Assimilation of GPS Radio Occultation (RO) Refractivity Data with Hybrid-Gain Data Assimilation Algorithm

Chih-Chien Chang* and Shu-Chih Yang
Department of Atmospheric Sciences, National Central University, JhongLi, Taiwan

Hybrid Data Assimilation (HDA) has been widely used in modern numerical weather prediction (NWP). Most of the HDA systems are established on the basis of variational perspective. The concept is to blend the static background error covariance used in the variational systems (VARs) with the flow-dependent background error covariance from Ensemble Kalman Filter. Recently, a novel algorithm proposed by Penny (2014) demonstrates a different hybrid concept based on EnKF perspective, called hybrid-gain data assimilation (HGDA). Instead of blending the background error covariance from both systems, HGDA combines the gain matrices from two systems linearly. HGDA updates the variational analysis with the analysis mean state of EnKF and uses this hybrid mean state to recenter the EnKF analysis ensemble. Based on this concept, a regional HGDA combining the WRF-VAR and WRF-LETKF systems is established.

In this study, we investigate whether the WRF-HGDA system has any advantage on assimilating the GPS-radio occultation reflectivity data and how such gain-hybrid property can help. Preliminary results with observation system simulation experiments suggest that WRF-HGDA has positive impact on the QV and wind fields compared with the WRF-VAR and WRF-LETKF systems.

*Preference: Oral