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dc.contributor.authorHämäläinen, Karolina
dc.contributor.authorVakkari, Ville
dc.contributor.authorSaltikoff, Elena
dc.contributor.authorHyvärinen, Otto
dc.contributor.authorNiemelä, Sami
dc.date.accessioned2020-04-20T06:27:29Z
dc.date.available2020-04-20T06:27:29Z
dc.date.issued2020
dc.identifier.citationHämäläinen, K. et al. 2020. Assessment of probabilistic wind forecasts at 100 m above ground level using doppler lidar and weather radar wind profiles. Monthly weather reviews, 148(3):1321-1334. [https://doi.org/10.1175/MWR-D-19-0184.1]en_US
dc.identifier.issn0027-0644
dc.identifier.issn1520-0493 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/34559
dc.identifier.urihttp://journals.ametsoc.org/doi/pdf/10.1175/MWR-D-19-0184.1
dc.identifier.urihttps://doi.org/10.1175/MWR-D-19-0184.1
dc.description.abstractModern society is very dependent on electricity. In the energy sector, the amount of renewable energy is growing, especially wind energy. To keep the electricity network in balance we need to know how much, when, and where electricity is produced. To support this goal, the need for proper wind forecasts has grown. Compared to traditional deterministic forecasts, ensemble models can better provide the range of variability and uncertainty. However, probabilistic forecasts are often either under- or overdispersive and biased, thus not covering the true and full distribution of probabilities. Hence, statistical postprocessing is needed to increase the value of forecasts. However, traditional closer-to-surface wind observations do not support the verification of wind higher above the surface that is more relevant for wind energy production. Thus, the goal of this study was to test whether new types of observations like radar and lidar winds could be used for verification and statistical calibration of 100-m winds. According to our results, the calibration improved the forecast skill compared to a raw ensemble. The results are better for low and moderate winds, but for higher wind speeds more training data would be needed, either from a larger number of stations or using a longer training perioden_US
dc.language.isoenen_US
dc.publisherAmerican Meteorological Societyen_US
dc.subjectLidars/Lidar observationsen_US
dc.subjectRadars/Radar observationsen_US
dc.subjectEnsemblesen_US
dc.subjectRenewable energyen_US
dc.titleAssessment of probabilistic wind forecasts at 100 m above ground level using doppler lidar and weather radar wind profilesen_US
dc.typeArticleen_US
dc.contributor.researchID33371210 - Vakkari, Ville T.


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