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We conclude with some speculations about the potential for data assimilation to further integrate these approaches to understanding dynamo action.
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The posterior distribution of the estimation step is then considered as prior information for data assimilation.
Red triangles and plus symbols indicate selected and unselected GEONET stations for data assimilation, respectively.
For the GNSS stations shown in Fig. 1b, both PWVCON and PWVSPD-H were used for data assimilation, while, for other GEONET stations not included in Fig. 1b, only PWVCON was used for data assimilation.
To overcome such limitations, Evensen (1994) suggested an algorithm for data assimilation employing Monte Carlo methods as an alternative.
For MicroLab1, the sounding locations on the Earth's surface were globally pseudo-randomly distributed, which is good for data assimilation in atmospheric and ionospheric research.
In this section, we briefly describe the rationale and algorithmic implementation of the Local Ensemble Transform Kalman Filter LETKFforfor data assimilation.
These results provide interesting perspectives for data-assimilation in flood-forecasting.
The parsimonious surrogates can then be employed for online data assimilation and for Bayesian estimation.
However, such a result does not demonstrate the potential utility of a data assimilation system in the context of cancer, where model error is likely to be substantial.
We have chosen the logistic Swanson and two-phenotype models because they are adequate to establish the potential feasibility of a data assimilation (state estimation) scheme in the face of significant errors in model parameters and data acquisition.
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