Sensitivity to the use of 3DVAR data assimilation in a mesoscale model for estimating offshore wind energy potential. A case study of the Iberian northern coastline

In this work, the WRF meteorological model is run in three different modes to estimate the wind energy potential in the Bay of Biscay for the 1990–2001 period. The first simulation (NODA) involves a typical use of the WRF model and it does not use data assimilation. The second one (12hDA) performs 3DVAR data assimilation at 00 UTC and 12 UTC. Finally, 6hDA uses 3DVAR data assimilation at 00 UTC, 06 UTC, 12 UTC and 18 UTC. Verification for the three simulations has been carried out at a preliminary stage using wind data from buoys, and then a spatially distributed analysis has been conducted of surface wind based on satellite data from the Cross-Calibrated Multi-Platform (CCMP). To that purpose, the spatial correlation and error patterns over our study area have been used as statistical indicators. The results indicate that the wind values obtained with data assimilation every six hours (6hDA) yield the best verification scores at a 95% confidence level, thereby being the most accurate at reproducing wind observations in the area. Regarding the estimation of wind energy potential, at a second stage, we tested the calculation’s sensitivity to the use of data assimilation. The most reliable simulation with data assimilation (6hDA) estimates 21% less energy potential than the simulation without data assimilation. In the absence of historical wind observation records of the sea with sufficient time and space resolution, meteorological models such as WRF provide an estimation of the wind values in tentative areas for offshore wind farms. In this line, our study highlights the need to use meteorological models with data assimilation, as future wind energy production can then be more realistically estimated beforehand. This may also contribute to a more accurate economic and technical evaluation of the risks and benefits for future investments in offshore wind energy.

[1]  Moncho Gómez-Gesteira,et al.  Comparison of reanalyzed, analyzed, satellite-retrieved and NWP modelled winds with buoy data along the Iberian Peninsula coast , 2014 .

[2]  Norberto Fueyo,et al.  High resolution modelling of the on‐shore technical wind energy potential in Spain , 2010 .

[3]  A. Yamaguchi,et al.  Assessment of offshore wind energy potential using mesoscale model and geographic information system , 2014 .

[4]  Norberto Fueyo,et al.  The use of cost-generation curves for the analysis of wind electricity costs in Spain , 2011 .

[5]  Deborah K. Smith,et al.  A Cross-calibrated, Multiplatform Ocean Surface Wind Velocity Product for Meteorological and Oceanographic Applications , 2011 .

[6]  Richard G. Jones,et al.  Simulation of climate change over europe using a nested regional‐climate model. I: Assessment of control climate, including sensitivity to location of lateral boundaries , 1995 .

[7]  Önder Güler,et al.  Evaluation of wind energy investment interest and electricity generation cost analysis for Turkey , 2010 .

[8]  Moncho Gómez-Gesteira,et al.  A sensitivity study of the WRF model in wind simulation for an area of high wind energy , 2012, Environ. Model. Softw..

[9]  Charlotte Bay Hasager,et al.  Satellite winds as a tool for offshore wind resource assessment: The Great Lakes Wind Atlas , 2015 .

[10]  Mark Z. Jacobson,et al.  California offshore wind energy potential , 2010 .

[11]  E. Kondili,et al.  Environmental and social footprint of offshore wind energy. Comparison with onshore counterpart , 2016 .

[12]  M.P. Bahrman,et al.  The ABCs of HVDC transmission technologies , 2007, IEEE Power and Energy Magazine.

[13]  M. Rummukainen.  State‐of‐the‐art with regional climate models , 2010 .

[14]  Jason Jonkman,et al.  Engineering Challenges for Floating Offshore Wind Turbines , 2007 .

[15]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[16]  G. Powers,et al.  A Description of the Advanced Research WRF Version 3 , 2008 .

[17]  R. Barthelmie,et al.  Quantifying offshore wind resources from satellite wind maps: study area the North Sea , 2004 .

[18]  A. Rocha,et al.  Wind resource modelling in complex terrain using different mesoscale–microscale coupling techniques , 2013 .

[19]  Yong-Run Guo,et al.  The Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA , 2012 .

[20]  Miguel Esteban,et al.  Current developments and future prospects of offshore wind and ocean energy , 2012 .

[21]  A. Sterl,et al.  The ERA‐40 re‐analysis , 2005 .

[22]  Eugenia Kalnay,et al.  Atmospheric Modeling, Data Assimilation and Predictability , 2002 .

[23]  K. Taylor.  Summarizing multiple aspects of model performance in a single diagram , 2001 .

[24]  R. Pielke,et al.  A comprehensive meteorological modeling system—RAMS , 1992 .

[25]  Lars Landberg,et al.  Wind Atlas Analysis and Application Program (WASP) Vol. 1: Getting Started , 1998 .

[26]  M. Gómez-Gesteira,et al.  Offshore wind energy resource simulation forced by different reanalyses: Comparison with observed data in the Iberian Peninsula , 2014 .

[27]  Moncho Gómez-Gesteira,et al.  Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula , 2014 .

[28]  S. Hsu,et al.  Estimating Overwater Friction Velocity and Exponent of Power-Law Wind Profile from Gust Factor during Storms , 2003 .

[29]  Stefano Vignudelli,et al.  Satellite radar altimetry from open ocean to coasts: challenges and perspectives , 2006, SPIE Asia-Pacific Remote Sensing.

[30]  M. Gómez-Gesteira,et al.  WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal , 2014 .

[31]  Gilbert M. Masters,et al.  Renewable and Efficient Electric Power Systems , 2004 .

[32]  G. Gaudiosi,et al.  Offshore wind energy in the world context , 1996 .