Evaluating Ensemble Kalman, Particle, and Ensemble Particle Filters through Soil Temperature Prediction
Publication: Journal of Hydrologic Engineering
Volume 19, Issue 12
Abstract
Data assimilation is a useful tool in hydrologic and agricultural application studies because of its ability to produce predicted results with high accuracy. However, different data-assimilation methods have different performances for a given application. Although the popular ensemble Kalman filter (EnKF) performs well with Gaussian distribution, the error is difficult to conform to the Gaussian distribution. To take advantage of the EnKF, this study presents a new data-assimilation method, ensemble particle filter (EnPF), which is an integration of the EnKF and the particle filter (PF). This new method was evaluated in comparison with two existing methods (EnKF and PF) through soil temperature predictions. The simple biosphere model (SiB2) and the filters were assessed with observations from the Wudaogou experimental area in the Huaihe River basin, China. Results show that when the time interval increases adequately, all the simulated or assimilated results improve significantly. All of these filters tend to be more stable when the number of particles reaches a certain amount (e.g., 60) or the variance is small (e.g., less than 0.6) in the study. When the number of particles is less than a threshold value (e.g., 30), the advantage among these three methods is not appreciable. The error obtained by EnPF is smaller than that by EnKF and PF; this means that EnPF performs better than EnKF and PF.
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Acknowledgments
This work is supported by the National Basic Research Program of China (2010CB951101), the National Natural Science Foundation of China (41101015 and 41101016), the National Technology Support Program in the 12th Five-year Plan of China (2012BAK10B04), the Fundamental Research Funds for the Central Universities, Program sponsored for scientific innovation research of college graduate in Jiangsu province in 2013 (CXZZ13_0251).
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Journal of Hydrologic Engineering
Volume 19 • Issue 12 • December 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Feb 10, 2012
Accepted: Feb 6, 2014
Published online: Feb 8, 2014
Published in print: Dec 1, 2014
Discussion open until: Dec 11, 2014
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