Statistical characterization of roughness uncertainty and impact on wind resource estimation

Kelly, Mark; Jørgensen, Hans Ejsing

doi:10.5194/wes-2-189-2017

Cited by 16 publications

(11 citation statements)

References 40 publications

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“…However, the accuracy of the maps and surface roughness is unknown. Previous studies have estimated the mean surface roughnesses uncertainties on the order of a factor of ∼ 3 (Kelly and Jørgensen, 2017). Halving and doubling all the roughnesses in Table 2 leads to mean wind speed errors for the 291 masts in this study of −0.11 ± 0.75 and 0.61 ± 0.78 after downscaling with WAsP, showing the wide span of results one can obtained within "reasonable" roughness values.…”

Section: Discussionsupporting

confidence: 54%

“…Therefore, the evaluation is based on mast-specific wind climates estimated with the NEWA model chain, modified slightly to be flexible for the purpose of the evaluation. The main differences between the methods used for the longterm atlas and for the evaluation presented here are as follows: (1) the evaluation method represents wind climates as a histogram (bins) throughout, while the long-term method fits a Weibull distribution during the generalization step, and (2) the evaluation method assumes neutral stability for vertical extrapolation and makes no stability correction to the wind climate, while the long-term method assumes slightly stable conditions (on land) for vertical extrapolation and makes a stability correction (Kelly and Troen, 2016) during the generalization step. These two factors should add very small differences between the two methods of evaluation.…”

Section: Discussionmentioning

confidence: 99%

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The Making of the New European Wind Atlas – Part 2: Production and evaluation

et al. 2020

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Abstract. This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). In Part 1, we described the sensitivity experiments and accompanying evaluation done to arrive at the final mesoscale model setup used to produce the mesoscale wind atlas. In this paper, Part 2, we document how we made the final wind atlas product, covering both the production of the mesoscale climatology generated with the Weather Research and Forecasting (WRF) model and the microscale climatology generated with the Wind Atlas Analysis and Applications Program (WAsP). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the downscaling using WAsP. We show the main results from the final wind atlas and present a comprehensive evaluation of each component of the NEWA model chain using observations from a large set of tall masts located all over Europe. The added value of the WRF and WAsP downscaling of wind climatologies is evaluated relative to the performance of the driving ERA5 reanalysis and shows that the WRF downscaling reduces the mean wind speed bias and spread relative to that of ERA5 from -1.50±1.30 to 0.02±0.78 m s−1. The WAsP downscaling has an added positive impact relative to that of the WRF model in simple terrain. In complex terrain, where the assumptions of the linearized flow model break down, both the mean bias and spread in wind speed are worse than those from the raw mesoscale results.

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Section: Discussionsupporting

confidence: 54%

Section: Discussionmentioning

confidence: 99%

The Making of the New European Wind Atlas – Part 2: Production and evaluation

et al. 2020

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“…While the relative change of LAI is 8% between the 1980s and 2010s, the modelled relative change in z 0 is only 1.3%, changing from 0.2569 in the 1980s to 0.2601 in the 2010s. Assuming an unchanged pressure gradient, the change in z 0 leads to 0.35% decrease of surface wind speed at 10 m height (Kelly and Jørgensen 2017), which is −0.013 m s −1 during 1982-2011. It is still an order of magnitude smaller than the observed trend from in situ observations (−0.198 m s −1 , figure 1(b)).…”

Section: Modelled Response Of U Over Land To the Greening Of The Earthmentioning

confidence: 99%

Global terrestrial stilling: does Earth’s greening play a role?

Zeng

Piao

et al. 2018

Environ. Res. Lett.

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Previous studies have documented that surface wind speed (u) has been increasing over the ocean but decreasing over land for the past several decades. The decreasing u at the surface over land has been referred to as terrestrial stilling. A plausible hypothesis for terrestrial stilling is an increase in surface roughness associated with changes in land surface (e.g. enhanced vegetation growth, landscape fragmentation or urbanization). One of the most widespread land surface changes is enhanced vegetation leaf area index (LAI) known as greening, particularly over the middle to high latitudes of the Northern Hemisphere where strong stilling is observed from weather station data. In this study, we examine the hypothesis that enhanced vegetation LAI is a key driver of global terrestrial stilling. We first characterized the trend in u over the ocean using long-term satellite altimeter measurements, and the trend in u over land using continuous wind records from 4305 in situ meteorological stations. We then performed initial condition ensemble Atmospheric Model Intercomparison Project-type simulations using two state-of-the-art Earth system models (IPSL-CM and CESM) to isolate the response of u to the historical increase in LAI (representing the greening) for the period 1982-2011. Both models, forced with observed sea surface temperature and sea ice and with LAI from satellite observation, captured the observed strengthening of Pacific trade winds and Southern Ocean westerly winds. However, these simulations did not reproduce the weakening of surface winds over land as significantly as it appears in the observations (−0.006 m s −1 versus −0.198 m s −1 during 1982-2011), indicating that enhanced LAI (greening) is not a dominant driver for terrestrial stilling.

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“…In practice, a background value for z 0 and d is set, and adjustments are made for specific areas where the roughness and displacement height differ from the background values. Manual assessments are subjective and time-consuming, and they can lead to a high level of uncertainty of the estimated wind resource (Kelly and Jørgensen, 2017).…”

Section: Introductionmentioning

confidence: 99%

Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling

et al. 2021

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Abstract. Wind turbines in northern Europe are frequently placed in forests, which sets new wind resource modelling requirements. Accurate mapping of the land surface can be challenging at forested sites due to sudden transitions between patches with very different aerodynamic properties, e.g. tall trees, clearings, and lakes. Tree growth and deforestation can lead to temporal changes of the forest. Global or pan-European land cover data sets fail to resolve these forest properties, aerial lidar campaigns are costly and infrequent, and manual digitization is labour-intensive and subjective. Here, we investigate the potential of using satellite observations to characterize the land surface in connection with wind energy flow modelling using the Wind Atlas Analysis and Application Program (WAsP). Collocated maps of the land cover, tree height, and leaf area index (LAI) are generated based on observations from the Sentinel-1 and Sentinel-2 missions combined with the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Three different forest canopy models are applied to convert these maps to roughness lengths and displacement heights. We introduce new functionalities for WAsP, which can process detailed land cover maps containing both roughness lengths and displacement heights. Validation is carried out through cross-prediction analyses at eight well-instrumented sites in various landscapes where measurements at one mast are used to predict wind resources at another nearby mast. The use of novel satellite-based input maps in combination with a canopy model leads to lower cross-prediction errors of the wind power density (rms = 10.9 %–11.2 %) than using standard global or pan-European land cover data sets for land surface parameterization (rms = 14.2 %–19.7 %). Differences in the cross-predictions resulting from the three different canopy models are minor. The satellite-based maps show cross-prediction errors close to those obtained from aerial lidar scans and manually digitized maps. The results demonstrate the value of using detailed satellite-based land cover maps for micro-scale flow modelling.

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Statistical characterization of roughness uncertainty and impact on wind resource estimation

Cited by 16 publications

References 40 publications

The Making of the New European Wind Atlas – Part 2: Production and evaluation

The Making of the New European Wind Atlas – Part 2: Production and evaluation

Global terrestrial stilling: does Earth’s greening play a role?

Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling

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