The optimal ensemble perturbation selection method using breeding concepts in Ensemble Kalman Filter (EnKF) assimilation system is developed and forecast skill using the system is examined with hybrid coupled model. Under the perfect model context, seasonal prediction results confirm that selected ensemble perturbations are fast growing, and the ensemble predictions using selected ensemble members guarantee the skillful forecasts than that using other ensemble members. The correlation skill improvement is about 0.1 robust at 6-8 forecast lead month. It is also found that the forecast skill improvements with selected ensemble members are robust when/where signal-to-noise ratio is small. It means that forecast skill improvement by selecting fast growing ensemble perturbation is significant when/where initial uncertainty is large. It also implies the method helps to reduce the intrinsic predictability barriers like ¢®¡Æspring barrier¢®¡¾. Similarly, during the El Nino events, the prediction skill improvement is embossed during El Nino onset and decaying phases when initial perturbation grows faster than other periods.
AGU Fall Meeting Abstracts
- Pub Date:
- December 2007
- 0312 Air/sea constituent fluxes (3339;
- 3339 Ocean/atmosphere interactions (0312;
- 4504 Air/sea interactions (0312;