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

Alain Ulazia, Jon Saenz and Gabriel Ibarra-Berastegui

Applied Energy, 2016, vol. 180, issue C, 617-627

Abstract: 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.

Keywords: Offshore wind energy potential; WRF; WRFDA; Data assimilation; Mesoscale model; Fluid mechanics (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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DOI: 10.1016/j.apenergy.2016.08.033

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