%0 Generic %1 Guo2013Simple %A Guo, Zhongling %D 2013 %K wind statistics downscaling syntheticdata %T A simple method to downscale daily wind statistics to hourly wind data %U http://arxiv.org/abs/1305.3367 %X Wind is the principal driver in the wind erosion models. The hourly wind speed data were generally required for precisely wind erosion modeling. In this study, a simple method to generate hourly wind speed data from daily wind statistics (daily average and maximum wind speeds together or daily average wind speed only) was established. A typical windy location with 3285 days (9 years) measured hourly wind speed data were used to validate the downscaling method. The results showed that the overall agreement between observed and simulated cumulative wind speed probability distributions appears excellent, especially for the wind speeds greater than 5 m s-1 range (erosive wind speed). The results further revealed that the values of daily average erosive wind power density (AWPD) calculated from generated wind speeds fit the counterparts computed from measured wind speeds well with high models' efficiency (Nash-Sutcliffe coefficient). So that the hourly wind speed data can be predicted from daily average and maximum wind speed data or only daily average wind speed data, respectively. Further studies are needed to examine the findings for inter-site wind data.

@misc{Guo2013Simple, abstract = {Wind is the principal driver in the wind erosion models. The hourly wind speed data were generally required for precisely wind erosion modeling. In this study, a simple method to generate hourly wind speed data from daily wind statistics (daily average and maximum wind speeds together or daily average wind speed only) was established. A typical windy location with 3285 days (9 years) measured hourly wind speed data were used to validate the downscaling method. The results showed that the overall agreement between observed and simulated cumulative wind speed probability distributions appears excellent, especially for the wind speeds greater than 5 m s-1 range (erosive wind speed). The results further revealed that the values of daily average erosive wind power density (AWPD) calculated from generated wind speeds fit the counterparts computed from measured wind speeds well with high models' efficiency (Nash-Sutcliffe coefficient). So that the hourly wind speed data can be predicted from daily average and maximum wind speed data or only daily average wind speed data, respectively. Further studies are needed to examine the findings for inter-site wind data.}, added-at = {2018-06-18T21:23:34.000+0200}, archiveprefix = {arXiv}, author = {Guo, Zhongling}, biburl = {https://www.bibsonomy.org/bibtex/2d2ab52356e61f173dc5158180cf5d5b9/pbett}, citeulike-article-id = {12356966}, citeulike-linkout-0 = {http://arxiv.org/abs/1305.3367}, citeulike-linkout-1 = {http://arxiv.org/pdf/1305.3367}, comment = {(private-note)They just assume that wind speed follows a sinusoidal diurnal cycle, so given the mean wind speed (or better yet, mean and max), you can conjure up what it could have been at any other hour in the day. I would be surprised if this got published?}, day = 15, eprint = {1305.3367}, interhash = {49391baab0252ebd336078dd104e28da}, intrahash = {d2ab52356e61f173dc5158180cf5d5b9}, keywords = {wind statistics downscaling syntheticdata}, month = may, posted-at = {2013-05-21 11:42:28}, priority = {2}, timestamp = {2018-06-22T18:33:28.000+0200}, title = {A simple method to downscale daily wind statistics to hourly wind data}, url = {http://arxiv.org/abs/1305.3367}, year = 2013 }