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Newtonian nudging

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The horizontal (d), vertical (z − z0), and time (t − t0) components of the Gaussian-exponential nudging four-dimensional weighting functions [W(x,y,z,t)] used in the Newtonian nudging runs.
Published: 01 November 2009
F ig . 1. The horizontal ( d ), vertical ( z − z 0 ), and time ( t − t 0 ) components of the Gaussian-exponential nudging four-dimensional weighting functions [ W ( x , y , z , t )] used in the Newtonian nudging runs.
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Net atmospheric boundary conditions for the base run (black) and the open loop and data assimilation runs (gray). The plotted series represents the actual forcing imposed on the Newtonian nudging and open loop scenarios and the nominal mean for the ensemble Kalman filter scenarios. Positive values correspond to rainfall rates and negative values to evaporation rates.
Published: 01 November 2009
F ig . 3. Net atmospheric boundary conditions for the base run (black) and the open loop and data assimilation runs (gray). The plotted series represents the actual forcing imposed on the Newtonian nudging and open loop scenarios and the nominal mean for the ensemble Kalman filter scenarios
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Difference in water table depth between the open loop run and the base run and between the base run and the Newtonian nudging (NN) and ensemble Kalman filter (EnKF) data assimilation runs for surface soil moisture (θ), pressure head at the bottom layer of the catchment (ψ), and streamflow at the catchment outlet (Q) at the end of the simulation (t = 3600 h).
Published: 01 November 2009
F ig . 5. Difference in water table depth between the open loop run and the base run and between the base run and the Newtonian nudging (NN) and ensemble Kalman filter (EnKF) data assimilation runs for surface soil moisture (θ), pressure head at the bottom layer of the catchment (ψ
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Time evolution of the RMSE computed on the system state in terms of pressure head across the entire three-dimensional subsurface grid using Eq. [9] for the Newtonian nudging (NN) and ensemble Kalman filter (EnKF) data assimilation runs for surface soil moisture (θ), pressure head at the bottom layer of the catchment (ψ), and streamflow at the catchment outlet (Q).
Published: 01 November 2009
F ig . 8. Time evolution of the RMSE computed on the system state in terms of pressure head across the entire three-dimensional subsurface grid using Eq. [9] for the Newtonian nudging (NN) and ensemble Kalman filter (EnKF) data assimilation runs for surface soil moisture (θ), pressure head
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Time evolution of the water volume stored in the catchment subsurface for the base run, the open loop run, and the Newtonian nudging (NN) and ensemble Kalman filter data assimilation runs for surface soil moisture (θ), pressure head at the bottom layer of the catchment (ψ), and streamflow at the catchment outlet (Q). The storage includes both the saturated zone and the vadose zone.
Published: 01 November 2009
F ig . 6. Time evolution of the water volume stored in the catchment subsurface for the base run, the open loop run, and the Newtonian nudging (NN) and ensemble Kalman filter data assimilation runs for surface soil moisture (θ), pressure head at the bottom layer of the catchment (ψ
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Difference in water saturation at the surface nodes between the open loop run and the base run and between the base run and the Newtonian nudging (NN) and ensemble Kalman filter (EnKF) data assimilation runs for surface soil moisture (θ), pressure head at the bottom layer of the catchment (ψ), and streamflow at the catchment outlet (Q) at the end of the simulation (t = 3600 h).
Published: 01 November 2009
F ig . 4. Difference in water saturation at the surface nodes between the open loop run and the base run and between the base run and the Newtonian nudging (NN) and ensemble Kalman filter (EnKF) data assimilation runs for surface soil moisture (θ), pressure head at the bottom layer
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Streamflow hydrograph (bottom) and cumulated streamflow volume (top) at the catchment outlet for the base run, the open loop run, and Newtonian nudging (NN) and ensemble Kalman filter (EnKF) data assimilation runs for surface soil moisture (θ), pressure head at the bottom layer of the catchment (ψ), and streamflow at the catchment outlet (Q). The red symbols denote the streamflow observations used for scenario EnKF-Q.
Published: 01 November 2009
F ig . 7. Streamflow hydrograph (bottom) and cumulated streamflow volume (top) at the catchment outlet for the base run, the open loop run, and Newtonian nudging (NN) and ensemble Kalman filter (EnKF) data assimilation runs for surface soil moisture (θ), pressure head at the bottom layer
Journal Article
Published: 01 November 2009
Vadose Zone Journal (2009) 8 (4): 837–845.
...F ig . 1. The horizontal ( d ), vertical ( z − z 0 ), and time ( t − t 0 ) components of the Gaussian-exponential nudging four-dimensional weighting functions [ W ( x , y , z , t )] used in the Newtonian nudging runs. ...
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Journal Article
Published: 01 November 2009
Vadose Zone Journal (2009) 8 (4): 823–824.
... and minor loss of accuracy. To better couple model and observations Camporese et al. (2009) embed a fully integrated hydrologic model (CATHY) into a data-assimilation framework. They compare an ensemble Kalman filter (EnKF) to a Newtonian nudging (NN) technique for a small catchment in Belgium...
Journal Article
Published: 01 January 2001
Reviews in Mineralogy and Geochemistry (2001) 44 (1): 191–216.
... state is located using the nudged elastic band method Henkelman et al. (2000) that solves the problem of locating a transition state between any two local minima. Using these methods, processes on the order of hours can be investigated with molecular modeling approaches. As yet, these methods have...
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