Assimilation of TOVS radiance information through one-dimensional variational analysis

In recent years difficulties have been experienced in exploiting satellite sounding data in numerical weather prediction (NWP) in the form of independently retrieved temperature and humidity profiles. Attention has now focused on methods through which the information in the radiance measurements may be assimilated more directly into the NWP system%. A scheme known as ‘one-dimensional variational analysis’ (1DVAR) has been developed at the European Centre for Medium-range Weather Forecasts as a method for extracting information from TIROS Operational Vertical Sounder radiances for use in the operational data-assimilation system. The 1DVAR scheme is based on variational principles applied to the analysis of the atmospheric profile at a single location, using a forecast profile and its error covariance as constraints. The details of the scheme are presented. Errors in 1DVAR products are correlated with those of the short-range forecast which serves as a background for the subsequent three-dimensional analysis. Methods for addressing this aspect of the assimilation problem are discussed. The characteristics of 1DVAR products and their impact on the analysis are described. A series of forecast impact experiments has been conducted and has demonstrated consistent positive impacts on forecast skill in the northern hemisphere.

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

[2]  Lennart Bengtsson.  Medium-Range Forecasting at the ECMWF , 1985 .

[3]  Jean-Noël Thépaut,et al.  Variational inversion of simulated TOVS radiances using the adjoint technique , 1990 .

[4]  J. R. Eyre,et al.  Inversion of cloudy satellite sounding radiances by nonlinear optimal estimation. I: Theory and simulation for TOVS , 1989 .

[5]  J. R. Eyre,et al.  The information content of data from satellite sounding systems: A simulation study , 1990 .

[6]  J. Eyre.  On systematic and their climatological mean values , 1987 .

[7]  K. Kashiwagi.  On the Impact of Sapce-based Observing Systems in the JMA Global Forecast/Analysis System , 1987 .

[8]  A. Hollingsworth,et al.  Quality Control of Operational Physical Retrievals of Satellite Sounding Data , 1991 .

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

[10]  E. Kalnay,et al.  An Assessment of the FGGE Satellite Observing System during SOP-1 , 1982 .

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

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

[13]  C. Rodgers,et al.  Retrieval of atmospheric temperature and composition from remote measurements of thermal radiation , 1976 .

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

[15]  Daniel L. Smith.  Eighty-Five Percent and Holding—A Limit to Forecast Accuracy? , 1979 .

[16]  A. Hollingsworth,et al.  Global Data Assimilation and Forecast Experiments Using the Improved Initialization Inversion Method for Satellite Soundings , 1991 .