Quarterly Journal of the Royal Meteorological Society Q. J. R. Meteorol. Soc. (2015) DOI:10.1002/qj.2569 A geo-statistical observation operator for the assimilation of near-surface wind data Jo¨ el B´ edard, a * St´ ephane Laroche b and Pierre Gauthier a a Department of Earth and Atmospheric Sciences, ESCER Centre, Universit´ e du Qu´ ebec ` a Montr´ eal (UQAM), Canada b Data Assimilation and Satellite Meteorology Section, Environment Canada, Dorval, Qu´ ebec, Canada *Correspondence to: J. B´ edard, Department of Earth and Atmospheric Science, ESCER Centre, Universit´ e du Qu´ ebec ` a Montr´ eal, PO Box 8888, Downtown Station, Montr´ eal, Qu´ ebec H3C 3P8, Canada. E-mail: bedard.joel@gmail.com Although many near-surface wind observations are available, very few are assimilated over land mainly due to sub-grid scale topographic interactions with the flow. The main objectives of this study are to understand the impact of near-surface wind observations on the analysis and to point out aspects that need to be improved to make a better use of these observations. A geo-statistical observation operator has been developed to correct for systematic and representativeness errors. Assimilation experiments were performed in a simplified context, assimilating only near-surface wind observations over land in the ensemble-variational data assimilation system developed at Environment Canada. Due to the background-error covariances, the assimilation of near-surface wind observations impacts the lower part of the atmosphere. The resulting correction has been evaluated by verifying the analyses against non-assimilated collocated radiosonde data. This assessment also made it possible to estimate the observation error variance to strike a balance between having an important impact at the surface and maintaining a good vertical fit to upper air observations. Results from 1 month of assimilation experiments show that the geo- statistical operator eliminates biases and significantly reduces representativeness errors as well as observation error correlations in the analysis, mainly over complex terrain. Results also show that flow-dependent background error covariances from ensembles provide better vertical information propagation than static error statistics. Overall, the analysis fit to non-assimilated collocated radiosonde observations is improved when assimilating wind observations from surface stations. Key Words: observation error statistics; representativeness error; error correlations; atmospheric boundary layer; evaluation against collocated radiosonde observations Received 6 October 2014; Revised 9 April 2015; Accepted 22 April 2015; Published online in Wiley Online Library 1. Introduction Detailed evaluations of Numerical Weather Prediction (NWP) systems indicate that errors in initial conditions and atmospheric boundary layer (ABL) parameterizations appear to be the main limitations for short-range near-surface wind prediction capabilities. B´ edard et al. (2013) showed that, when compared with persistence, NWP models offer poor short-range near- surface wind forecasts (up to 6 h). Zack et al. (2010, 2011) showed that such predictions are sensitive to local initial conditions. Rostkier-Edelstein and Hacker (2010) suggests that the assimilation of near-surface observations can significantly improve nowcasting predictions, more than enhanced forecasting models. Thus, interest is growing in the improvement of NWP modeling by means of more accurate initial conditions defined by large-scale analyses. Although many observations describing the wind field in the lower troposphere are available from the global observing system, very few are assimilated over land, mainly due to sub-grid scale topographic interactions with the flow. Until recently, near-surface wind observations over land were not used. However, with the increasing vertical and horizontal resolution of NWP models, finer topographic features are now resolved such that the assimilation of near-surface wind observations can be revisited. A number of recent studies show that the assimilation of near-surface observations, including 10 m winds over land, can be beneficial for short-range forecasts in the lower troposphere (Hacker and Snyder, 2005; Benjamin et al., 2010; Dong et al., 2010; Ingleby, 2014). However, most of these studies exclude winds over complex terrain as representativeness errors can be significant. From its near-surface data assimilation assessment, Ingleby (2014) showed that biases and representativeness errors limit the global impact of near-surface wind observations. Winds from small islands, sub-grid scale headlands and tropical lands are still excluded from the UK Met Office data assimilation system. Similarly, the Rapid Update Cycle (RUC) system uses narrow c  2015 Royal Meteorological Society