Xingxiu Deng
Meteorological Service of Canada
Peter Houtekamer
Atmospheric Science and Technology Directorate, Environment Canada
The 8th EnKF Data Assimilation Workshop
Use of a 3h window in the Global Ensemble Kalman Filter
Talk:
The operational Ensemble Kalman Filter (EnKF) for the Canadian Global Ensemble Prediction System (GEPS) will move to a 39km Yin-Yang grid from a 50km Gaussian grid. With a recent successful migration to a new High-Performance Computing solution at Canadian Meteorological Center, the resolution of the GEPS EnKF systems can be increased further in the future. As the resolution increases, more rapidly changing dynamical features can be captured. A shorter assimilation window may permit a better tracking of rapidly evolving high-resolution features. Therefore, a study is performed to compare our conventional 6 hour data assimilation window with a shorter 3 hour assimilation window. In both cases, the Incremental Analysis Updating (IAU) approach is used to minimize the shocks due to the addition of analysis increments. The preliminary results of the impact on model balance, innovation statistics and the medium-range forecasts will be shown
The operational Ensemble Kalman Filter (EnKF) for the Canadian Global Ensemble Prediction System (GEPS) will move to a 39km Yin-Yang grid from a 50km Gaussian grid. With a recent successful migration to a new High-Performance Computing solution at Canadian Meteorological Center, the resolution of the GEPS EnKF systems can be increased further in the future. As the resolution increases, more rapidly changing dynamical features can be captured. A shorter assimilation window may permit a better tracking of rapidly evolving high-resolution features. Therefore, a study is performed to compare our conventional 6 hour data assimilation window with a shorter 3 hour assimilation window. In both cases, the Incremental Analysis Updating (IAU) approach is used to minimize the shocks due to the addition of analysis increments. The preliminary results of the impact on model balance, innovation statistics and the medium-range forecasts will be shown
Thanks to McGill's Atmospheric and Oceanic Sciences department for hosting this web-site.