The ECMWF implementation of three-dimensional variational assimilation ( 3 D-Var ) . 111 : Experimental results

SUMMARY 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. The overall impact on forecast accuracy in the troposphere of the northern hemisphere extratropics is neutral for geopotential and positive for wind and temperature. The impact is neutral in the tropics, and significantly positive in the southern hemisphere. Analyses and forecasts for the stratosphere have improved in all regions. Other positive results include a clear improvement in analyses of near-surface winds over oceans, particularly in the vicinity of tropical storms. This is predominantly because of the assimilation of scatterometer wind-data.

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

[2]  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 .

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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