A geo‐statistical observation operator for the assimilation of near‐surface wind data

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.