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A 4DEnVar-based Ensemble Four-Dimensional Variational (En4DVar) Hybrid Data Assimilation System for Global NWPs: System Description and Primary Tests
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  • Shujun Zhu,
  • Bin Wang,
  • Lin Zhang,
  • J. J. Liu,
  • Yongzhu Liu,
  • Jiandong Gong,
  • Shiming Xu,
  • Yong Wang,
  • Wenyu Huang,
  • Li Liu,
  • Yujun He,
  • Xiangjun Wu,
  • Bin Zhao,
  • Fajing Chen
Shujun Zhu
Department of Earth System Science, Tsinghua University
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Bin Wang
LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences

Corresponding Author:[email protected]

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Lin Zhang
Research and Development Division, Numerical Weather Prediction Center of China Meteorological Administration
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J. J. Liu
LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences
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Yongzhu Liu
Numerical Weather Prediction Center of China Meteorological Administration
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Jiandong Gong
China Meteorological Administration
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Shiming Xu
Department of Earth System Science, Tsinghua University
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Yong Wang
Department of Earth System Science, Tsinghua University
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Wenyu Huang
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
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Li Liu
Tsinghua University
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Yujun He
Chinese Academy of Sciences
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Xiangjun Wu
Research and Development Division, Numerical Weather Prediction Center of China Meteorological Administration
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Bin Zhao
CMA Earth System Modeling and Prediction Centre, China Meteorological Administration
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Fajing Chen
Numerical Weather Prediction Center of China Meteorological Administration
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Abstract

This study developed an ensemble four-dimensional variational (En4DVar) hybrid data assimilation (DA) system. Different from most of the available En4DVar systems that adopted ensemble Kalman Filter class or ensemble DA approaches to produce ensemble covariances for their hybrid background error covariances (BECs), it used a four-dimensional ensemble-variational (4DEnVar) system to obtain the ensemble covariance. The localization scheme for 4DEnVar applied orthogonal functions to decompose the correlation matrix so that it was implemented easily and rapidly. In terms of analysis quality and forecast skill, the En4DVar system was evaluated in the single-point observation experiments and observing system simulation experiments (OSSEs) with sounding and cloud-derived wind observations, using its standalone four-dimensional variational (4DVar) and 4DEnVar components as references. The single-point observation experiments visually verified the explicit flow-dependent characteristic of the BEC due to the introduction of the ensemble covariance from the 4DEnVar system. The OSSE-based sensitivity experiments revealed different contributions of the weight for the ensemble covariance in the En4DVar system to the forecasts in the Northern and Southern Extratropics and Tropics. A much higher weight for the ensemble covariance in a properly inflated hybrid covariance helped En4DVar produce the most reasonable analysis. The forecast initialized by En4DVar is overall better than by 4DVar and 4DEnVar, although the quality of En4DVar analysis is between those of 4DVar and 4DEnVar ensemble mean analyses. It indicates that the flow-dependent ensemble covariance provided by 4DEnVar dominantly contributes to the improvements in the En4DVar-initialized forecast, with certain but necessary constraint from the balanced climatological covariance.