Skill assessment of an operational hydrodynamic forecast system for the North Sea and Danish Belts

Vested, Hans Jacob; Nielsen, Jacob Woge; Jensen, H. René; Kristensen, Karsten Holding

doi:10.1029/ce047p0373

Cited by 23 publications

(13 citation statements)

References 6 publications

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“…The state-of-art in these forecast systems is based on two-dimensional shallow water equation models (Vested et al, 1995;Gerritsen et al, 1995;Bode and Hardy, 1997). In shallow seas, these types of models provide a very good simulation of the water levels and they have very good forecasting capabilities.…”

Section: Storm Surge and Flood Forecasting Modelmentioning

confidence: 99%

Cyclone induced storm surge and flood forecasting in the northern Bay of Bengal

Madsen

Jakobsen

2004

Coastal Engineering

116

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Section: Storm Surge and Flood Forecasting Modelmentioning

confidence: 99%

Cyclone induced storm surge and flood forecasting in the northern Bay of Bengal

Madsen

Jakobsen

2004

Coastal Engineering

116

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“…This model applied two-dimensional shallow water equation models based on the three-dimensional Navier-Stokes equation [1,30] as the hydrodynamic models. The factors affecting the fluid field, such as the tide, wind, Coriolis force, the bottom friction, fluid shear stress and topographical boundary conditions will be considered in this model.…”

Section: Numerical Storm Surge Modelmentioning

confidence: 99%

Predictions of typhoon storm surge in Taiwan using artificial neural networks

Lee

2009

Advances in Engineering Software

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“…The state-of-the-art in these forecast systems is based on two-dimensional shallow water equation models (Vested et al, 1995;Gerritsen et al, 1995;Bode and Hardy, 1997). A cyclone-induced storm surge and flood forecasting system has been developed for the northern Bay of Bengal (Madsen and Jakobsn, 2004).…”

Section: Introductionmentioning

confidence: 99%

The application of a Grey Markov Model to forecasting annual maximum water levels at hydrological stations

Dong

Chi

Zhang

et al. 2012

J. Ocean Univ. China

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Compared with traditional real-time forecasting, this paper proposes a Grey Markov Model (GMM) to forecast the maximum water levels at hydrological stations in the estuary area. The GMM combines the Grey System and Markov theory into a higher precision model. The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values, and thus gives forecast results involving two aspects of information. The procedure for forecasting annul maximum water levels with the GMM contains five main steps: 1) establish the GM (1, 1) model based on the data series; 2) estimate the trend values; 3) establish a Markov Model based on relative error series; 4) modify the relative errors caused in step 2, and then obtain the relative errors of the second order estimation; 5) compare the results with measured data and estimate the accuracy. The historical water level records (from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin, China are utilized to calibrate and verify the proposed model according to the above steps. Every 25 years' data are regarded as a hydro-sequence. Eight groups of simulated results show reasonable agreement between the predicted values and the measured data. The GMM is also applied to the 10 other hydrological stations in the same estuary. The forecast results for all of the hydrological stations are good or acceptable. The feasibility and effectiveness of this new forecasting model have been proved in this paper.

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Skill assessment of an operational hydrodynamic forecast system for the North Sea and Danish Belts

Cited by 23 publications

References 6 publications

Cyclone induced storm surge and flood forecasting in the northern Bay of Bengal

Cyclone induced storm surge and flood forecasting in the northern Bay of Bengal

Predictions of typhoon storm surge in Taiwan using artificial neural networks

The application of a Grey Markov Model to forecasting annual maximum water levels at hydrological stations

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