Quality Control of Surface Wind Observations in Northeastern North America. Part II: Measurement Errors

A quality control (QC) process has been developed and applied to an observational database of surface wind speed and wind direction in northeastern North America. The database combines data from three datasets of different initial quality, including a total of 526 land stations and buoys distributed over the provinces of eastern Canada and five adjacent northeastern U.S. states. The data span from 1953 to 2010. The first part of the QC deals with data management issues and is developed in a companion paper. Part II, presented herein, is focused on the detection of measurement errors and deals with low-variability errors, like the occurrence of unrealistically long calms, and high-variability problems, like rapid changes in wind speed; some types of biases in wind speed and wind direction are also considered. About 0.5% (0.16%) of wind speed (wind direction) records have been flagged. Additionally, 15.87% (1.73%) of wind speed (wind direction) data have been corrected. The most pervasive error type in terms of affected sites and erased data corresponds to unrealistic low wind speeds (89% of sites affected with 0.35% records removed). The amount of detected and corrected/removed records in Part II (~9%) is approximately two orders of magnitude higher than that of Part I. Both management and measurement errors are shown to have a discernible impact on the statistics of the database.EELE was supported by the Agreement of Cooperation 4164281 between the UCM and St. Francis Xavier University, and projects CGL2014- 59644-R and PCIN-2014-017-C07-06 of the MINECO (Spain). Funding for 4164281 was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC DG 140576948), the Canada Research Chairs Program (CRC 230687), and the Atlantic Innovation Fund (AIF-ACOA). HB holds a Canada Research Chair in Climate Dynamics. JN and JFGR were supported by projects PCIN-2014-017-C07-03, PCIN-2014-017-C07-06, CGL2011-29677-C02-01, and CGL2011-29677-C02-02 of the MINECO (Spain). JC was supported by Global Forecasters until March 2014. This research has been conducted under the Joint Research Unit between UCM and CIEMAT, by the Collaboration Agreement 7158/ 2016. The research has also received funding from the European Union’s Horizon 2020 Programme (2014–2020) and from Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (www.hpc4e.eu), Grant Agreement 689772.Peer reviewe