Sentence examples for data assimilation process from inspiring English sources

Open image in new window Fig. 3 Data assimilation process for the network model (adapted from Barros et al. 2015).

In the data assimilation process, these parameters are updated via EnKF scheme as the model parameters of the state vector.

The level set functions on the representing nodes are the model parameters of the EnKF state vector and are updated in the data assimilation process.

Newtonian relaxation method was used for this data assimilation process, where ionosonde data from four different locations in South America was used to improve the system accuracy.

Through the reparameterization, the level set function values generated for the representing nodes are the model parameters of the Ensemble Kalman filter (EnKF) and then be updated in the data assimilation process.

IRI is therefore a suitable model for specification of monthly averages of Total Electron Content and can be used to initialise a data assimilation process at times away from solar maximum.

Two effective data assimilation processes are developed to improve the wave-field estimates obtained from the study of one or several probe signals, so that we obtain proper initial conditions for the forecast.

Therefore, in the present study, we developed a method to accurately predict various CFRP thermoset molding characteristics based on data assimilation, a process that combines theoretical and experimental values.

(3) From data assimilation, the process by which models are updated with the latest observational data to be able to correct and validate the assumptions made in the model due to different factors like missing parameters, incorrect data, etc. Analysis of this Big Data can give unprecedented possibilities for better decision making for understanding and mitigating the effects of climate changes.

Data assimilation is the process by which observations of the actual system are incorporated into the model state of a numerical model of that system.

This paper aims to investigate how surface soil moisture data assimilation affects each hydrologic process and how spatially varying inputs affect the potential capability of surface soil moisture assimilation at the watershed scale.