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In the nested NHM-LETKF, data assimilation is conducted on the assumption that observation data are uncorrelated.
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Data assimilation is becoming an increasingly important tool for understanding the near Earth hazardous radiation environments.
An important issue in data assimilation is the estimation of error covariance matrices.
The data assimilation is demonstrated for the period of March April 1997.
The adjoint-based data assimilation is outlined in this section.
Data assimilation is a versatile methodology for estimating unknown state variables and parameters.
In order to improve on the numerical outcome data assimilation is common.
Data assimilation is an application of weighted least squares, as we now describe.
Applications of the stochastic models, namely data assimilation, are presented.
In this study, a comparative analysis is conducted to assess the impact of discharge data assimilation on the quality of streamflow forecasts issued by two different modeling conceptualizations of catchment response.
Data analysis was conducted anonymously.
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