hybrid ensemble 4dvar assimilation of stratospheric ozone using a global shallow water model
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2016
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Abstract
Wind extraction from stratospheric ozone (O3)
assimilation is examined using a hybrid ensemble 4-D
variational assimilation (4DVar) shallow water model
(SWM) system coupled to the tracer advection equation. Stratospheric
radiance observations are simulated using global observations of the SWM
fluid height (Z), while O3 observations represent sampling by a typical
polar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100,
and 1518 members), with the largest ensemble equal to the number of
dynamical state variables. The optimal length scale for ensemble
localization was found by tuning an ensemble Kalman filter (EnKF). This
scale was then used for localizing the ensemble covariances that were
blended with conventional covariances in the hybrid 4DVar experiments. Both
optimal length scale and optimal blending coefficient increase with ensemble
size, with optimal blending coefficients varying from 0.2–0.5 for small
ensembles to 0.5–1.0 for large ensembles. The hybrid system outperforms
conventional 4DVar for all ensemble sizes, while for large ensembles the
hybrid produces similar results to the offline EnKF. Assimilating O3 in
addition to Z benefits the winds in the hybrid system, with the fractional
improvement in global vector wind increasing from ∼ 35 %
with 25 and 50 members to ∼ 50 % with 1518 members. For the
smallest ensembles (25 and 50 members), the hybrid 4DVar assimilation
improves the zonal wind analysis over conventional 4DVar in the Northern
Hemisphere (winter-like) region and also at the Equator, where Z observations
alone have difficulty constraining winds due to lack of geostrophy. For
larger ensembles (100 and 1518 members), the hybrid system results in both
zonal and meridional wind error reductions, relative to 4DVar, across the
globe.
| Reference Key |
allen2016atmospherichybrid
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|---|---|
| Authors | ;D. R. Allen;K. W. Hoppel;D. D. Kuhl |
| Journal | Journal of agricultural and food chemistry |
| Year | 2016 |
| DOI |
10.5194/acp-16-8193-2016
|
| URL | |
| Keywords |
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