Open AccessToward Random Sampling of Model Error in the Canadian Ensemble Prediction System
Author(s) -
Martin Charron,
G. Pellerin,
Ľuboš Spaček,
P. L. Houtekamer,
Normand Gag,
Herschel L. Mitchell,
Laurent Michelin
Publication year - 2010
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/2009mwr3187.1
Subject(s) - kalman filter , probabilistic logic , ensemble kalman filter , ensemble forecasting , meteorology , computer science , sensitivity (control systems) , environmental science , grid , sampling (signal processing) , precipitation , filter (signal processing) , extended kalman filter , geology , geodesy , machine learning , artificial intelligence , geography , electronic engineering , engineering , computer vision
An updated global ensemble prediction system became operational at the Meteorological Service of Canada in July 2007. The new elements of the system include the use of 20 members instead of 16, a single dynamical core [the Global Environmental Multiscale (GEM) model], stochastic physical tendency perturbations and a kinetic energy backscatter algorithm, an ensemble Kalman filter with four-dimensional data handling, and a decrease from 1.2° to 0.9° in horizontal grid spacing. This system is compared with the former operational one using a variety of probabilistic measures. For global upper-air dynamical fields, the improvement in predictive skill for equivalent forecast quality is from 9 to 16 h around day 6. Precipitation forecasts, verified over Canada, are also significantly improved. The impact of each of the abovementioned new elements of the ensemble prediction system is also evaluated separately in a series of sensitivity experiments for which one given element is removed from the system.