be implied for some ranges of the predictand. In this paper, logistic regressions are extended to yield full continuous, and coherent, probability distribution forecasts by including the predictand threshold itself as an additional predictor in the forecast equation. The procedure is illustrated using 6–10 day precipitation forecasts for a sample of locations in the U.S., drawn from the GFS reforecast dataset. Copyright © 2009 Royal Meteorological Society