The probabilistic multi-model ensemble prediction system used at WMO LC-LRFMME is based on an uncalibrated MME with model weights being inversely proportional to the random errors in the forecast probability associated with the standard error of the ensemble mean (i.e., proportional to the square root of model ensemble size), and a Gaussian fitting method for the estimation of tercile-based categorical probabilities (Min et al. 2009).
Probabilistic forecasts are issued in the form of tercile-based categorical probabilities (hereafter, tercile probabilities), that is, the probability of the below-normal (BN), near-normal (NN), and above-normal (AN) categories, with respect to climatology. A Gaussian approximation was applied to estimate tercile probabilities. The lower ( ) and upper ( ) terciles are estimated as and , respectively, with and being the mean and standard deviation of the hindcast sample. Forecast probability of each category is estimated as a portion of the cumulative probability of the forecast sample associated with this category.