Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean

Abstract

In this article, offshore wind energy potential is measured around the West Mediterranean using the WRF meteorological model without 3DVAR data assimilation (the N simulation) and with 3DVAR data assimilation (the D simulation). Both simulations have been checked against the observations of six buoys and a spatially distributed analysis of wind based on satellite data (second version of Cross-Calibrated Multi-Platform, CCMPv2), and compared with ERA-Interim (ERAI). Three statistical indicators have been used: Pearson's correlation, root mean square error and the ratio of standard deviations. The simulation with data assimilation provides the best fit, and it is as good as ERAI, in many cases at a 95% confidence level. Although ERAI is the best model, in the spatially distributed evaluation versus CCMPv2 the D simulation has more consistent indicators than ERAI near the buoys. Additionally, our simulation's spatial resolution is five times higher than ERAI. Finally, regarding the estimation of wind energy potential, we have represented the annual and seasonal capacity factor maps over the study area, and our results have identified two areas of high potential to the north of Menorca and at Cabo Begur, where the wind energy potential has been estimated for three turbines at different heights according to the simulation with data assimilation.

Harmonized evaluation of daily precipitation downscaled using SDSM and WRF+WRFDA models over the Iberian Peninsula

“…5,6,7,8,9). This is consistent with Navascués et al (2013), Ulazia et al (2016Ulazia et al ( , 2017 and González-Rojí et al (2018). However, such simulations are 55% more computationally demanding to perform, so our present set of WRF experiments were limited to five years.…”

Moisture Recycling over the Iberian Peninsula: The Impact of 3DVAR Data Assimilation

“…Additionally, WRF precipitation generated with 3DVAR data assimilation showed a similar performance as the one obtained by means of statistical downscaling [75]. Furthermore, as wind in this simulation showed also an improvement over ERAI, WRF outputs were used to calculate the offshore wind energy potential around the Mediterranean [74] and at every coast of the IP [76]. Due to the good results obtained by the experiment including 3DVAR data assimilation, the simulated period has been extended until the end of 2018 for the analysis of recycling ratio with a longer perspective.…”

“…Finally, due to the good capacity of the simulation including data assimilation to correct to some extent the bias in soil moisture observed between both simulations (presented in this study), and due to its ability to reproduce the spatial and temporal evolution of variables involved in the water balance as presented in previous studies by the authors [73,74,76], this simulation was extended until 2018. This is why this simulation (WRF D) was chosen to show the interannual variability of areal mean moisture recycling over the IP for the period 2010-2018.…”

“…Both experiments presented here have already been validated against observational data in previous studies of the authors. The validation was carried out for variables such as precipitable water, precipitation, evaporation over the IP [73], and wind over the coastal areas [74], and this study presents the analysis of moisture recycling calculated using these thorough estimations of atmospheric moisture fields for the first time. In González-Rojí et al [73], it was found that the experiment including data assimilation produced more reliable results than those obtained with the numerical integration where only boundary conditions drive the model (WRF N) for variables related to the water balance (e.g., precipitation, evaporation and precipitable water) over the IP.…”

Wind Power Integration: An Experimental Investigation for Powering Local Communities

“…When the wind power density is calculated using the air pressure and temperature data of candidature site, the results are slightly different. Therefore, it is better to use air density data of the selected site instead of a standard value for air density [56,57]. Whereas in this research work, authors have utilized the 10 min average actual data of temperature and air pressure of selected site to evaluate the 10 min average air density.…”