Journal of Meteorological Research

, Volume 31, Issue 6, pp 1123–1132 | Cite as

Evaluation of the impact of observations on blended sea surface winds in a two-dimensional variational scheme using degrees of freedom

  • Ting Wang
  • Jie Xiang
  • Jianfang Fei
  • Yi Wang
  • Chunxia Liu
  • Yuanxiang Li
Regular Articles


This paper presents an evaluation of the observational impacts on blended sea surface winds from a two-dimensional variational data assimilation (2D-Var) scheme. We begin by briefly introducing the analysis sensitivity with respect to observations in variational data assimilation systems and its relationship with the degrees of freedom for signal (DFS), and then the DFS concept is applied to the 2D-Var sea surface wind blending scheme. Two methods, a priori and a posteriori, are used to estimate the DFS of the zonal (u) and meridional (v) components of winds in the 2D-Var blending scheme. The a posteriori method can obtain almost the same results as the a priori method. Because only by-products of the blending scheme are used for the a posteriori method, the computation time is reduced significantly. The magnitude of the DFS is critically related to the observational and background error statistics. Changing the observational and background error variances can affect the DFS value. Because the observation error variances are assumed to be uniform, the observational influence at each observational location is related to the background error variance, and the observations located at the place where there are larger background error variances have larger influences. The average observational influence of u and v with respect to the analysis is about 40%, implying that the background influence with respect to the analysis is about 60%.


sea surface wind blending sensitivity observational influence degrees of freedom for signal 


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We thank the anonymous reviewers, whose comments were helpful and greatly appreciated.


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Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Ting Wang
    • 1
  • Jie Xiang
    • 1
    • 2
  • Jianfang Fei
    • 1
  • Yi Wang
    • 1
  • Chunxia Liu
    • 3
  • Yuanxiang Li
    • 4
  1. 1.Institute of Meteorology and OceanographyNational University of Defense TechnologyNanjingChina
  2. 2.Key Laboratory of Mesoscale Severe Weather of Ministry of EducationNanjing UniversityNanjingChina
  3. 3.Guangdong Institute of Tropical and Marine Meteorology of China Meteorological AdministrationGuangzhouChina
  4. 4.School of Aeronautics and AstronauticsShanghai Jiao Tong UniversityShanghaiChina

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