64499 20191126134632.0 doi 10.1016/j.atmosres.2017.12.010 sideral 103833 ART-2018-103833 eng Azorin-Molina, C. Evaluating anemometer drift: A statistical approach to correct biases in wind speed measurement 2018 Access copy available to the general public Unrestricted Recent studies on observed wind variability have revealed a decline (termed “stilling”) of near-surface wind speed during the last 30–50 years over many mid-latitude terrestrial regions, particularly in the Northern Hemisphere. The well-known impact of cup anemometer drift (i.e., wear on the bearings) on the observed weakening of wind speed has been mentioned as a potential contributor to the declining trend. However, to date, no research has quantified its contribution to stilling based on measurements, which is most likely due to lack of quantification of the ageing effect. In this study, a 3-year field experiment (2014–2016) with 10-minute paired wind speed measurements from one new and one malfunctioned (i.e., old bearings) SEAC SV5 cup anemometer which has been used by the Spanish Meteorological Agency in automatic weather stations since mid-1980s, was developed for assessing for the first time the role of anemometer drift on wind speed measurement. The results showed a statistical significant impact of anemometer drift on wind speed measurements, with the old anemometer measuring lower wind speeds than the new one. Biases show a marked temporal pattern and clear dependency on wind speed, with both weak and strong winds causing significant biases. This pioneering quantification of biases has allowed us to define two regression models that correct up to 37% of the artificial bias in wind speed due to measurement with an old anemometer. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 703733-STILLING info:eu-repo/grantAgreement/EC/H2020/703733/EU/Towards improved understanding of the worldwide decline of wind speed in a climate change scenario/STILLING info:eu-repo/grantAgreement/ES/DGA/Modelos Estocásticos info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 4.114 2018 METEOROLOGY & ATMOSPHERIC SCIENCES 13 / 86 = 0.151 2018 Q1 T1 1.464 2018 Atmospheric Science 2018 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion (orcid)0000-0002-0174-789X Asin, J. Universidad de Zaragoza McVicar, T.R. Minola, L. (orcid)0000-0002-7270-9313 Lopez-Moreno, J.I. (orcid)0000-0003-2892-518X Vicente-Serrano, S.M. Chen, D. 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 203 (2018), 175-188 Atmos. res. ATMOSPHERIC RESEARCH 0169-8095 570213 http://zaguan.unizar.es/record/64499/files/texto_completo.pdf Versión publicada 92764 http://zaguan.unizar.es/record/64499/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:64499 articulos driver 2019-11-26-13:41:55 ARTICLE