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.