The ecmwf operational implementation of four‐dimensional variational assimilation. III: Experimental results and diagnostics with operational configuration

The first two papers of this series describe the development of the operational four‐dimensional variational assimilation (4D‐Var) configuration implemented at the European Centre for Medium‐Range Weather Forecasts (ECMWF). The basic features are a 6‐hour incremental 4D‐Var set‐up with two minimization steps, using very simplified physics in the first minimization and a more complete set of linear physics in the second. This paper describes the validation of this configuration. Prior to implementation, 12 weeks of experimentation showed a consistent improvement relative to 3D‐Var. After an additional 6 weeks of encouraging parallel operation with the then current operational suite, 4D‐Var with physics was introduced in operations at ECMWF in November 1997. The difference in scores is statistically significant, and the fast‐growing components of the 4D‐Var analysis errors are shown to be smaller than their 3D‐Var counterparts. The performance of this new operational assimilation system is studied for the month of January 1998, for which the 4D‐Var analyses exhibit more realistic baroclinic waves than the 3D‐Var, especially in the Pacific area. A case‐study illustrates the improvement one can expect in forecast terms in the mid latitudes. The 4D‐Var system improved the forecast skill in the Tropics in general. Observing‐system experiments show that the current 4D‐Var operational system benefits from the assimilation both of satellite data and conventional observations.

[1]  Heikki Järvinen,et al.  Variational assimilation of time sequences of surface observations with serially correlated errors , 1999 .

[2]  R. Gelaro,et al.  Estimation of key analysis errors using the adjoint technique , 1998 .

[3]  Mats Hamrud,et al.  Impact of model resolution and ensemble size on the performance of an Ensemble Prediction System , 1998 .

[4]  P. Courtier,et al.  The ECMWF implementation of three‐dimensional variational assimilation (3D‐Var). II: Structure functions , 1998 .

[5]  P. Courtier,et al.  The ECMWF implementation of three‐dimensional variational assimilation (3D‐Var). I: Formulation , 1998 .

[6]  P. Courtier,et al.  Extended assimilation and forecast experiments with a four‐dimensional variational assimilation system , 1998 .

[7]  P. Courtier,et al.  A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .

[8]  Milija Zupanski,et al.  Regional Four-Dimensional Variational Data Assimilation in a Quasi-Operational Forecasting Environment , 1993 .

[9]  J. Derber,et al.  Variational Data Assimilation with an Adiabatic Version of the NMC Spectral Model , 1992 .

[10]  Philippe Courtier,et al.  Four‐Dimensional Assimilation In the Presence of Baroclinic Instability , 1992 .

[11]  P. Courtier,et al.  Four-dimensional variational data assimilation using the adjoint of a multilevel primitive-equation model , 1991 .

[12]  Claude Lemaréchal,et al.  Some numerical experiments with variable-storage quasi-Newton algorithms , 1989, Math. Program..

[13]  F. L. Dimet,et al.  Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects , 1986 .

[14]  J. M. Lewis,et al.  The use of adjoint equations to solve a variational adjustment problem with advective constraints , 1985 .

[15]  Philippe Courtier,et al.  The ECMWF implementation of three-dimensional variational assimilation ( 3 D-Var ) . 111 : Experimental results , 2006 .

[16]  Heikki Järvinen,et al.  Variational quality control , 1999 .

[17]  Ying-Hwa Kuo,et al.  Variational Assimilation of Precipitable Water Using a Nonhydrostatic Mesoscale Adjoint Model. Part I: Moisture Retrieval and Sensitivity Experiments , 1996 .

[18]  Philippe Courtier,et al.  Sensitivity of forecast errors to initial conditions , 1996 .