Super-ensemble techniques applied to wave forecast: performance and limitations

Nowadays, several operational ocean wave fore- casts are available for a same region. These predictions may considerably differ, and to choose the best one is gener- ally a difficult task. The super-ensemble approach, which consists in merging different forecasts and past observations into a single multi-model prediction system, is evaluated in this study. During the DART06 campaigns organized by the NATO Undersea Research Centre, four wave forecasting sys- tems were simultaneously run in the Adriatic Sea, and sig- nificant wave height was measured at six stations as well as along the tracks of two remote sensors. This effort pro- vided the necessary data set to compare the skills of vari- ous multi-model combination techniques. Our results indi- cate that a super-ensemble based on the Kalman Filter im- proves the forecast skills: The bias during both the hindcast and forecast periods is reduced, and the correlation coeffi- cient is similar to that of the best individual model. The spa- tial extrapolation of local results is not straightforward and requires further investigation to be properly implemented.

[1]  Alessandro Berni,et al.  Improved ocean prediction skill and reduced uncertainty in the coastal region from multi-model super-ensembles , 2009 .

[2]  Luigi Cavaleri,et al.  The calibration of wind and wave model data in the Mediterranean Sea , 2006 .

[3]  Bertrand Chapron,et al.  Waves and operational oceanography: Toward a coherent description of the upper ocean , 2005 .

[4]  B. Strong,et al.  The performance of ADCP-derived directional wave spectra and comparison with other independent measurements , 2000, OCEANS 2000 MTS/IEEE Conference and Exhibition. Conference Proceedings (Cat. No.00CH37158).

[5]  N. Booij,et al.  A third‐generation wave model for coastal regions: 2. Verification , 1999 .

[6]  Gregg A. Jacobs,et al.  A statistical comparison of wind speed, wave height, and wave period derived from satellite altimeters and ocean buoys in the Gulf of Mexico region , 1998 .

[7]  C. M. Kishtawal,et al.  Multimodel Ensemble Forecasts for Weather and Seasonal Climate , 2000 .

[8]  Michael Fiorino,et al.  Multimodel Superensemble Forecasting of Tropical Cyclones in the Pacific , 2003 .

[9]  Jacopo Chiggiato,et al.  Orographic effects on meteorological fields over the Adriatic from different models , 2009 .

[10]  Allan R. Robinson,et al.  Multi‐model fusion and error parameter estimation , 2005 .

[11]  C. M. Kishtawal,et al.  Improving Tropical Precipitation Forecasts from a Multianalysis Superensemble , 2000 .

[12]  James D. Dykes,et al.  An evaluation of a high-resolution operational wave forecasting system in the Adriatic Sea , 2009 .

[13]  E. Ferreira-Coelho,et al.  Operational prediction of acoustic properties in the ocean using multi-model statistics , 2006 .

[14]  C. Fratianni,et al.  Super-Ensemble Techniques: Application to Surface Drift Prediction During the DART06 and MREA07 Campaigns , 2009 .

[15]  Luigi Cavaleri,et al.  Wave Modelling - The State of the Art , 2007 .

[16]  Luigi Cavaleri,et al.  In Search of the Correct Wind and Wave Fields in a Minor Basin , 1997 .

[17]  J. Steppeler,et al.  Meso-gamma scale forecasts using the nonhydrostatic model LM , 2003 .

[18]  Luigi Cavaleri,et al.  Assessment of Wind Quality for Oceanographic Modelling in Semi-Enclosed Basins , 2005 .

[19]  R. H. Stewart,et al.  Ocean Wave Measurement Techniques , 1980 .

[20]  Zvjezdana Bencetić Klaić,et al.  Orographic influences on the Adriatic sirocco wind , 2007 .

[21]  Michel Rixen,et al.  Operational surface drift prediction using linear and non-linear hyper-ensemble statistics on atmospheric and ocean models , 2007 .

[22]  N. Booij,et al.  A third-generation wave model for coastal regions-1 , 1999 .

[23]  D. W. Shin.  Short- to medium-range superensemble precipitation forecasts using satellite products , 2001 .