Operational implementation of a hybrid ensemble/4D‐Var global data assimilation system at the Met Office

TLDR

The study develops and operationally tests a hybrid ensemble/4D‑Var global data assimilation system at the Met Office, achieving an average RMS error reduction of just under 1%. The system employs an extended control variable technique to blend climatological covariance with a 23‑member ensemble, and incorporates a horizontal anti‑aliasing filter, a vertical localization based on a modified stream‑function covariance, and inflation of the climatological covariance to preserve observation fit. Development results show the hybrid yields a larger improvement in 3D‑Var than 4D‑Var, benefits from anti‑aliasing and vertical localization, is largely insensitive to full coupling, and its capture of coherent “Errors of the Day” is key to forecast skill gains, though tropical verification is poorer against own analyses but consistent with ECMWF results.

Abstract

Abstract We describe the development and testing of the hybrid ensemble/4D‐Var global data assimilation system that was implemented operationally at the Met Office in July 2011, giving an average reduction of RMS errors of just under 1%. The scheme uses the extended control variable technique to implement a hybrid background error covariance that combines the standard climatological covariance with a covariance derived from the 23‐member operational ensemble MOGREPS‐G. Unique features of the Met Office scheme include application of a horizontal ‘anti‐aliasing’ filter to the ensemble error modes, a vertical localization scheme based uniquely on a modification of the climatological stream function covariance, and inflation of the climatological covariance to maintain the analysis fit to observations. Findings during development include a significantly greater impact of the scheme in 3D‐Var than 4D‐Var, a clear positive impact from the combination of the anti‐aliasing filter and vertical localization, and a relatively small sensitivity to full coupling of the ensemble and 4D‐Var systems. Supplementary experiments suggest that the ability of the ensemble to capture coherent ‘Errors of the Day’ is key to the improvements in forecast skill. A particular problem encountered during development was significantly poorer tropical verification scores when measured against own analyses. In contrast, verification against independent (ECMWF) analyses gave scores that were much more consistent with those against observations.