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Wind extraction from stratospheric ozone (O3)assimilation is examined using a hybrid ensemble 4-Dvariational assimilation (4DVar) shallow water model(SWM) system coupled to the tracer advection equation. Stratosphericradiance observations are simulated using global observations of the SWMfluid height (Z), while O3 observations represent sampling by a typicalpolar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100,and 1518 members), with the largest ensemble equal to the number ofdynamical state variables. The optimal length scale for ensemblelocalization was found by tuning an ensemble Kalman filter (EnKF). Thisscale was then used for localizing the ensemble covariances that wereblended with conventional covariances in the hybrid 4DVar experiments. Bothoptimal length scale and optimal blending coefficient increase with ensemblesize, with optimal blending coefficients varying from 0.2–0.5 for smallensembles to 0.5–1.0 for large ensembles. The hybrid system outperformsconventional 4DVar for all ensemble sizes, while for large ensembles thehybrid produces similar results to the offline EnKF. Assimilating O3 inaddition to Z benefits the winds in the hybrid system, with the fractionalimprovement in global vector wind increasing from  ∼  35 %with 25 and 50 members to  ∼  50 % with 1518 members. For thesmallest ensembles (25 and 50 members), the hybrid 4DVar assimilationimproves the zonal wind analysis over conventional 4DVar in the NorthernHemisphere (winter-like) region and also at the Equator, where Z observationsalone have difficulty constraining winds due to lack of geostrophy. Forlarger ensembles (100 and 1518 members), the hybrid system results in bothzonal and meridional wind error reductions, relative to 4DVar, across theglobe

Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model