The ECMWF implementation of three‐dimensional variational assimilation (3D‐Var). III: Experimental results

In this third and final paper of a series, we assess the performance of the three‐dimensional variational data assimilation scheme, in the light of the results from the extensive pre‐operational programme of numerical experimentation. Its performance is compared with that of the previous operational scheme at the European Centre for Medium‐Range Weather Forecasts, which was based on Optimal Interpolation. The main features of the new scheme are illustrated, in particular the effects of non‐separable structure functions and the improved data usage. TIROS‐N Operational Vertical Sounder cloud‐cleared radiances, for example, are used directly without a separate retrieval step. Scatterometer data are assimilated in the form of ambiguous winds with the ambiguity removal taking place within the analysis itself. Problems encountered during the tests are discussed and the solutions implemented are explained.

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

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

[3]  Roger Saunders,et al.  Near-Surface Satellite Wind Observations of Hurricanes and Their Impact on ECMWF Model Analyses and Forecasts , 1998 .

[4]  B. Knudsen Accuracy of Arctic stratospheric temperature analyses and the implications for the prediction of polar stratospheric clouds , 1996 .

[5]  Roger Daley,et al.  Generation of Global Multivariate Error Covariances by Singular-Value Decomposition of the Linear Balance Equation , 1996 .

[6]  Michèle Vesperini,et al.  Variational analysis of humidity information from TOVS radiances , 1996 .

[7]  A. Simmons,et al.  Implementation of the Semi-Lagrangian Method in a High-Resolution Version of the ECMWF Forecast Model , 1995 .

[8]  M. Donelan,et al.  Dynamics and Modelling of Ocean Waves , 1994 .

[9]  Philippe Courtier,et al.  Use of cloud‐cleared radiances in three/four‐dimensional variational data assimilation , 1994 .

[10]  M. Tiedtke,et al.  Representation of Clouds in Large-Scale Models , 1993 .

[11]  J. R. Eyre,et al.  Assimilation of TOVS radiance information through one-dimensional variational analysis , 1993 .

[12]  N. B. Ingleby,et al.  Bayesian quality control using multivariate normal distributions , 1993 .

[13]  John Derber,et al.  The National Meteorological Center's spectral-statistical interpolation analysis system , 1992 .

[14]  N. B. Ingleby,et al.  Treatment of Gross Errors Using Maximum Probability Theory , 1992 .

[15]  A. Hollingsworth,et al.  Global Observing System Experiments on Operational Statistical Retrievals of Satellite Sounding Data , 1991 .

[16]  P. Undén Tropical data assimilation and analysis of divergence , 1989 .

[17]  A. Hollingsworth,et al.  Data Assimilation: the 1984/85 Revisions of the Ecmwf Mass and Wind Analysis , 1987 .

[18]  Andrew C. Lorenc,et al.  Analysis methods for numerical weather prediction , 1986 .

[19]  Norman A. Phillips,et al.  The spatial statistics of random geostrophic modes and first-guess errors , 1986 .

[20]  C. Dean,et al.  Evaluation of a New Operational Technique for Producing Clear Radiances , 1982 .

[21]  A. Lorenc A Global Three-Dimensional Multivariate Statistical Interpolation Scheme , 1981 .

[22]  F. Lott,et al.  A new subgrid‐scale orographic drag parametrization: Its formulation and testing , 1997 .

[23]  Ad Stoffelen,et al.  Ambiguity removal and assimilation of scatterometer data , 1997 .

[24]  Philippe Courtier,et al.  Dynamical structure functions in a four‐dimensional variational assimilation: A case study , 1996 .

[25]  L. Phalippou,et al.  Variational retrieval of humidity profile, wind speed and cloud liquid‐water path with the SSM/I: Potential for numerical weather prediction , 1996 .

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

[27]  Hannu Savijärvi,et al.  Error Growth in a Large Numerical Forecast System , 1995 .

[28]  Andrew C. Lorenc,et al.  Objective quality control of observations using Bayesian methods. Theory, and a practical implementation , 1988 .