Articles | Volume 13, issue 10
https://doi.org/10.5194/gmd-13-5053-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-5053-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| Highlight paper
| 
27 Oct 2020
Methods for assessment of models | Highlight paper |
| 27 Oct 2020
| 27 Oct 2020
The making of the New European Wind Atlas – Part 1: Model sensitivity
Wind Energy Department, Technical University of Denmark, Roskilde, Denmark
Tija Sīle
Institute of Numerical Modelling, Department of Physics, University of Latvia, Riga, Latvia
Björn Witha
ForWind, Carl von Ossietzky University Oldenburg, Oldenburg, Germany
energy & meteo systems GmbH, Oldenburg, Germany
Neil N. Davis
Wind Energy Department, Technical University of Denmark, Roskilde, Denmark
Martin Dörenkämper
Fraunhofer Institute for Wind Energy Systems, Oldenburg, Germany
Yasemin Ezber
Eurasia Institute of Earth Sciences, Istanbul Technical University, Istanbul, Turkey
Elena García-Bustamante
Wind Energy Unit, CIEMAT, Madrid, Spain
J. Fidel González-Rouco
Department of Earth Physics and Astrophysics, University Complutense of Madrid, Madrid, Spain
Institute of Geosciences, IGEO (UCM-CSIC), Madrid, Spain
Jorge Navarro
Wind Energy Unit, CIEMAT, Madrid, Spain
Bjarke T. Olsen
Wind Energy Department, Technical University of Denmark, Roskilde, Denmark
Stefan Söderberg
WeatherTech, Uppsala, Sweden
Renewable Energy Analytics, DNV-GL Energy, Solna, Sweden
Related authors
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025, https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
Short summary
Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci., 9, 963–979, https://doi.org/10.5194/wes-9-963-2024, https://doi.org/10.5194/wes-9-963-2024, 2024
Short summary
Short summary
This study compares the results of two wind farm parameterizations (WFPs) in the Weather Research and Forecasting model, simulating a two-turbine array under three atmospheric stabilities with large-eddy simulations. We show that the WFPs accurately depict wind speeds either near turbines or in the far-wake areas, but not both. The parameterizations’ performance varies by variable (wind speed or turbulent kinetic energy) and atmospheric stability, with reduced accuracy in stable conditions.
Xiaoli Guo Larsén, Marc Imberger, Ásta Hannesdóttir, and Andrea N. Hahmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-102, https://doi.org/10.5194/wes-2022-102, 2023
Revised manuscript not accepted
Short summary
Short summary
We study how climate change will impact extreme winds and choice of turbine class. We use data from 18 CMIP6 members from a historic and a future period to access the change in the extreme winds. The analysis shows an overall increase in the extreme winds in the North Sea and the southern Baltic Sea, but a decrease over the Scandinavian Peninsula and most of the Baltic Sea. The analysis is inconclusive to whether higher or lower classes of turbines will be installed in the future.
Andrea N. Hahmann, Oscar García-Santiago, and Alfredo Peña
Wind Energ. Sci., 7, 2373–2391, https://doi.org/10.5194/wes-7-2373-2022, https://doi.org/10.5194/wes-7-2373-2022, 2022
Short summary
Short summary
We explore the changes in wind energy resources in northern Europe using output from simulations from the Climate Model Intercomparison Project (CMIP6) under the high-emission scenario. Our results show that climate change does not particularly alter annual energy production in the North Sea but could affect the seasonal distribution of these resources, significantly reducing energy production during the summer from 2031 to 2050.
Graziela Luzia, Andrea N. Hahmann, and Matti Juhani Koivisto
Wind Energ. Sci., 7, 2255–2270, https://doi.org/10.5194/wes-7-2255-2022, https://doi.org/10.5194/wes-7-2255-2022, 2022
Short summary
Short summary
This paper presents a comprehensive validation of time series produced by a mesoscale numerical weather model, a global reanalysis, and a wind atlas against observations by using a set of metrics that we present as requirements for wind energy integration studies. We perform a sensitivity analysis on the numerical weather model in multiple configurations, such as related to model grid spacing and nesting arrangements, to define the model setup that outperforms in various time series aspects.
Martin Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann
Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, https://doi.org/10.5194/gmd-13-5079-2020, 2020
Short summary
Short summary
This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
Johanna Borowski, Sandra Schwegmann, Kerstin Avila, and Martin Dörenkämper
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-117, https://doi.org/10.5194/wes-2025-117, 2025
Preprint under review for WES
Short summary
Short summary
Assessing the wind resource and mitigating its associated uncertainties are crucial to wind farm profitability. The study quantifies the uncertainty due to inter-annual variability, averaging 6.5 % and ranging from 1 % to 14 %, using long-term, quality-controlled wind measurements from tall met masts in terrain of varying complexity. Further, the results indicate that machine learning models are beneficial to mitigate the impact of inter-annual variability in heterogeneous and complex terrain.
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025, https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
Short summary
Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Félix García-Pereira, Jesús Fidel González-Rouco, Nagore Meabe-Yanguas, Philipp de Vrese, Norman Julius Steinert, Johann Jungclaus, and Stephan Lorenz
EGUsphere, https://doi.org/10.5194/egusphere-2025-2126, https://doi.org/10.5194/egusphere-2025-2126, 2025
Short summary
Short summary
This work shows that changing the hydrological state of permafrost produces differences of up to 3 °C in the annual ground temperature, 1–2 m in the active layer thickness, and 5 million km2 in the permafrost extent. Including a deeper vertical thermal scheme reduces the extent decline by more than 2 million km2 in the highest radiative emission scenario. This is shown for the first time in fully-coupled experiments with an Earth System Model.
Lukas Vollmer, Balthazar Arnoldus Maria Sengers, and Martin Dörenkämper
Wind Energ. Sci., 9, 1689–1693, https://doi.org/10.5194/wes-9-1689-2024, https://doi.org/10.5194/wes-9-1689-2024, 2024
Short summary
Short summary
This study proposes a modification to a well-established wind farm parameterization used in mesoscale models. The wind speed at the location of the turbine, which is used to calculate power and thrust, is corrected to approximate the free wind speed. Results show that the modified parameterization produces more accurate estimates of the turbine’s power curve.
Félix García-Pereira, Jesús Fidel González-Rouco, Camilo Melo-Aguilar, Norman Julius Steinert, Elena García-Bustamante, Philip de Vrese, Johann Jungclaus, Stephan Lorenz, Stefan Hagemann, Francisco José Cuesta-Valero, Almudena García-García, and Hugo Beltrami
Earth Syst. Dynam., 15, 547–564, https://doi.org/10.5194/esd-15-547-2024, https://doi.org/10.5194/esd-15-547-2024, 2024
Short summary
Short summary
According to climate model estimates, the land stored 2 % of the system's heat excess in the last decades, while observational studies show it was around 6 %. This difference stems from these models using land components that are too shallow to constrain land heat uptake. Deepening the land component does not affect the surface temperature. This result can be used to derive land heat uptake estimates from different sources, which are much closer to previous observational reports.
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci., 9, 963–979, https://doi.org/10.5194/wes-9-963-2024, https://doi.org/10.5194/wes-9-963-2024, 2024
Short summary
Short summary
This study compares the results of two wind farm parameterizations (WFPs) in the Weather Research and Forecasting model, simulating a two-turbine array under three atmospheric stabilities with large-eddy simulations. We show that the WFPs accurately depict wind speeds either near turbines or in the far-wake areas, but not both. The parameterizations’ performance varies by variable (wind speed or turbulent kinetic energy) and atmospheric stability, with reduced accuracy in stable conditions.
Félix García-Pereira, Jesús Fidel González-Rouco, Thomas Schmid, Camilo Melo-Aguilar, Cristina Vegas-Cañas, Norman Julius Steinert, Pedro José Roldán-Gómez, Francisco José Cuesta-Valero, Almudena García-García, Hugo Beltrami, and Philipp de Vrese
SOIL, 10, 1–21, https://doi.org/10.5194/soil-10-1-2024, https://doi.org/10.5194/soil-10-1-2024, 2024
Short summary
Short summary
This work addresses air–ground temperature coupling and propagation into the subsurface in a mountainous area in central Spain using surface and subsurface data from six meteorological stations. Heat transfer of temperature changes at the ground surface occurs mainly by conduction controlled by thermal diffusivity of the subsurface, which varies with depth and time. A new methodology shows that near-surface diffusivity and soil moisture content changes with time are closely related.
Pedro José Roldán-Gómez, Jesús Fidel González-Rouco, Jason E. Smerdon, and Félix García-Pereira
Clim. Past, 19, 2361–2387, https://doi.org/10.5194/cp-19-2361-2023, https://doi.org/10.5194/cp-19-2361-2023, 2023
Short summary
Short summary
Analyses of reconstructed data suggest that the precipitation and availability of water have evolved in a similar way during the Last Millennium in different regions of the world, including areas of North America, Europe, the Middle East, southern Asia, northern South America, East Africa and the Indo-Pacific. To confirm this link between distant regions and to understand the reasons behind it, the information from different reconstructed and simulated products has been compiled and analyzed.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 8, 1153–1178, https://doi.org/10.5194/wes-8-1153-2023, https://doi.org/10.5194/wes-8-1153-2023, 2023
Short summary
Short summary
This study investigates the performance of pumping-mode ground-generation airborne wind energy systems by determining power-optimal flight trajectories based on realistic, k-means clustered, vertical wind velocity profiles. These profiles, derived from mesoscale weather simulations at an offshore and an onshore site in Europe, are incorporated into an optimal control model that maximizes average cycle power by optimizing the kite's trajectory.
Philipp de Vrese, Goran Georgievski, Jesus Fidel Gonzalez Rouco, Dirk Notz, Tobias Stacke, Norman Julius Steinert, Stiig Wilkenskjeld, and Victor Brovkin
The Cryosphere, 17, 2095–2118, https://doi.org/10.5194/tc-17-2095-2023, https://doi.org/10.5194/tc-17-2095-2023, 2023
Short summary
Short summary
The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone. We used an adapted version of the Max Planck Institute (MPI) Earth System Model to show that differences in the representation of the soil hydrology in permafrost-affected regions could help explain a large part of this inter-model spread and have pronounced impacts on important elements of Earth systems as far to the south as the tropics.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Xiaoli Guo Larsén, Marc Imberger, Ásta Hannesdóttir, and Andrea N. Hahmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2022-102, https://doi.org/10.5194/wes-2022-102, 2023
Revised manuscript not accepted
Short summary
Short summary
We study how climate change will impact extreme winds and choice of turbine class. We use data from 18 CMIP6 members from a historic and a future period to access the change in the extreme winds. The analysis shows an overall increase in the extreme winds in the North Sea and the southern Baltic Sea, but a decrease over the Scandinavian Peninsula and most of the Baltic Sea. The analysis is inconclusive to whether higher or lower classes of turbines will be installed in the future.
Andrea N. Hahmann, Oscar García-Santiago, and Alfredo Peña
Wind Energ. Sci., 7, 2373–2391, https://doi.org/10.5194/wes-7-2373-2022, https://doi.org/10.5194/wes-7-2373-2022, 2022
Short summary
Short summary
We explore the changes in wind energy resources in northern Europe using output from simulations from the Climate Model Intercomparison Project (CMIP6) under the high-emission scenario. Our results show that climate change does not particularly alter annual energy production in the North Sea but could affect the seasonal distribution of these resources, significantly reducing energy production during the summer from 2031 to 2050.
Graziela Luzia, Andrea N. Hahmann, and Matti Juhani Koivisto
Wind Energ. Sci., 7, 2255–2270, https://doi.org/10.5194/wes-7-2255-2022, https://doi.org/10.5194/wes-7-2255-2022, 2022
Short summary
Short summary
This paper presents a comprehensive validation of time series produced by a mesoscale numerical weather model, a global reanalysis, and a wind atlas against observations by using a set of metrics that we present as requirements for wind energy integration studies. We perform a sensitivity analysis on the numerical weather model in multiple configurations, such as related to model grid spacing and nesting arrangements, to define the model setup that outperforms in various time series aspects.
Francisco José Cuesta-Valero, Hugo Beltrami, Stephan Gruber, Almudena García-García, and J. Fidel González-Rouco
Geosci. Model Dev., 15, 7913–7932, https://doi.org/10.5194/gmd-15-7913-2022, https://doi.org/10.5194/gmd-15-7913-2022, 2022
Short summary
Short summary
Inversions of subsurface temperature profiles provide past long-term estimates of ground surface temperature histories and ground heat flux histories at timescales of decades to millennia. Theses estimates complement high-frequency proxy temperature reconstructions and are the basis for studying continental heat storage. We develop and release a new bootstrap method to derive meaningful confidence intervals for the average surface temperature and heat flux histories from any number of profiles.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 7, 1847–1868, https://doi.org/10.5194/wes-7-1847-2022, https://doi.org/10.5194/wes-7-1847-2022, 2022
Short summary
Short summary
This research explores the ground-generation airborne wind energy system (AWES) design space and investigates scaling effects by varying design parameters such as aircraft wing size, aerodynamic efficiency and mass. Therefore, representative simulated onshore and offshore wind data are implemented into an AWES trajectory optimization model. We estimate optimal annual energy production and capacity factors as well as a minimal operational lift-to-weight ratio.
Beatriz Cañadillas, Maximilian Beckenbauer, Juan J. Trujillo, Martin Dörenkämper, Richard Foreman, Thomas Neumann, and Astrid Lampert
Wind Energ. Sci., 7, 1241–1262, https://doi.org/10.5194/wes-7-1241-2022, https://doi.org/10.5194/wes-7-1241-2022, 2022
Short summary
Short summary
Scanning lidar measurements combined with meteorological sensors and mesoscale simulations reveal the strong directional and stability dependence of the wake strength in the direct vicinity of wind farm clusters.
Almudena García-García, Francisco José Cuesta-Valero, Hugo Beltrami, J. Fidel González-Rouco, and Elena García-Bustamante
Geosci. Model Dev., 15, 413–428, https://doi.org/10.5194/gmd-15-413-2022, https://doi.org/10.5194/gmd-15-413-2022, 2022
Short summary
Short summary
We study the sensitivity of a regional climate model to resolution and soil scheme changes. Our results show that the use of finer resolutions mainly affects precipitation outputs, particularly in summer due to changes in convective processes. Finer resolutions are associated with larger biases compared with observations. Changing the land surface model scheme affects the simulation of near-surface temperatures, yielding the lowest biases in mean temperature with the most complex soil scheme.
Marc Imberger, Xiaoli Guo Larsén, and Neil Davis
Adv. Geosci., 56, 77–87, https://doi.org/10.5194/adgeo-56-77-2021, https://doi.org/10.5194/adgeo-56-77-2021, 2021
Short summary
Short summary
Events like mid-latitude storms with their high winds have an impact on wind energy production and forecasting of such events is crucial. This study investigates the capabilities of a global weather prediction model MPAS and looks at how key parameters like storm intensity, arrival time and duration are represented compared to measurements and traditional methods. It is found that storm intensity is represented well while model drifts negatively influence estimation of arrival time and duration.
Jörge Schneemann, Frauke Theuer, Andreas Rott, Martin Dörenkämper, and Martin Kühn
Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, https://doi.org/10.5194/wes-6-521-2021, 2021
Short summary
Short summary
A wind farm can reduce the wind speed in front of it just by its presence and thus also slightly impact the available power. In our study we investigate this so-called global-blockage effect, measuring the inflow of a large offshore wind farm with a laser-based remote sensing method up to several kilometres in front of the farm. Our results show global blockage under a certain atmospheric condition and operational state of the wind farm; during other conditions it is not visible in our data.
Julia Gottschall and Martin Dörenkämper
Wind Energ. Sci., 6, 505–520, https://doi.org/10.5194/wes-6-505-2021, https://doi.org/10.5194/wes-6-505-2021, 2021
Francisco José Cuesta-Valero, Almudena García-García, Hugo Beltrami, J. Fidel González-Rouco, and Elena García-Bustamante
Clim. Past, 17, 451–468, https://doi.org/10.5194/cp-17-451-2021, https://doi.org/10.5194/cp-17-451-2021, 2021
Short summary
Short summary
We provide new global estimates of changes in surface temperature, surface heat flux, and continental heat storage since preindustrial times from geothermal data. Our analysis includes new measurements and a more comprehensive description of uncertainties than previous studies. Results show higher continental heat storage than previously reported, with global land mean temperature changes of 1 K and subsurface heat gains of 12 ZJ during the last half of the 20th century.
Andreas Bechmann, Juan Pablo M. Leon, Bjarke T. Olsen, and Yavor V. Hristov
Wind Energ. Sci., 5, 1679–1688, https://doi.org/10.5194/wes-5-1679-2020, https://doi.org/10.5194/wes-5-1679-2020, 2020
Short summary
Short summary
When assessing wind resources for wind farm development, the first step is to measure the wind from tall meteorological masts. As met masts are expensive, they are not built at every planned wind turbine position but sparsely while trying to minimize the distance. However, this paper shows that it is better to focus on the
similaritybetween the met mast and the wind turbines than the distance. Met masts at similar positions reduce the uncertainty of wind resource assessments significantly.
Almudena García-García, Francisco José Cuesta-Valero, Hugo Beltrami, Fidel González-Rouco, Elena García-Bustamante, and Joel Finnis
Geosci. Model Dev., 13, 5345–5366, https://doi.org/10.5194/gmd-13-5345-2020, https://doi.org/10.5194/gmd-13-5345-2020, 2020
Martin Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann
Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, https://doi.org/10.5194/gmd-13-5079-2020, 2020
Short summary
Short summary
This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
Cited articles
Anderson, J. R., Hardy, E. E., Roach, J. T., and Witmer, R. E.: A land use
and land cover classification system for use with remote sensor data, Tech. rep., United States Geological Service, availabl e at: https://pubs.usgs.gov/pp/0964/report.pdf (last access: 18 October 2020), 1976. a
Badger, J., Frank, H., Hahmann, A. N., and Giebel, G.: Wind-climate estimation based on mesoscale and microscale modeling: Statistical-dynamical downscaling for wind energy applications, J. Appl. Meteorol. Clim., 53, 1901–1919, https://doi.org/10.1175/JAMC-D-13-0147.1, 2014. a
Benjamin, S. G., Grell, G. A., Brown, J. M., and Smirnova, T. G.: Mesoscale
weather prediction with the RUC hybrid isentropic-terrain-following coordinate model, Mon. Weather Rev., 132, 473–494,
https://doi.org/10.1175/1520-0493(2004)132<0473:MWPWTR>2.0.CO;2, 2004. a
Bosveld, F. C.: Cabauw In-situ Observational Program 2000 – Now: Instruments, Calibrations and Set-up, Tech. rep., KNMI, available at:
http://projects.knmi.nl/cabauw/insitu/observations/documentation/Cabauw_TR/Cabauw_TR.pdf
(last access: 28 June 2018), 2019. a
Chávez-Arroyo, R., Lozano-Galiana, S., Sanz-Rodrigo, J., and Probst, O.:
Statistical-dynamical downscaling of wind fields using self-organizing maps, Appl. Therm. Eng., 75, 1201–1209, https://doi.org/10.1016/j.applthermaleng.2014.03.002, 2015. a
Copernicus Land Monitoring Service: CORINE Land Cover, available at:
https://land.copernicus.eu/pan-european/corine-land-cover, last access:
15 April 2019. a
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation
data 2010 (GMTED2010), Tech. Rep. 2011-1073, US Geological Survey Open-File Report, US Geological Survey, https://doi.org/10.3133/ofr20111073, 2011. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Koehler, M.,
Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park,
B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011. a, b
Dellwik, E., Arnqvist, J., Bergström, H., Mohr, M., Söderberg, S.,
and Hahmann, A.: Meso-scale modeling of a forested landscape, J. Phys. Conf. Ser., 524, 012121, https://doi.org/10.1088/1742-6596/524/1/012121, 2014. a
Donlon, C. J., Martin, M., Stark, J. D., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice analysis (OSTIA), Remote Sens. Environ., 116, 140–158, https://doi.org/10.1016/j.rse.2010.10.017, 2012. a
Dörenkämper, M., Olsen, B. T., Witha, B., Hahmann, A. N., Davis, N. N., Barcons, J., Ezber, Y., García-Bustamante, E., Fidel González-Rouco, J., Navarro, J., Sastre-Marugán, M., Sīle, T., Trei, W., Žagar, M., Badger, J., Gottschall, J., Sanz Rodrigo, J., and Mann, J.: The Making of the New European Wind Atlas
– Part 2: Production and evaluation, Geosci. Model Dev., 13, 5079–5102, https://doi.org/10.5194/gmd-13-5079-2020, 2020. a, b, c, d, e, f, g, h, i
Draxl, C., Hahmann, A. N., Peña, A., and Giebel, G.: Evaluating winds and
vertical wind shear from WRF model forecasts using seven PBL schemes, Wind Energy, 17, 39–55, https://doi.org/10.1002/we.1555, 2014. a
Draxl, C., Clifton, A., Hodge, B.-M., and McCaa, J.: The Wind Integration
National Dataset (WIND) Toolkit, Appl. Energ., 151, 355–366,
https://doi.org/10.1016/j.apenergy.2015.03.121, 2015. a
Dudhia, J.: A multi-layer soil temperature model for MM5, in: The Sixth
PSU/NCAR Mesoscale Model Users' Workshop, Boulder, Colorado, USA, 1996. a
Edson, J., Jampana, V., Weller, R., Bigorre, S., Plueddemann, A., Fairall, C. D., Miller, S., Mahrt, L., Vickers, D., and Hersbach, H.: On the exchange of
momentum over the open ocean, J. Phys. Oceanogr., 43, 1589–1610,
https://doi.org/10.1175/JPO-D-12-0173.1, 2013. a
Fairall, C. W., Bradley, E. F., Hare, J. E., Grachev, A. A., and Edson, J. B. A.: Bulk parameterization of air-sea Fluxes: Updates and verification for
the COARE algorithm, J. Climate, 16, 571–591,
https://doi.org/10.1175/1520-0442(2003)016<0571:bpoasf>2.0.co;2, 2003. a
Fernández-González, S., Martín, M. L., Merino, A., Sánchez, J. L., and Valero, F.: Uncertainty quantification and predictability of wind speed over the Iberian Peninsula, J. Geophys. Res., 122, 3877–3890, https://doi.org/10.1002/2017JD026533, 2017. a
Fernández-González, S., Sastre, M., Valero, F., Merino, A.,
García-Ortega, E., Luis Sánchez, J., Lorenzana, J., and Martín, M. L.: Characterization of spread in a mesoscale Ensemble prediction system: Multiphysics versus Initial Conditions, Meteorol. Z., 28, 59–67, https://doi.org/10.1127/metz/2018/0918, 2018. a
Floors, R., Enevoldsen, P., Davis, N., Arnqvist, J., and Dellwik, E.: From
lidar scans to roughness maps for wind resource modelling in forested areas,
Wind Energ. Sci., 3, 353–370, https://doi.org/10.5194/wes-3-353-2018, 2018a. a
Floors, R., Hahmann, A. N., and Peña, A.: Evaluating mesoscale simulations of the coastal flow using lidar measurements, J. Geophys. Res.,
123, 2718–2736, https://doi.org/10.1002/2017JD027504, 2018b. a, b
Frank, H. and Landberg, L.: Modelling the wind climate of Ireland, Bound.-Lay
Meteorol., 85, 359–378, https://doi.org/10.1023/A:1000552601288, 1997. a
García-Díez, M., Fernández, J., Fita, L., and Yagüe, C.: Seasonal dependence of WRF model biases and sensitivity to PBL schemes over Europe, Q. J. Roy. Meteorol. Soc., 139, 501–514, https://doi.org/10.1002/qj.1976, 2013. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Gemmill, W., Katz, B., and Li, X.: Daily Real-Time Global Sea Surface
Temperature – High Resolution Analysis at NOAA/NCEP, Office note no. 260,
NOAA/NWS/NCEP/MMAB, Camp Springs, Maryland, USA, 39 pp., 2007. a
Gómez-Navarro, J. J., Raible, C. C., and Dierer, S.: Sensitivity of the
WRF model to PBL parametrizations and nesting techniques: evaluation of
surface wind over complex terrain, Geosci Model Dev., 8, 3349–3363,
https://doi.org/10.5194/gmd-8-3349-2015, 2015. a
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014. a
Hahmann, A. N.: Summary wind statistics from NEWA WRF mesoscale ensemble [Data set], Zenodo, https://doi.org/10.5281/zenodo.4002351, 2020. a
Hahmann, A. N., Rostkier-Edelstein, D., Warner, T. T., Vandenberghe, F., Liu,
Y., Babarsky, R., and Swerdlin, S. P.: A reanalysis system for the generation of mesoscale climatographies, J. Appl. Meteorol. Clim., 49, 954–972, https://doi.org/10.1175/2009JAMC2351.1, 2010. a
Hahmann, A. N., Davis, N. N., Dörenkämper, M., Sīle, T., Witha, B., and Trey, W.: WRF configuration files for NEWA mesoscale ensemble and
production simulations, Zenodo, https://doi.org/10.5281/zenodo.3709088, 2020. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 Global Reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b
Hong, S.-Y., Dudhia, J., and Chen, S.-H.: A revised approach to ice
microphysical processes for the bulk parameterization of clouds and
precipitation., Mon. Weatjer Rev., 132, 103–120,
https://doi.org/10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2, 2004. a, b
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long–lived greenhouse gases:
Calculations with the AER radiative transfer models., J. Geophys. Res., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Janjic, Z. I. and Zavisa, I.: The Step–Mountain Eta Coordinate Model: Further developments of the convection, viscous sublayer, and turbulence closure schemes, Mon. Weather Rev., 122, 927–945, 1994. a
Jiménez, P., García-Bustamante, E., González-Rouco, J., Valero, F., Montávez, J., and Navarro, J.: Surface Wind Regionalization over Complex Terrain: Evaluation and Analysis of a High-Resolution WRF Simulation, J. Appl. Meteorol. Clim., 49, 268–287, https://doi.org/10.1175/2009JAMC2175.1, 2010. a
Jiménez, P., González-Rouco, J., Montávez, J., Navarro, J.,
García-Bustamante, E., and Dudhia, J.: Analysis of the long-term surface
wind variability over complex terrain using a high spatial resolution WRF
simulation, Clim. Dynam., 40, 1643–1656, https://doi.org/10.1007/s00382-012-1326-z, 2013. a
Jiménez, P. A., Vilà-Guerau de Arellano, J., González-Rouco, J. F., Navarro, J., Montávez, J. P., García-Bustamante, E., and Dudhia, J.: The Effect of Heat Waves and Drought on Surface Wind Circulations in the
Northeast of the Iberian Peninsula during the Summer of 2003, J. Climate, 24, 5416–5422, https://doi.org/10.1175/2011JCLI4061.1, 2011. a
Jiménez, P. A., Dudhia, J., Gonzalez-Rouco, J. F., Navarro, J., Montavez,
J. P., and Garcia-Bustamante, E.: A Revised Scheme for the WRF Surface Layer Formulation, Mon. Weather Rev., 140, 898–918, https://doi.org/10.1175/MWR-D-11-00056.1, 2012. a
Kain, J. S.: The Kain–Fritsch convective parameterization: An update, J.
Appl. Meteorol. Clim., 43, 170–181,
https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004. a
Kalverla, P. C., Steeneveld, G. J., Ronda, R. J., and Holtslag, A. A.: An
observational climatology of anomalous wind events at offshore meteomast
IJmuiden (North Sea), J. Wind Eng. Ind. Aerodyn., 165, 86–99,
https://doi.org/10.1016/j.jweia.2017.03.008, 2017. a, b, c
Katragkou, E., García-Díez, M., Vautard, R., Sobolowski, S., Zanis, P., Alexandri, G., Cardoso, R. M., Colette, A., Fernandez, J., Gobiet, A., Goergen, K., Karacostas, T., Knist, S., Mayer, S., Soares, P. M. M., Pytharoulis, I., Tegoulias, I., Tsikerdekis, A., and Jacob, D.: Regional climate hindcast simulations within EURO-CORDEX: evaluation of a WRF multi-physics ensemble, Geosci. Model Dev., 8, 603–618, https://doi.org/10.5194/gmd-8-603-2015, 2015. a, b
Kruse, C., Vento, D. D., Montuoro, R., Lubin, M., and McMillan, S.: Evaluation of WRF scaling to several thousand cores on the Yellowstone
supercomputer, in: Proceedings of the Front Range Consortium for Research
Computing Conference, 14 August 2013, Boulder, CO, USA, 2013. a
Lee, J. A., Kolczynski, W. C., McCandless, T. C., and Haupt, S. E.: An
Objective Methodology for Configuring and Down-Selecting an NWP Ensemble for
Low-Level Wind Prediction, Mon. Weather Rev., 140, 2270–2286,
https://doi.org/10.1175/MWR-D-11-00065.1, 2012. a
Li, D., Bou-Zeid, E., Barlage, M., Chen, F., and Smith, J. A.: Development and Evaluation of a Mosaic Approach in the WRF-Noah Framework, J. Geophys.
Res., 118, 11918–11935, https://doi.org/10.1002/2013JD020657, 2013. a
Li, S., Rupp, D. E., Hawkins, L., Mote, P. W., McNeall, D., Sparrow, S. N.,
Wallom, D. C. H., Betts, R. A., and Wettstein, J. J.: Reducing climate model
biases by exploring parameter space with large ensembles of climate model
simulations and statistical emulation, Geosci Model Dev., 12, 3017–3043,
https://doi.org/10.5194/gmd-12-3017-2019, 2019. a
Lucio-Eceiza, E. E., González-Rouco, J. F., Navarro, J., and Beltrami, H.: Quality Control of surface wind observations in North Eastern North America. Part I: Data Management Issues, J. Atmos. Ocean. Tech., 35, 163–182, https://doi.org/10.1175/JTECH-D-16-0204.1, 2018a. a
Lucio-Eceiza, E. E., González-Rouco, J. F., Navarro, J., Beltrami, H., and Conte, J.: Quality control of surface wind observations in North Eastern
North America. Part II: Measurement errors, J. Atmos. Ocean. Tech., 35,
183–205, https://doi.org/10.1175/JTECH-D-16-0205.1, 2018b. a
Lupu, N., Selios, L., and Warner, Z.: A new measure of congruence: The Earth Mover's Distance, Polit. Anal., 25, 95–113, https://doi.org/10.1017/pan.2017.2, 2017. a
Mayner, W.: PyEMD: Fast EMD for Python, available at: https://pypi.org/project/pyemd/ (last access: 19 October 2020), 2018. a
Mortensen, N. G., Heathfield, D. N., Rathmann, O., and Nielsen, M.: Wind Atlas Analysis and Application Program: WAsP 10 Help Facility, Tech. rep., DTU Wind Energy, available at:
https://orbit.dtu.dk/files/116352660/WAsP_10_Help_Facility.pdf (last access: 18 October 2020), 2011. a
Nakanishi, M. and Niino, H.: An improved Mellor-Yamada Level-3 model: Its
numerical stability and application to a regional prediction of advection
fog, Bound.-Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8,
2006. a
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure
model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn., 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009. a, b
Nawri, N., Petersen, G., Bjornsson, H., Hahmann, A., Jónasson, K., Hasager, C., and Clausen, N.-E.: The wind energy potential of Iceland, Renew. Energ., 69, 290–291, https://doi.org/10.1016/j.renene.2014.03.040, 2014. a
NCAR: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, https://doi.org/10.5065/D6M043C6, 2000. a
NCAR: WRF Model User's Page (WRF Version 3.8.1), https://doi.org/10.5065/D6MK6B4K, 2020. a
NEWA: New European Wind Atlas, available at: https://map.neweuropeanwindatlas.eu/ (last access: 19 October 2020), 2018. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011. a
Noilhan, J. and Planton, S.: A simple parameterization of land surface processes for meteorological models, Mon. Weatjer Rev., 117, 536–549,
https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2, 1989. a
Olsen, B. T., Hahmann, A. N., Sempreviva, A. M., Badger, J., and Jørgensen, H. E.: An intercomparison of mesoscale models at simple sites for wind energy applications, Wind Energy, 2, 211–228, https://doi.org/10.5194/wes-2-211-2017, 2017. a
Olson, J., Kenyon, J., Brown, J., Angevine, W., and Suselj, K.: Updates to the MYNN PBL and surface layer scheme for RAP/HRRR, NOAA Earth System Research Laboratory, Boulder, CO, USA, available at:
http://www2.mmm.ucar.edu/wrf/users/workshops/WS2016/oral_presentations/6.6.pdf, last access: 15 January 2016. a
Pele, O. and Werman, M.: A Linear Time Histogram Metric for Improved SIFT Matching, in: ECCV 2008, Computer Vision – ECCV 2008, Lecture Notes in Computer Science, vol. 5304, edited by: Forsyth, D., Torr, P., and Zisserman, A., Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-540-88690-7_37, 2008. a
Peña, A.: Østerild: A natural laboratory for atmospheric turbulence, J. Renew. Sustain. Energ., 11, 063302, https://doi.org/10.1063/1.5121486, 2019. a
Peña, A., Floors, R., Sathe, A., Gryning, S.-E., Wagner, R., Courtney, M. S., Larsén, X. G., Hahmann, A. N., and Hasager, C. B.: Ten Years of
Boundary-Layer and Wind-Power Meteorology at Høvsøre, Denmark, Bound.-Lay. Meteorol., 158, 1–26, https://doi.org/10.1007/s10546-015-0079-8, 2015. a
Petersen, E. L.: In search of the wind energy potential, J. Renew. Sustain.
Energ., 9, 052301, https://doi.org/10.1063/1.4999514, 2017. a
Pinard, J., Benoit, R., and Yu, W.: A WEST wind climate simulation of the
mountainous Yukon, Atmos.-Ocean, 43, 259–282, https://doi.org/10.3137/ao.430306, 2005. a
Pleim, J. E.: A Combined Local and Nonlocal Closure Model for the Atmospheric
Boundary Layer. Part I: Model Description and Testing, J. Appl. Meteorol. Clim., 46, 1383–1395, 2007. a
Poulter, B., MacBean, N., Hartley, A., and coauthors: Plant functional type
classification for earth system models: results from the European Space
Agency's Land Cover Climate Change Initiative, Geosci. Model Dev., 8,
2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, 2015. a
Rabin, J., Delon, J., and Gousseau, Y.: Circular Earth Mover's Distance for
the comparison of local features, in: IEEE 2008 19th Int. Conf. Pattern
Recognit., 8–11 December 2008, Tampa, FL, USA, 1–4, https://doi.org/10.1109/ICPR.2008.4761372, 2008. a, b
Refslund, J., Dellwik, E., Hahmann, A. N., Barlage, M. J., and Boegh, E.:
Development of satellite green vegetation fraction time series for use in
mesoscale modeling: application to the European heat wave 2006, Theor. Appl.
Climatol., 117, 377–392, https://doi.org/10.1007/s00704-013-1004-z, 2014. a
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and
Schlax, M. G.: Daily High-Resolution-Blended Analyses for Sea Surface
Temperature, J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1, 2007. a
Reynolds, R. W., Gentemann, C. L., and Corlett, G. K.: Evaluation of AATSR
and TMI Satellite SST Data, J. Climate, 23, 152–165, https://doi.org/10.1175/2009JCLI3252.1, 2010. a
Rife, D. L. and Davis, C. A.: Verification of temporal variations in mesoscale numerical wind forecasts, Mon. Weather Rev., 133, 3368–3381,
https://doi.org/10.1175/MWR3052.1, 2005. a
Rubner, Y., Tomasi, C., and Guibas, L. J.: The Earth Mover's Distance as a
Metric for Image Retrieval, Int. J. Comput. Vis., 40, 99–121, https://doi.org/10.1023/A:1026543900054, 2000. a
Santos-Alamillos, F., Pozo-Vázquez, D., Ruiz-Arias, J., and Tovar-Pescador, J.: Influence of land-use misrepresentation on the accuracy
of WRF wind estimates: Evaluation of GLCC and CORINE land-use maps in southern Spain, Atmos. Res., 157, 17–28, https://doi.org/10.1016/j.atmosres.2015.01.006, 2015. a
Siuta, D., West, G., and Stull, R.: WRF hub-height wind forecast sensitivity
to PBL scheme, grid length, and initial condition choice in complex terrain,
Weather Forecast., 32, 493–509, https://doi.org/10.1175/WAF-D-16-0120.1, 2017. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A Description of the
Advanced Research WRF Version 3, Tech. Rep. NCAR/TN−475+STR, National Center for Atmospheric Research, Boulder, Colorado, USA, 2008. a
Smith, E. N., Gibbs, J. A., Fedorovich, E., and Klein, P. M.: WRF model study
of the Great Plains low-level jet: Effects of grid spacing and boundary layer
parameterization, J. Appl. Meteorol. Clim., 57, 2375–2397,
https://doi.org/10.1175/JAMC-D-17-0361.1, 2018. a
Strobach, E. and Bel, G.: Regional Decadal Climate Predictions Using an Ensemble of WRF Parameterizations Driven by the MIROC5 GCM, J. Appl. Meteorol. Clim., 58, 527–549, https://doi.org/10.1175/JAMC-D-18-0051.1, 2019. a
Tammelin, B., Vihma, T., Atlaskin, E., Badger, J., Fortelius, C., Gregow, H.,
Horttanainen, M., Hyvönen, R., Kilpinen, J., Latikka, J., Ljungberg, K.,
Mortensen, N. G., Niemelä, S., Ruosteenoja, K., Salonen, K., Suomi, I.,
and Venäläinen, A.: Production of the Finnish Wind Atlas, Wind Energy, 16, 19–35, https://doi.org/10.1002/we.517, 2013. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek,
M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification
of the unified Noah land surface model in the WRF model, in: 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, AMS, 12–16 January 2004, Seattle, 2004. a, b
Thompson, D. R., Horstmann, J., Mouche, A., Winstead, N. S., Sterner, R., and
Monaldo, F. M.: Comparison of high-resolution wind fields extracted from
TerraSAR-X SAR imagery with predictions from the WRF mesoscale model, J. Geophys. Res., 117, C02035, https://doi.org/10.1029/2011JC007526, 2012. a
Troen, I. and Petersen, E. L.: European Wind Atlas, Published for the Commission of the European Communities, Directorate-General for Science,
Research, and Development, Brussels, Belgium by Risø National Laboratory,
available at: https://backend.orbit.dtu.dk/ws/portalfiles/portal/112135732/European_Wind_Atlas.pdf
(last access: 18 October 2020), 1989. a, b, c
Vincent, C. L. and Hahmann, A. N.: The Impact of Grid and Spectral Nudging on
the Variance of the Near-Surface Wind Speed, J. Appl. Meteorol. Clim., 54,
1021–1038, https://doi.org/10.1175/JAMC-D-14-0047.1, 2015. a
Wang, W., Dudhia, J., and Chen, M.: Application of WRF – How to get better
performance, National Center for Atmospheric Research, Boulder, CO, USA,
available at: http://www2.mmm.ucar.edu/wrf/users/tutorial/201901/chen_best_practices.pdf
(last access: 11 January 2018), 2019.
a
Westerhellweg, A., Neumann, T., and Riedel, V.: FINO1 Mast Correction, DEWI Magazin North America Inc., available at:
https://pdfs.semanticscholar.org/cf85/2b7bc731b071162e537edf45f9578f4ec86e.pdf
(last access: 20 February 2019), 2012. a
Wijnant, I., van Ulft, B., van Stratum, B., Barkmeijer, J., Onvlee, J., de Valk, C., Knoop, S., Kok, S., Marseille, G., Baltink, H. K., and Stepek,
A.: The Dutch Offshore Wind Atlas (DOWA): Description of the dataset, Tech. Rep. TR-380, Royal Netherlands Meteorological Institute (KNMI),
available at: https://www.dutchoffshorewindatlas.nl/, last access: 8 December 2019. a
Witha, B., Hahmann, A., Sile, T., Dörenkämper, M., Ezber, Y.,
García-Bustamante, E., González-Rouco, J. F., Leroy, G., and Navarro, J.: WRF model sensitivity studies and specifications for the NEWA mesoscale wind atlas production runs, Tech. rep., Carl von Ossietzky University of Oldenburg, Oldenburg, https://doi.org/10.5281/ZENODO.2682604, 2019. a
Wohlfart, C., Winkler, K., Wendleder, A., and Roth, A.: TerraSAR-X and
Wetlands: A Review, Remote Sens., 10, 916, https://doi.org/10.3390/rs10060916, 2018. a
Yang, B., Qian, Y., Berg, L. K., Ma, P.-L., Wharton, S., Bulaevskaya, V., Yan, H., Hou, Z., and Shaw, W. J.: Sensitivity of turbine-height wind speeds to parameters in planetary boundary-layer and surface-layer schemes in the
Weather Research and Forecasting model, Bound.-Lay. Meteorol., 162, 117–142, https://doi.org/10.1007/s10546-016-0185-2, 2017. a
Short summary
Wind energy resource assessment routinely uses numerical weather prediction model output. We describe the evaluation procedures used for picking the suitable blend of model setup and parameterizations for simulating European wind climatology with the WRF model. We assess the simulated winds against tall mast measurements using a suite of metrics, including the Earth Mover's Distance, which diagnoses the performance of each ensemble member using the full wind speed and direction distribution.
Wind energy resource assessment routinely uses numerical weather prediction model output. We...
