Modelling wind resources in climate change scenarios in complex terrains

Gonçalves, M.; Barrera-Escoda, A.; Baldasano, J. M.; Cunillera, Jordi

doi:10.1016/j.renene.2014.11.066

Cited by 16 publications

(5 citation statements)

References 33 publications

(35 reference statements)

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“…In addition to wind speed, the wind energy density (WED), that is the kinetic energy flux associated with the winds, is commonly used to assess the implications of possible future wind speed changes for the wind energy resource in any particular region [7,8,34]. It is defined as…”

Section: Energy Density and Extractable Wind Power Estimationmentioning

confidence: 99%

Future projections of wind resource in a mountainous archipelago, Canary Islands

et al. 2017

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Large-scale atmospheric patterns and near-surface winds are expected to be modified in the future due to climate change, altering the availability of wind resources on a regional scale. These possible changes are especially important in isolated power systems, as is the case for a great percentage of the islands. High-resolution climate regionalization is therefore necessary to assess the future projections of wind resource, mainly in the case of orographically complex territories, such as The Canary Islands. In this work, WRF was used to perform a dynamic regionalization in this Archipelago, using the pseudo-global warming technique to compute the initial and boundary conditions from a reanalysis dataset and from the monthly mean changes obtained from the simulations of fourteen global climate models included in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Projections of mean wind, wind energy density and extractable wind power were obtained for two future decades (2045-2054 and 2090-2099) and for two different greenhouse gas scenarios (RCP4.5 and RCP8.5) and the results were compared with those for 1995-2004. Statistically significant changes in wind resource were found in some areas, mainly during summer. Most of these areas correspond to zones where at present wind farms are located.

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Section: Energy Density and Extractable Wind Power Estimationmentioning

confidence: 99%

Future projections of wind resource in a mountainous archipelago, Canary Islands

et al. 2017

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“…Therefore, this scheme aims to correct the general tendency of WRF to overestimate wind speed (see e.g. Cheng & Steenburgh, 2005), and has subsequently been shown to be more suited for reproducing wind speed over complex terrain (Jiménez & Dudhia, 2012;Lee et al, 2014;Gómez-Navarro et al, 2015;Gonçalves-Ageitos et al 2015). In contrast to this switch, accounting for topographical and shading effects (slope_rad and topo_sha; these switches account for shadows induced by nearby topography, self-shading and land surface inclination, which subsequently affect incoming radiation) have minimal impact on the surface meteorology during these föhn case studies.…”

Section: Sensitivity Of the Model To Topographical Resolution Land Smentioning

confidence: 99%

Föhn winds on South Georgia and their impact on regional climate

Bannister

King

2015

Weather

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South Georgia is a small and mountainous island, located in the remote Southern Ocean. The island's subantarctic climate is controlled by its location and steep orography; with 19 peaks over 2000m and situated within a belt of strong westerly winds South Georgia acts as an effective barrier to the winds that impinge upon it.Since the 1920s, average summer temperatures have risen by ~1 o C on South Georgia.Coupled with this has been an increase in the rate of glacial retreat throughout the last century, with glaciers on the northeast leeside of the island retreating at a faster rate than those on the southwest side. These asymmetrical changes are thought to be linked with the strengthening of the westerlies. If the strength of the westerlies is sufficient, downslope winds can develop on the leeside of the island causing significant temperature increases as the descending air warms adiabatically; this is known as the föhn effect. Therefore, the aim of this thesis is to investigate whether the observed asymmetric pattern of regional warming and glacier retreat are caused by the föhn warming process.To explore the link between the föhn effect and its impact on the regional climate of

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“…Schär et al, 2004;Kjellström et al, 2007;Rajczak et al, 2013). For example, in areas of complex terrain like Switzerland, the main focus of high-resolution simulations with respect to wind is on case studies (Goyette, 2008;Etienne et al, 2013). Recently, simulations of about 90 storms over Switzerland were combined into a storm climatology (Stucki et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity of the WRF model to PBL parametrizations and nesting techniques: evaluation of surface wind over complex terrain

Gómez-Navarro¹,

Raible²,

Dierer³

2015

Preprint

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Abstract. Simulating surface wind over complex terrain is a challenge in regional climate modelling. Therefore, this study aims at identifying a setup of the WRF model that minimizes systematic errors of surface winds in hindcast simulations. Major factors of the model configuration are tested to find a suitable setup: the horizontal resolution, the PBL parameterization scheme and the way WRF is nested to the driving dataset. Hence, a number of sensitivity simulations at a spatial resolution of 2 km are carried out and compared to observations. Given the importance of wind storms, the analysis is based on case studies of 24 historical wind storms that caused great economic damage in Switzerland. Each of these events is downscaled using eight different model setups, but sharing the same driving dataset. The results show that the unresolved topography leads to a general overestimation of wind speed in WRF. However, this bias can be substantially reduced by using a PBL scheme that explicitly considers the effects of non-resolved topography, which also improves the spatial structure of wind speed over Switzerland. The wind direction, although generally well reproduced, is not very sensitive to the PBL scheme. Further sensitivity tests include four types of nesting methods: nesting only at the boundaries of the outermost domain, analysis and spectral nudging, and the so-called re-forecast method, where the simulation is frequently restarted. These simulations show that restricting the freedom of the model to develop large-scale disturbances slightly increases the temporal agreement with the observations, at the same time that it further reduces the overestimation of wind speed, especially for maximum wind peaks. The model skill is also evaluated in the outermost domains, where the resolution is coarser. The results demonstrate the important role of horizontal resolution, where the step from 6 to 2 km significantly improves model performance. In summary, the combination of a grid size of 2 km, the non-local PBL scheme modified to explicitly account for non-resolved orography, as well as analysis or spectral nudging, is a superior combination when dynamical downscaling is aimed at reproducing real wind fields.

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Modelling wind resources in climate change scenarios in complex terrains

Cited by 16 publications

References 33 publications

Future projections of wind resource in a mountainous archipelago, Canary Islands

Future projections of wind resource in a mountainous archipelago, Canary Islands

Föhn winds on South Georgia and their impact on regional climate

Sensitivity of the WRF model to PBL parametrizations and nesting techniques: evaluation of surface wind over complex terrain

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