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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
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Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)

Authors: Simon K. Siedersleben; Andreas Platis; Julie K. Lundquist; Bughsin Djath; Astrid Lampert; Konrad Bärfuss; Beatriz Canadillas; +4 Authors

Turbulent kinetic energy over large wind farms observed and simulated by the mesoscale model WRF (3.8.1)

Abstract

This repository contains the WRF configuration files necessary to reproduce the simulations as described in Siedersleben et al. 2019 (https://doi.org/10.5194/gmd-2019-100) The file windturbines_GMD.txt contains the locations of all windturbines implemented in the simulations. The corresponding attributes of each wind turbine type is described in the wind-turbine-xx.tbl. Be aware that all windturbines use the same power and thrust coefficients only the different hub heights and rotor diameters are taken into account as described in Siedersleben et al. (2019). The namelist.input_nameOfSimulation files necessary to run the simulations are provided in this repository as well. You may notice that there are less namelist files than simulations. The simulations not using a TKE source use the same namelists as the ones with a TKE a source. However, the WRF model needs to be recompiled using the manipolated module_wind_fitch.F (you find this file in this repository). The sensitivity studies investigating the impact of the uncertainties in the power and thrust coefficients use the namelist of the control simulation CNTRb, but with manipulated wind-turbine-x_modMin/Max.tbl wind turbine files. The two python files get_era5*.py can be used to retrieve the ERA5 data, driving the WRF model. Note that the dates and pathes have to be adjusted in the python files. After downloading the surface and model level data some postprocessing is necessary as described nicely here: "http://valcap74.blogspot.com/2017/10/how-to-run-wrf-model-driven-by-era5-on.html". For this purpose the simple script called postProcessERA5 (based on the blog entry mentioned above) can be used.

Keywords

ERA5, wind turbines, WRF

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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