Soil Moisture Assimilation Using a Modified Ensemble Transform Kalman Filter Based on Station Observations in the Hai River Basin
Joint Authors
Wu, Guocan
Dan, Bo
Zheng, Xiaogu
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-18
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Assimilating observations to a land surface model can further improve soil moisture estimation accuracy.
However, assimilation results largely rely on forecast error and generally cannot maintain a water budget balance.
In this study, shallow soil moisture observations are assimilated into Common Land Model (CoLM) to estimate the soil moisture in different layers.
A proposed forecast error inflation and water balance constraint are adopted in the Ensemble Transform Kalman Filter to reduce the analysis error and water budget residuals.
The assimilation results indicate that the analysis error is reduced and the water imbalance is mitigated with this approach.
American Psychological Association (APA)
Wu, Guocan& Dan, Bo& Zheng, Xiaogu. 2016. Soil Moisture Assimilation Using a Modified Ensemble Transform Kalman Filter Based on Station Observations in the Hai River Basin. Advances in Meteorology،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1095480
Modern Language Association (MLA)
Wu, Guocan…[et al.]. Soil Moisture Assimilation Using a Modified Ensemble Transform Kalman Filter Based on Station Observations in the Hai River Basin. Advances in Meteorology No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1095480
American Medical Association (AMA)
Wu, Guocan& Dan, Bo& Zheng, Xiaogu. Soil Moisture Assimilation Using a Modified Ensemble Transform Kalman Filter Based on Station Observations in the Hai River Basin. Advances in Meteorology. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1095480
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1095480