Shu-Chih Yang

Department of Atmospheric Sciences,

National Central University,

shuchih.yang@atm.ncu.edu.tw, shuchih.yang@gmail.com

+886-3-4227151#65515

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Research interest

Dr. Shu-Chih Yang received her Phd degree from Department of Atmospheric and Oceanic Science in University of Maryland under Prof. Eugenia Kalnay. Her research interest is to study the numerical forecast skill through data assimilation methods and ensemble forecasting.

During the past few years, Dr. Shu-Chih Yang worked with Prof. Eugenia Kalnay on the issue of understanding the pros and cons for the variational-based and ensemble-based data assimilation schemes (Yang et al. 2008a, Kalnay et al. 2007). They have extended their research goal to improve the performance of the Local Ensemble Transform Kalman filter (LETKF) developed by Hunt et al. (2007) and Ott et al. (2004) in University of Maryland. Her works includes aiming to reduce the analysis computational cost for cases like high-resolution model or dense observation network (Yang et al. 2008b) and improving the longer spin-up problem commonly seen in the Ensemble-based Kalman Filters for meso-small scales (Kalnay and Yang 2008). Recently, she and Prof. Kalnay work on handling the nonlinear problem with LETKF by adapting the advantages in the 4D-Var (Kalnay 2008, Yang and Kalnay 2008).

Starting from 2002, Dr. Yang has been investigating the technique of coupled breeding in the ocean-atmosphere coupled system for isolating the structures of ENSO-related coupled errors (Yang et al. 2006, Yang et al. 2008c). She implemented the coupled breeding in the operational coupled general circulation model developed in NASA/Global modeling and assimilation office (GMAO). During her post-doc at GMAO collaborating with Drs. Michele Rienecker, Christian Keppenne and Eugenia Kalnay, she explored the possibilities of the applications of coupled bred vectors in data assimilation and ensemble forecasting for climate prediction (Yang et al. 2009a, 2009b).

Ongoing work

• "Running in place" with the WRF-LETKF for typhoon assimilation and prediction
To initialize the mesoscale EnKF for a regional model, it is common to use initial conditions from the global (re)analysis products and initial ensemble perturbations constructed based on the 3D-Var background covariance. Such initial conditions don't have enough mesoscale information and the perturbations are less than optimal due to the lack of mesoscale flow-dependency. Therefore, mesoscale EnKF requires a spin-up period to reach its asymptotic level of accuracy.
To accelerate the spin-up, the "running in place" method proposed by Kalnay and Yang (2010, QJRMS) is implemented based on the framework of Local Ensemble Transform Kalman Filter (LETKF) with the Weather Research and Forecasting model (WRF). Recent presentation in WPGM
• Handling nonlinearity and non-Gaussianity with Ensemble Kalman Filter

Recent invited talks

• "Running in place" with Local Ensemble Transform Kalman Filter for typhoon assimilation and prediction. Academia Sinica, Taiwan, Oct 2009
• Applications of coupled bred vectors to ocean data assimilation and its impact on seasonal-to-interannual forecasting, Naval Research Laboratory, USA, Sep 2009
• Applications of Local Ensemble Transform Kalman Filter in meso-scale assimilation and the development of the WRF-LETKF in NCU.Central Weather Bureau, Taiwan, Jun 2009.
• Applications of Local Ensemble Kalman Filter to Typhoon prediction, International Workshop on Advanced Typhoon and Flood Research, Taiwan, Dec 2008.
• Coupled ocean-atmosphere breeding for ensemble forecasting and data assimilation, National Taiwan University, Dec 2008.
• How do Kalman Filters handle the nonlinearity and non-gaussianity? WWRP/THORPEX WORKSHOP on 4D-VAR and Ensemble Kalman Filter Inter-Comparison, Argentina, Nov 2008.