Multi-decadal variability in a centennial reconstruction of daily wind
A wind clustering methodology capable of dynamically characterizing and long-term reconstructing daily surface wind series is introduced and tested for six meteorological towers at different wind farms in Spain, for the period 1871–2009. On this basis this paper provides for the first time a centennial surface wind reconstruction with a daily resolution without the need of numerical simulations. Thus, several soft-computing algorithms are developed, with public domain Sea Level Pressure (SLP) Reanalysis data as the only input. These algorithms are constructed by tackling an Euclidean distances’ problem at the geostrophic speeds’ space. Once the wind-independent classifications are obtained, the methodology is calibrated by linking the obtained classifications with observed wind data, thus allowing to estimate and characterize the daily surface wind speed and direction. A cross-validation is then performed in order to obtain several measures of goodness of the method, such as its wind speed estimation uncertainty in terms of Mean Absolute Error (MAE) and Pearson correlation (r) for both the wind module and vectorial values. Regarding previous approaches, this statistic downscaling shows an outstanding performance: Wind speed module estimates produce a MAE of 1.12 m/s (0.32 m/s) in some towers for a daily (monthly) scale, as r reaches values of 0.78 (daily scale) and 0.91 (monthly scale). The wind-independent classifications allowed to perform daily surface wind speed and rose reconstructions in time periods when no wind data are available, which constitutes the main goal of this work. Thus, a 140 year daily wind reconstruction is performed and analyzed for one tower located at central Iberia. There, significant low frequency variations are detected, as well as wind speed oscillations in the 20 y band. Remarkable changes are also identified over reconstructed decadal wind speed frequency distributions and wind rose. Since long-term wind measurements are rarely available at modern wind farm sites, such an analysis on centennial reconstructed wind series can represent an appropriate tool that places the last years of observed wind speed in a climatological perspective. âº A wind clustering methodology for wind speed reconstruction is presented. âº The method allows long-term reconstruction of daily surface wind series. âº An evolutionary algorithm and a constructive heuristic are presented. âº The method is tested in six meteorological towers at different wind farms in Spain, for the period 1871–2009.