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Estimation and diagnosis of heterogeneous flow‐dependent background‐error covariances at the convective scale using either large or small ensembles
Author(s) -
Ménétrier Benjamin,
Montmerle Thibaut,
Berre Loïk,
Michel Yann
Publication year - 2014
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.2267
Subject(s) - data assimilation , scale (ratio) , sampling (signal processing) , statistics , environmental science , ensemble average , meteorology , mathematics , computer science , climatology , physics , filter (signal processing) , quantum mechanics , computer vision , geology
A 90 member ensemble assimilation experiment is run on a severe weather event for the convective‐scale Application de la Recherche à l'Opérationnel à Méso‐Echelle (model AROME‐France), coupled to a global 90 member Action de Recherche Petite Echelle Grande Echelle (ARPEGE) ensemble. Background‐error covariances are diagnosed for this period, in order to examine flow‐dependent features, sampling noise effects associated with six‐member estimates (corresponding to the size of a preliminary research AROME ensemble) and a comparison between AROME and ARPEGE estimates. The dependence of error covariances on the weather situation is illustrated for low‐level specific humidity in particular, which is associated, for example, with large variance values over the Mediterranean area. While this large‐scale contrast is visible in both AROME and ARPEGE ensembles, differences between the two ensembles are also apparent as expected, for instance due to resolution differences. The effects of sampling noise are investigated by comparing independent six‐member and 84 member estimates of variances and correlation length‐scales. While the six‐member ensemble is able to capture some of the signal of interest, spatial filtering techniques seem to be useful in order to reduce estimation errors.