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Christos Karras, Aristeidis Karras, Konstantinos C. Giotopoulos, Markos Avlonitis and Spyros Sioutas
In the context of big-data analysis, the clustering technique holds significant importance for the effective categorization and organization of extensive datasets. However, pinpointing the ideal number of clusters and handling high-dimensional data can b...
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Marta Galvani, Chiara Bardelli, Silvia Figini and Pietro Muliere
Bootstrap resampling techniques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of a statistical functional ϕ(F)" role="presentation">??(??)?(F)
?
(
F
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, where F is a...
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Ang Su, Liang Zhang, Xuefeng Zhang, Shaoqing Zhang, Zhao Liu, Caili Liu and Anmin Zhang
Due to the model and sampling errors of the finite ensemble, the background ensemble spread becomes small and the error covariance is underestimated during filtering for data assimilation. Because of the constraint of computational resources, it is diffi...
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Christin Bobe, Daan Hanssens, Thomas Hermans and Ellen Van De Vijver
Often, multiple geophysical measurements are sensitive to the same subsurface parameters. In this case, joint inversions are mostly preferred over two (or more) separate inversions of the geophysical data sets due to the expected reduction of the non-uni...
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Elias D. Nino-Ruiz
In this paper, a matrix-free posterior ensemble Kalman filter implementation based on a modified Cholesky decomposition is proposed. The method works as follows: the precision matrix of the background error distribution is estimated based on a modified C...
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Mousong Wu, Per-Erik Jansson, Xiao Tan, Jingwei Wu, Jiesheng Huang
Pág. 1 - 17
Water and energy processes in frozen soils are important for better understanding hydrologic processes and water resources management in cold regions. To investigate the water and energy balance in seasonally frozen soils, CoupModel combined with the gen...
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Liang Xue
With the development of in-situ monitoring techniques, the ensemble Kalman filter (EnKF) has become a popular data assimilation method due to its capability to jointly update model parameters and state variables in a sequential way, and to assess the unc...
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