Beschreibung
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.
| Zeitraum | 2023 |
|---|---|
| Ereignistitel | EGU, General Assembly 2023 |
| Veranstaltungstyp | Konferenz |
| Ort | Wien, ÖsterreichAuf Karte anzeigen |
| Bekanntheitsgrad | International |