Searched for: subject:"filters"
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document
Fu, Guangliang (author), Prata, Fred (author), Lin, H.X. (author), Heemink, A.W. (author), Segers, AJ (author), Lu, S. (author)
Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and...
journal article 2017
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Van Delft, G. (author), El Serafy, G.Y. (author), Heemink, A.W. (author)
Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the particle filter (PF) both have their own...
journal article 2009
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Heemink, A.W. (author)
In this study the theory of Kalman filtering has been employed to develop a new method for predicting water-levels along the Dutch coast. The combination of the standard Kalman filter with a non-linear tidal model of the entire North Sea is, from a computational point of view, not (yet) feasible. Therefore, in this investigation two different...
report 1986
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Ten Brummelhuis, P.G.J. (author), De Jong, B. (author), Heemink, A.W. (author)
This report gives an exposition of the applications Kalman filters and the related nonlinear filters offer when used in models describing tidal motion in seas and estuaries. In addition to methods that are generally used in this field, for example deterministic and black-box models, Kalman filters can provide significant contributions in...
report 1985
Searched for: subject:"filters"
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