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J. Bidlot, D. Holmes, P. Wittmann, R. Lalbeharry, Hsuan Chen (2002)
Intercomparison of the Performance of Operational Ocean Wave Forecasting Systems with Buoy DataWeather and Forecasting, 17
S. Caires, A. Sterl, J. Bidlot, N. Graham, V. Swail (2004)
Intercomparison of Different Wind–Wave ReanalysesJournal of Climate, 17
Bidlot (2006)
Verification of operational global and regional wave forecasting systems against measurements from moored buoys.
Y. Faugère, J. Dorandeu, F. Lefèvre, N. Picot, P. Féménias (2006)
Envisat Ocean Altimetry Performance Assessment and Cross-calibrationSensors (Basel, Switzerland), 6
F. Woodcock, D. Greenslade (2007)
Consensus of Numerical Model Forecasts of Significant Wave HeightsWeather and Forecasting, 22
T. Durrant, D. Greenslade, I. Simmonds (2009)
Validation of Jason-1 and Envisat Remotely Sensed Wave HeightsJournal of Atmospheric and Oceanic Technology, 26
P. Queffeulou (2004)
Long-Term Validation of Wave Height Measurements from AltimetersMarine Geodesy, 27
R. Clemen, R. Winkler (1985)
Limits for the Precision and Value of Information from Dependent SourcesOper. Res., 33
F. Woodcock, Chermelle Engel (2005)
Operational Consensus ForecastsWeather and Forecasting, 20
(2000)
Potential benefit of ensemble forecasts for ship routing
Tolman (2002)
Validation of WAVEWATCH III version 1.15 for a global domain.
B. Efron, R. Tibshirani (1991)
Statistical Data Analysis in the Computer AgeScience, 253
P. Janssen, B. Hansen, J. Bidlot (1997)
Verification of the ECMWF Wave Forecasting System against Buoy and Altimeter DataWeather and Forecasting, 12
M. Hibon, T. Evgeniou (2005)
To combine or not to combine: selecting among forecasts and their combinationsInternational Journal of Forecasting, 21
R. Wonnacott, T. Wonnacott (1972)
Introductory statistics for business and economics
William Cheng, W. Steenburgh (2007)
Strengths and Weaknesses of MOS, Running-Mean Bias Removal, and Kalman Filter Techniques for Improving Model Forecasts over the Western United StatesWeather and Forecasting, 22
(1988)
The WAM model — A third - generation ocean wave prediction model
H. Glahn, D. Lowry (1972)
The Use of Model Output Statistics (MOS) in Objective Weather ForecastingJournal of Applied Meteorology, 11
L. Farina (2002)
On ensemble prediction of ocean wavesTellus A: Dynamic Meteorology and Oceanography, 54
Komen (1994)
Dynamics and Modelling of Ocean Waves.
H. Tolman (1991)
A Third-Generation Model for Wind Waves on Slowly Varying, Unsteady, and Inhomogeneous Depths and CurrentsJournal of Physical Oceanography, 21
N. Booij, R. Ris, L. Holthuijsen (1999)
A third-generation wave model for coastal regions-1
S. Caires, A. Sterl (2003)
Validation of ocean wind and wave data using triple collocationJournal of Geophysical Research, 108
N. Booij, R. Ris, L. Holthuijsen
A third-generation wave model for coastal regions Model description and validation
(1977)
The consensus of subjective probability forecasts: Are two, three
Chen (2006)
Ensemble prediction of ocean waves at NCEP.
Chermelle Engel, E. Ebert (2007)
Performance of Hourly Operational Consensus Forecasts (OCFs) in the Australian RegionWeather and Forecasting, 22
Janssen (2000)
Potential benefits of ensemble prediction of waves.
Winkler (1977)
The consensus of subjective probability forecasts: Are two, three,…, heads better than one?
Bidlot (2007)
Inter-comparison of operational wave forecasting systems.
The use of numerical guidance has become integral to the process of modern weather forecasting. Using various techniques, postprocessing of numerical model output has been shown to mitigate some of the deficiencies of these models, producing more accurate forecasts. The operational consensus forecast scheme uses past performance to bias-correct and combine numerical forecasts to produce an improved forecast at locations where recent observations are available. This technique was applied to forecasts of significant wave height ( H s ), peak period ( T p ), and 10-m wind speed ( U 10 ) from 10 numerical wave models, at 14 buoy sites located around North America. Results show the best forecast is achieved with a weighted average of bias-corrected components for both H s and T p , while a weighted average of linear-corrected components gives the best results for U 10 . For 24-h forecasts, improvements of 36%, 47%, and 31%, in root-mean-square-error values over the mean raw model components are achieved, or 14%, 22%, and 18% over the best individual model. Similar gains in forecast skill are retained out to 5 days. By reducing the number of models used in the construction of consensus forecasts, it is found that little forecast skill is gained beyond five or six model components, with the independence of these components, as well as individual component’s quality, being important considerations. It is noted that for H s it is possible to beat the best individual model with a composite forecast of the worst four.
Weather and Forecasting – American Meteorological Society
Published: Mar 25, 2008
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