Study of the Application of Real Observation Data and a Rescaling Factor in Ensemble Transform Kalman Filter Initial Perturbation Method
Received:December 03, 2018  
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KeyWord:Regional ensemble forecast  ETKF (Ensemble transform Kalman filter)  Real observation  Rescaling factor
Author NameAffiliationE-mail
ZHANG Hanbin Institute of Urban Meteorology, Beijing 100089  
CHEN Jing National Meteorological Center, China Meteorological Administration, Beijing 100081 
WANG Jiaoyang The 58th Unit of the 96164 Force of the Chinese People’s Liberation Army, Jinhua, Zhejiang 321021  
DONG Yan Beijing Meteorological Service Center, Beijing 100089  
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      At present, ETKF (ensemble transform Kalman filter) method used in the operational GRAPES (Global/Regional Assimilation Prediction Enhanced System) regional ensemble prediction system of National Meteorological Center uses pseudo observation information, and the number and distribution of observations are fixed. To improve the ETKF method, real observation data are introduced into the ETKF process. The real observational radio-sounding data enable the perturbation field to represent uncertainty information about the observational state. Considering that the number and distribution of real observational data change daily, this can cause instability in the perturbation amplitude for the ETKF calculation. Therefore, the authors introduce a new self-adjustment amplitude rescaling factor. In this study, the authors analyzed ETKF schemes based on pseudo observations, real observations, and real observation plus the new rescaling factor and compared them in terms of their perturbation characteristics, ensemble verifications, and precipitation forecast skills. The results show that real sounding data can be effectively applied to the GRAPES regional ensemble forecasting system. Compared with pseudo observation data, real observation data is sparse, so large initial perturbation amplitudes can be obtained. The use of real observation data can help to improve the spread of the regional ensemble, but the improvements in the ensemble prediction accuracy and probability forecast skill are limited, as is the improvement in the precipitation prediction. The authors designed a new perturbation-amplitude rescaling factor to adaptively adjust the initial perturbation amplitude based on the spread and root-mean-square-error relationship in the grid space. Our investigation of the adaptive rescaling factor for adjusting the perturbation amplitude shows that this new rescaling factor can effectively obtain a stable initial perturbation amplitude and maintain the ETKF-generated perturbation structure. Since the real-observation-based ETKF scheme exhibits over-spread characteristics and limited improvement with respect to the pseudo-observation-based ETKF, the use of real observation data combined with the adaptive rescaling factor can effectively improve the skill of the regional ensemble in terms of the probabilistic forecast results while also effectively improving the precipitation forecasting skill.