REVISED LEPS SCORES FOR ASSESSING CLIMATE MODEL SIMULATIONS AND LONG-RANGE FORECASTS

The most commonly used measures for verifying forecasts or simulations of continuous variables are root-mean-squared error (rmse) and anomal y correlation. Some disadvantages of these measures are demonstrated. Existing assessment systems for categorical forecasts are discussed br iefly. An alternative unbiased verification measure is developed, know n as the linear error in probability space (LEPS) score. The LEPS scor e may be used to assess forecasts of both continuous and categorical v ariables and has some advantages over rmse and anomaly correlation. Th e properties of the version of LEPS discussed here are reviewed and co mpared with an earlier form of LEPS. A skill-score version of LEPS may be used to obtain an overall measure of the skill of a number of fore casts. This skill score is biased, but the bias is negligible if the n umber of effectively independent forecasts or simulations is large. So me examples are given in which the LEPS skill score is compared with r mse and anomaly correlation.