scholarly journals A variational ensemble Kalman filtering method for data assimilation using 2D and 3D version of COHERENS model

2016 ◽  
Vol 83 (6) ◽  
pp. 544-558 ◽  
Author(s):  
Idrissa Amour ◽  
Tuomo Kauranne
Keyword(s):  
Data Assimilation ◽  
Kalman Filtering ◽  
Filtering Method ◽  
2D And 3D ◽  
Ensemble Kalman Filtering

Extended versus Ensemble Kalman Filtering for Land Data Assimilation

2002 ◽  
Vol 3 (6) ◽  
pp. 728-740 ◽  
Author(s):  
Rolf H. Reichle ◽  
Jeffrey P. Walker ◽  
Randal D. Koster ◽  
Paul R. Houser
Keyword(s):  
Data Assimilation ◽  
Kalman Filtering ◽  
Land Data Assimilation ◽  
Ensemble Kalman Filtering

scholarly journals Unsupervised ensemble Kalman filtering with an uncertain constraint for land hydrological data assimilation

2018 ◽  
Vol 564 ◽  
pp. 175-190 ◽  
Author(s):  
M. Khaki ◽  
B. Ait-El-Fquih ◽  
I. Hoteit ◽  
E. Forootan ◽  
J. Awange ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Kalman Filtering ◽  
Hydrological Data ◽  
Ensemble Kalman Filtering

scholarly journals On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation

2019 ◽  
Vol 55 (9) ◽  
pp. 7622-7637 ◽  
Author(s):  
Ashkan Shokri ◽  
Jeffrey P. Walker ◽  
Albert I. J. M. Dijk ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Data Assimilation ◽  
Kalman Filtering ◽  
Grace Data ◽  
Ensemble Kalman Filtering

Reducing Quantitative Fluctuation of Laser-Induced Breakdown Spectroscopy by Kalman Filtering

2013 ◽  
Vol 333-335 ◽  
pp. 243-247 ◽  
Author(s):  
Lan Xiang Sun ◽  
Zhi Bo Cong ◽  
Yong Xin ◽  
Li Feng Qi ◽  
Yang Li
Keyword(s):  
Kalman Filtering ◽  
Elemental Concentration ◽  
Estimation Error ◽  
Laser Induced Breakdown Spectroscopy ◽  
System State ◽  
Large Signal ◽  
Filtering Method ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Quantitative Results

Laser-induced breakdown spectroscopy (LIBS) is excellent for its potential of online compositional analysis. Large signal fluctuation is the major obstacle of LIBS for quantitative analysis application. A kalman filtering method is proposed to estimate the elemental concentration and smooth the quantitative results. The system state model and the measurement model are deduced. The relation matrix between the measured values and system state is estimated based on calibration curve built on some standard samples, and the measurement noise matrix is estimated by the variance of multiple measurements of the spectral intensity. In order to make Kalman filter follow the changes of elemental concentration, the initial value of the covariance matrix of estimation error is reset as a certain rule. The experimental results show that the Kalman filtering method can greatly reduce the fluctuation of quantitative results and improve the measurement accuracy.

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