Towards Full Flow‐Dependence: New Temporally Varying EDA Quotient Functionality to Estimate Background Errors in CERRA

Journal of Advances in Modeling Earth Systems

A. El‐Said¹  P. Brousseau¹  R. Randriamampianina²  M. Ridal³ 

[1] Metéo‐France CNRM‐GMAP Toulouse France;Norwegian Meteorological Institute Oslo Norway;Swedish Meteorological and Hydrological Institute Norrköping Sweden;

关键词: background error estimation;    B‐matrix;    data assimilation;    EDA;    reanalysis;   

DOI  :  10.1029/2021MS002637

来源: DOAJ

【 摘 要 】

Abstract A new temporally evolving quotient on the Ensemble of Data Assimilations (EDA) technique for estimating background error covariances has been developed for the Copernicus European Regional Re‐Analysis (CERRA). The B‐matrix is modeled on a bi‐Fourier limited area model. Background errors are assumed isotropic, homogeneous and non‐separable. Linearized geostrophic and hydrostatic balances are incorporated as multivariate relationships, coupling vorticity, and geopotential extended to mass‐wind and specific humidity fields via the f‐plane approximation. The EDA comprises two main pools: seasonal and daily. The seasonal component comprises winter and summer EDA forecast differences at reanalysis resolution (5.5 km). The new time quotient function temporally changes the mixture of differences from each season, to make up the seasonal component. The daily component is an 11 km moving 2.5 days average changing in real‐time. Subsequent B‐matrix computation sees the ingestion of forecast differences from both components, with a fixed split of 80%–20% seasonal‐daily, every 2 days. The sourcing of these forecast differences from both seasonal and daily sources is in continuous temporal flux therefore. We consider a case study to illustrate the potential of estimating weather regime change using CERRA‐EDA with varying proportions of seasonal‐daily mixing, while including settings used for CERRA production. Our case study shows that the most influential factors are differences in observation networks between the given years, their spatial distribution across the CERRA domain, and the proportion of seasonal‐daily split. It is shown that our method provides improvement over a static B‐matrix.

【 授权许可】

Unknown