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A Four-Dimensional Ensemble-Variational (4DEnVar) Data Assimilation System for Global NWPs: System Description and Primary Tests
  • +9
  • 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
Shujun Zhu
Department of Earth System Science, Tsinghua University, Department of Earth System Science, Tsinghua University
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Bin Wang
LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, 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, 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, LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences
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Yongzhu Liu
Numerical Weather Prediction Center of China Meteorological Administration, Numerical Weather Prediction Center of China Meteorological Administration
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Jiandong Gong
China Meteorological Administration, China Meteorological Administration
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Shiming Xu
Department of Earth System Science, Tsinghua University, Department of Earth System Science, Tsinghua University
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Yong Wang
Department of Earth System Science, Tsinghua University, 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, 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, Tsinghua University
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Yujun He
Chinese Academy of Sciences, Chinese Academy of Sciences
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Xiangjun Wu
Research and Development Division, Numerical Weather Prediction Center of China Meteorological Administration, Research and Development Division, Numerical Weather Prediction Center of China Meteorological Administration
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Abstract

A four-dimensional ensemble-variational (4DEnVar) data assimilation (DA) system was developed for global numerical weather predictions (NWPs). Instead of using the adjoint technique, this system utilizes a dimension-reduced projection (DRP) technique to minimize the cost function of the standard four-dimensional variational (4DVar) DA. It dynamically predicts ensemble background error covariance (BEC) initialized from its previous inflated analyses and realizes the flow-dependence of BEC in the variational configuration during the assimilation cycle. These inflated analyses, linear combinations of the ensemble analyses increment and balanced random perturbations, aim to prevent the predicted BEC from underestimation as well as to implicitly achieve the hybrid of the flow-dependent and static BEC matrices. A limited number of leading eigenvectors of the localization correlation function are selected to filter out the spurious correlations in the BEC matrix (B-matrix). In order to evaluate the new system, single-point observation experiments (SOEs) and observing system simulation experiments (OSSEs) were conducted with sounding and cloud-derived wind data. The flow-dependent characteristic was verified in the SOEs that utilized the localized ensemble covariance and compared with that of 4DVar. In the OSSEs, 4DEnVar reduced the analysis errors compared with 4DVar. The deterministic forecast initialized from the 4DEnVar ensemble mean analysis has better (worse) performance in the medium-range (long-range) forecasts in the Northern Extratropics and opposite performance in the Southern Extratropics, and exhibits slightly worse effects in the Tropics. Moreover, the ensemble mean forecast initialized from the 4DEnVar ensemble analyses has higher forecast skills than 4DVar.