Performance measurement with advanced diagnostic tools of all‐sky microwave imager radiances in 4D‐Var

In this paper, a comprehensive assessment of the impact of all-sky radiance observations in the operational European Centre for Medium-Range Weather Forecasts' assimilation and forecast system is presented using advanced diagnostic tools. In particular, the observation influence in the assimilation process and the related contribution to the short-range forecast error of radiance observations from microwave sensors is evaluated with recently developed diagnostic tools based on the adjoint version of the model. Recent operational changes to the assimilation of these observations result in a more beneficial impact on the initial condition and the short-range forecast. The largest information content is obtained for observations that are located in clear-sky regions as indicated by the model short-range forecast and the observations themselves. However, the largest decrease in the forecast error is provided by observations detected in cloudy regions. It is shown that the use of Special Sensor Microwave/Imager data helps to reduce model systematic errors in the central eastern equatorial Pacific, where the intertropical convergence zone is located, and the Arabian Sea, where the monsoon season occurs. Copyright © 2011 Royal Meteorological Society

[1]  Roberto Buizza,et al.  Sensitivity Analysis of Forecast Errors and the Construction of Optimal Perturbations Using Singular Vectors , 1998 .

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

[3]  R. Errico.  Interpretations of an adjoint-derived observational impact measure , 2007 .

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

[5]  Dacian N. Daescu,et al.  Adjoint sensitivity of the model forecast to data assimilation system error covariance parameters , 2010 .

[6]  Olivier Talagrand,et al.  Assimilation of Observations, an Introduction (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice) , 1997 .

[7]  Florence Rabier,et al.  Properties and first application of an error‐statistics tuning method in variational assimilation , 2004 .

[8]  A. Simmons,et al.  The ECMWF operational implementation of four‐dimensional variational assimilation. I: Experimental results with simplified physics , 2007 .

[9]  Roberto Buizza,et al.  Tropical singular vectors computed with linearized diabatic physics , 2001 .

[10]  Philippe Lopez,et al.  A convection scheme for data assimilation: Description and initial tests , 2005 .

[11]  M. Rodwell,et al.  The ECMWF `Diagnostics Explorer: A web tool to aid forecast system assessment and development , 2008 .

[12]  Jean-Jacques Morcrette,et al.  Linearized radiation and cloud schemes in the ECMWF model: Development and evaluation , 2002 .

[13]  Gérald Desroziers,et al.  Diagnosis and adaptive tuning of observation‐error parameters in a variational assimilation , 2001 .

[14]  H.-L. Huang,et al.  Estimating effective data density in a satellite retrieval or an objective analysis , 1993 .

[15]  O. Talagrand,et al.  Diagnosis and tuning of observational error in a quasi‐operational data assimilation setting , 2006 .

[16]  Peter Bauer,et al.  Direct 4D‐Var assimilation of all‐sky radiances. Part I: Implementation , 2010 .

[17]  R. Gelaro,et al.  Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System , 2008 .

[18]  D. Daescu.  On the Sensitivity Equations of Four-Dimensional Variational (4D-Var) Data Assimilation , 2008 .

[19]  Roger Daley,et al.  Observation and background adjoint sensitivity in the adaptive observation‐targeting problem , 2007 .

[20]  Keiji Imaoka,et al.  The Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), NASDA's contribution to the EOS for global energy and water cycle studies , 2003, IEEE Trans. Geosci. Remote. Sens..

[21]  Erik Andersson,et al.  Influence‐matrix diagnostic of a data assimilation system , 2004 .

[22]  C. Cardinali.  Monitoring the observation impact on the short‐range forecast , 2009 .

[23]  P. Bauer,et al.  Direct 4D‐Var assimilation of all‐sky radiances. Part II: Assessment , 2010 .

[24]  James P. Hollinger,et al.  SSM/I instrument evaluation , 1990 .

[25]  Rolf H. Langland,et al.  Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system , 2004 .