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. In this study, ESVs, targeted in the typhoon area with norms associated with typhoon development, will be derived with a regional ensemble-based assimilation system (WRF-ELTKF). We will examine the characteristic of the typhoon-associated ESV and investigate the relationship between ESVs and forecast errors. For the purpose of improving typhoon assimilation and prediction, the typhoon-associated ESV will be implemented in the WRF-LETKF framework to enhance the flow-dependent structures of the background error covariance.
|Effective start/end date||1/08/15 → 30/09/16|
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