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Evaluating anemometer drift: A statistical approach to correct biases in wind speed measurement

Journal article
Authors Cesar Azorin-Molina
J. Asin
T. R. McVicar
Lorenzo Minola
J. I. Lopez-Moreno
S. M. Vicente-Serrano
Deliang Chen
Published in Atmospheric research
Volume 203
Pages 175-188
ISSN 0169-8095
Publication year 2018
Published at Department of Earth Sciences
Pages 175-188
Language en
Keywords Bearing ageing, Intercomparison, Regression models, Stilling phenomenon, Wind speed, Anemometers, Regression analysis, Speed, Speed regulators, Weather information services, Automatic weather stations, Intercomparisons, Meteorological agency, Regression model, Statistical approach, Wind speed measurement, Wind, anemometer, comparative study, error correction, instrumentation, measurement method, midlatitude environment, Northern Hemisphere, wind velocity, Spain
Subject categories Meteorology and Atmospheric Sciences

Abstract

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.

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