[7-5] Invited

Ensemble Singular Vectors and its application in the EnKF

Shu-Chih Yang (1)(2), Eugenia Kalnay (3) and Takeshi Enomoto (4)

(1) National Central University, Taiwan
(2) RIKEN, Japan
(3)University of Maryland, USA
(4) Kyoto University, Japan

Given an ensemble of forecasts, it is possible to determine the leading ensemble singular vector (ESV), i.e., a linear combination of the forecasts that, given the choice of the perturbation norm and forecast interval, will maximize the growth of the perturbations. Because the ESV indicates the directions of the fastest growing forecast errors, we explore the potential of applying the ESV in Ensemble Kalman Filter (EnKF) for correcting fast growing errors.

The characteristics of ESVs and its relationship to the short-range forecast errors are first examined based on a quasi-geostrophic multi-level channel model under framework of the Local Ensemble Transform Kalman Filter (LETKF). In the QG-LETKF framework, the ESVs are applied as the additive error covariance inflation for improving the structures of the background error covariance. Results show that the positive impact from using ESVs is particularly shown in areas with large growing errors.

For the purpose of improving typhoon data assimilation and prediction, ESVs are derived based on a regional weather model (Weather and Research Forecasting model), targeting in the typhoon area with norms associated with typhoon development. We explore the relationship between ESVs and forecast errors and the possibility of using ESVs as the additive covariance inflation in the complex regional EnKF system. More results will be presented in this talk.