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- W4322010419 abstract "Ensemble and hybrid ensemble-variational Data Assimilation (DA) methods incorporating ensemble-based flow-dependent error statistics into state estimation have emerged in recent decades. In a hybrid DA, the background error covariances are a combination of ensemble covariances and static climatology. The ensemble component provides flow-dependency and non-linear error growth critical for convective-scale models, and the static climatology mitigates the effects of a small ensemble size. Hybrid ensemble variational DA methods were recently implemented in the convective-scale NWP model AROME at Meteo-France.We present our findings from testing Hybrid-3-Dimensional Variational Data Assimilation in convective-scale NWP model AROME over Austria. Given Austria's unique alpine orography, we investigate the impact of applying different weighting to flow-dependent covariances in hybrid DA for a summertime convection case over central Europe. In addition to the hybrid weights, we explore optimal ensemble size, the increase of ensemble size with a time-lagged approach as well as suitable localization settings. Finally, we compare our results to the 3-dimensional variational data assimilation (3DVar) operational model forecast of GeoSphere Austria and discuss the potential benefits, drawbacks, and challenges of using hybrid DA over traditional 3DVar.Keywords: summertime convection; hybrid-3DEnVar; AROME NWP model; flow dependent background error covariance" @default.
- W4322010419 created "2023-02-26" @default.
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- W4322010419 date "2023-05-15" @default.
- W4322010419 modified "2023-09-29" @default.
- W4322010419 title "Testing the AROME Hybrid 3DEnVar for convective-scale NWP over Austria" @default.
- W4322010419 doi "https://doi.org/10.5194/egusphere-egu23-12636" @default.
- W4322010419 hasPublicationYear "2023" @default.
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