scholarly journals State and bias estimation for soil moisture profiles by an ensemble Kalman filter: Effect of assimilation depth and frequency

2007 ◽  
Vol 43 (6) ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Paul R. Houser ◽  
Valentijn R. N. Pauwels ◽  
Niko E. C. Verhoest
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Bias Estimation ◽  
Filter Effect ◽  
Moisture Profiles

scholarly journals Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments

2011 ◽  
Vol 15 (8) ◽  
pp. 2437-2457 ◽  
Author(s):  
S. Nie ◽  
J. Zhu ◽  
Y. Luo
Keyword(s):  
Parameter Estimation ◽  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Land Surface ◽  
Estimation Method ◽  
State Parameter ◽  
Hydraulic Parameters ◽  
Constrained Estimation ◽  
Estimated Parameters

Abstract. The performance of the ensemble Kalman filter (EnKF) in soil moisture assimilation applications is investigated in the context of simultaneous state-parameter estimation in the presence of uncertainties from model parameters, soil moisture initial condition and atmospheric forcing. A physically based land surface model is used for this purpose. Using a series of identical twin experiments in two kinds of initial parameter distribution (IPD) scenarios, the narrow IPD (NIPD) scenario and the wide IPD (WIPD) scenario, model-generated near surface soil moisture observations are assimilated to estimate soil moisture state and three hydraulic parameters (the saturated hydraulic conductivity, the saturated soil moisture suction and a soil texture empirical parameter) in the model. The estimation of single imperfect parameter is successful with the ensemble mean value of all three estimated parameters converging to their true values respectively in both NIPD and WIPD scenarios. Increasing the number of imperfect parameters leads to a decline in the estimation performance. A wide initial distribution of estimated parameters can produce improved simultaneous multi-parameter estimation performances compared to that of the NIPD scenario. However, when the number of estimated parameters increased to three, not all parameters were estimated successfully for both NIPD and WIPD scenarios. By introducing constraints between estimated hydraulic parameters, the performance of the constrained three-parameter estimation was successful, even if temporally sparse observations were available for assimilation. The constrained estimation method can reduce RMSE much more in soil moisture forecasting compared to the non-constrained estimation method and traditional non-parameter-estimation assimilation method. The benefit of this method in estimating all imperfect parameters simultaneously can be fully demonstrated when the corresponding non-constrained estimation method displays a relatively poor parameter estimation performance. Because all these constraints between parameters were obtained in a statistical sense, this constrained state-parameter estimation scheme is likely suitable for other land surface models even with more imperfect parameters estimated in soil moisture assimilation applications.

Unscented weighted ensemble Kalman filter for soil moisture assimilation

2020 ◽  
Vol 580 ◽  
pp. 124352 ◽  
Author(s):  
Xiaolei Fu ◽  
Zhongbo Yu ◽  
Yongjian Ding ◽  
Yu Qin ◽  
Lifeng Luo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter

scholarly journals Dynamic Calibration with an Ensemble Kalman Filter Based Data Assimilation Approach for Root-Zone Moisture Predictions

2007 ◽  
Vol 8 (4) ◽  
pp. 910-921 ◽  
Author(s):  
Nicola Montaldo ◽  
John D. Albertson ◽  
Marco Mancini
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Root Zone ◽  
Sensitivity Analyses ◽  
Surface Model ◽  
Early Model ◽  
Model Errors ◽  
Model Bias ◽  
Near Surface

Abstract In the presence of uncertain initial conditions and soil hydraulic properties, land surface model (LSM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the near-surface soil moisture (θg), as observed from a remote platform. In this paper the possibility of merging observations and the model optimally for providing robust predictions of root-zone soil moisture (θ2) is demonstrated. An assimilation approach that assimilates θg through the ensemble Kalman filter (EnKF) and provides a physics-based update of θ2 is developed. This approach, as with other common soil moisture assimilation approaches, may fail when a key LSM parameter, for example, the saturated hydraulic conductivity (ks), is estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach is developed that accepts this violation in the early model run times and dynamically calibrates all the components of the ks ensemble as a function of the persistent bias in root-zone soil moisture, allowing one to remove the model bias, restore the fidelity to the EnKF requirements, and reduce the model uncertainty. The robustness of the proposed approach is also examined in sensitivity analyses.

scholarly journals Optimal ensemble size of ensemble Kalman filter in sequential soil moisture data assimilation

2015 ◽  
Vol 42 (16) ◽  
pp. 6710-6715 ◽  
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Youfei Zheng ◽  
Christopher R. Hain ◽  
Jicheng Liu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Ensemble Size ◽  
Soil Moisture Data ◽  
Optimal Ensemble

Application and improvement of an adaptive ensemble Kalman filter for soil moisture data assimilation

2010 ◽  
Vol 53 (11) ◽  
pp. 1700-1708 ◽  
Author(s):  
XiaoKang Shi ◽  
Jun Wen ◽  
JianWen Liu ◽  
Hui Tian ◽  
Xin Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Soil Moisture Data ◽  
Adaptive Ensemble Kalman Filter

Understanding the key factors that influence soil moisture estimation using the unscented weighted ensemble Kalman filter

2022 ◽  
Vol 313 ◽  
pp. 108745
Author(s):  
Xiaolei Fu ◽  
Xiaolei Jiang ◽  
Zhongbo Yu ◽  
Yongjian Ding ◽  
Haishen Lü ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Key Factors ◽  
Moisture Estimation

Assimilation of SMOS soil moisture in the MESH model with the ensemble Kalman filter

2014 ◽  
Author(s):  
Xiaoyong Xu ◽  
Jonathan Li ◽  
Bryan A. Tolson ◽  
Ralf M. Staebler ◽  
Frank Seglenieks ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Mesh Model

scholarly journals Data assimilation of soil water flow via ensemble Kalman filter: Infusing soil moisture data at different scales

2017 ◽  
Vol 555 ◽  
pp. 912-925 ◽  
Author(s):  
Penghui Zhu ◽  
Liangsheng Shi ◽  
Yan Zhu ◽  
Qiuru Zhang ◽  
Kai Huang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Soil Water ◽  
Water Flow ◽  
Ensemble Kalman Filter ◽  
Soil Moisture Data ◽  
Soil Water Flow
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