Probabilistic Seasonal Forecasts in the North American Multimodel Ensemble: A Baseline Skill Assessment

摘要

The North American Multimodel Ensemble (NMME) forecasting system has been continuously producing seasonal forecasts since August 2011. The NMME, with its suite of diverse models, provides a valuable opportunity for characterizing forecast confidence using probabilistic forecasts. The current experimental probabilistic forecast product (in map format) presents the most likely tercile for the seasonal mean value, chosen out of above normal, near normal, or below normal categories, using a nonparametric counting method to determine the probability of each class. The skill of the 3 -month -mean probabilistic forecasts of 2-m surface temperature (T2m), precipitation rate, and sea surface temperature is assessed using forecasts from the 29-yr (1982-2010) NMME hindcast database. Three forecast configurations are considered: a full six model NMME; a "mini-NMME" with 24 members, four each from six models; and the 24 -member CFSv2 alone. Skill is assessed on the cross -validated hindcasts using the Brier skill score (BSS); forecast reliability and resolution are also assessed. This study provides a baseline skill assessment of the current method of creating probabilistic forecasts from the NMME system.