Journal of Meteorological Research

, Volume 33, Issue 3, pp 519–527 | Cite as

Assessing the Performance of Separate Bias Kalman Filter in Correcting the Model Bias for Estimation of Soil Moisture Profiles

  • Bangjun Cao
  • Fuping Mao
  • Shuwen ZhangEmail author
  • Shaoying Li
  • Tian Wang
Special Collection on Development and Applications of Regional and Global Land Data Assimilation Systems


The performance of separate bias Kalman filter (SepKF) in correcting the model bias for the improvement of soil moisture profiles is evaluated by assimilating the near-surface soil moisture observations into a land surface model (LSM). First, an observing system simulation experiment (OSSE) is carried out, where the true soil moisture is known, two types of model bias (i.e., constant and sinusoidal) are specified, and the bias error covariance matrix is assumed to be proportional to the model forecast error covariance matrix with a ratio λ. Second, a real assimilation experiment is carried out with measurements at a site over Northwest China. In the OSSE, the soil moisture estimation with the SepKF is improved compared with ensemble Kalman filter (EnKF) without the bias filter, because SepKF can properly correct the model bias, especially in the situation with a large model bias. However, the performance of SepKF becomes slightly worse if the constant model bias increases or temporal variability of the sinusoidal model bias becomes large. It is suggested that the ratio λ should be increased (decreased) in order to improve the soil moisture estimation if temporal variability of the sinusoidal model bias becomes high (low). Finally, the assimilation experiment with real observations also shows that SepKF can further improve the estimation of soil moisture profiles compared with EnKF without the bias correction.

Key words

soil moisture bias correction ensemble Kalman filter (EnKF) Noah-MP 


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Datasets used in this paper were kindly provided by the Semi-Arid Climate and Environment Observatory of Lanzhou University. See their website u] for details.


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Copyright information

© The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019

Authors and Affiliations

  • Bangjun Cao
    • 1
    • 2
  • Fuping Mao
    • 2
    • 3
  • Shuwen Zhang
    • 2
    Email author
  • Shaoying Li
    • 2
  • Tian Wang
    • 2
    • 4
  1. 1.School of Atmospheric SciencesChengdu University of Information TechnologyChengduChina
  2. 2.Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric SciencesLanzhou UniversityLanzhouChina
  3. 3.NanpingChina
  4. 4.Moji Co. Ltd.BeijingChina

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