Wind Resource Estimation—An Overview

Landberg, L.; Myllerup, Lisbeth; Rathmann, Ole; Petersen, Erik Lundtang; Jørgensen, Birte Holst; Badger, Jake; Mortensen, Niels Gylling

doi:10.1002/we.94

Cited by 174 publications

(144 citation statements)

References 15 publications

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“…where f is the Coriolis parameter and A = 1.8 and B = 4.5 are dimensionless parameters according to [27]. Neutral atmospheric conditions are assumed as no temperature observations are available for estimation of the atmospheric stability.…”

Section: Spatial Averaging and Post-processing Proceduresmentioning

confidence: 99%

Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea

et al. 2015

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Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshore winds which can be used for offshore wind resource assessment. First, wind speeds retrieved from Synthetic Aperture Radar (SAR) and Scatterometer ASCAT images were validated against in situ measurements from seven coastal meteorological stations in South China Sea (SCS). The wind roses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and ASCAT agree well with these observations from the corresponding in situ measurements. The statistical results comparing in situ wind speed and SAR-based (ASCAT-based) wind speed for the whole co-located samples show a standard deviation (SD) of 2.09 m/s (1.83 m/s) and correlation coefficient of R 0.75 (0.80). When the offshore winds (i.e., winds directed from land to sea) are excluded, the comparison results for wind speeds show an improvement of SD and R, indicating that the satellite data are more credible over the open ocean. Meanwhile, the validation of satellite winds against the same co-located mast observations shows a OPEN ACCESSRemote Sens. 2015, 7 468 satisfactory level of accuracy which was similar for SAR and ASCAT winds. These satellite winds are then assimilated into the Weather Research and Forecasting (WRF) Model by WRF Data Assimilation (WRFDA) system. Finally, the wind resource statistics at 100 m height based on the reconstructed winds have been achieved over the study area, which fully combines the offshore wind information from multiple satellite data and numerical model. The findings presented here may be useful in future wind resource assessment based on satellite data.

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Section: Spatial Averaging and Post-processing Proceduresmentioning

confidence: 99%

Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea

et al. 2015

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“…Nowadays, wind maps and global databases have been developed for many regions around the world, such as NCEP/NCAR and ECMWF databases, containing wind speed, temperature and pressure at several heights around the world (Landberg et al, 2003). However, low resolution of some existing data (i.e.…”

Section: Measurements -Data Collectionmentioning

confidence: 99%

“…The idea is to correlate short term measurements at the target site with an overlapping long time series of the reference site using simple statistical models. According to Landberg et al (2003) climatological representativeness is obtained by having measurements for at least 5 but preferably 10 years.…”

Section: Forecasting Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Methods and tools to evaluate the availability of renewable energy sources

Angelis-Dimakis

Biberacher

Bravo

et al. 2011

Renewable and Sustainable Energy Reviews

349

132

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The recent statements of both the European Union and the US Presidency pushed in the direction of using renewable forms of energy, in order to act against climate changes induced by the growing concentration of carbon dioxide in the atmosphere. In this paper, a survey regarding methods and tools presently available to determine potential and exploitable energy in the most important renewable sectors (i.e., solar, wind, wave, biomass and geothermal energy) is presented. Moreover, challenges for each renewable resource are highlighted as well as the available tools that can help in evaluating the use of a mix of different sources.

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“…Research has shown that the lower mean speeds are linked to the higher surface roughness lengths z 0 prevalent in urban environments [2][3][4]. The manifestation of turbulence however, is less well understood.…”

Section: IImentioning

confidence: 99%