Dynamic initialization by repeated insertion of data

In this study we isolate and assess the impact of dynamic initialization by repeated insertion of data (RI), also known as ‘nudging’. A novel control experiment compares this iterative technique with an alternative intermittent analysis-forecast cycle (AF) framework within the same assimilation system. Both schemes are versions of the operational global data assimilation system for numerical weather prediction at the Meteorological Office. A secondary issue is the impact of divergence damping as an extra initialization measure. The effectiveness of RI for control of high-frequency gravity waves is demonstrated, together with its beneficial impact on the short-period forecast of surface pressure. Changes to analysed fields due to RI are only a small fraction of the changes due to model evolution in six hours. RI is found to enhance upper-level divergence in the equatorial west Pacific and the problem of spin-up of the global mean precipitation is virtually absent, except when divergence damping is imposed during assimilation and removed during the forecast. The main benefit from divergence damping is a more accurate tropical wind field. RI is found to degrade the short-period forecast of the wind field in the basic system studied, and the extra errors are largest in strong upper winds at mid latitudes. This fact is discussed in the context of an earlier idealized study of RI. Analysis constraints are a primary source of difference between various ‘nudging’ schemes used in numerical weather prediction. The sensitivity of the impact from RI to these is evaluated by relaxing constraints in the basic system. The result emerges that as the analysis step of a nudging scheme becomes better balanced, the scope for improvement by using RI for initialization becomes smaller. Also, if the better-balanced analysis step entails stronger enforcement of non-divergence on the wind increments, then the damaging effect of RI on the wind field through advection becomes larger and can outweigh the benefit through initialization. A scheme combining RI for mass data with an iterative AF method for wind data retains much of the benefit and avoids most of the problems in the scheme with RI of all data. This suggests a method of improving the contribution of an RI structure to the overall performance of an assimilation scheme based on the nudging approach.

[1]  P. Bergthórsson,et al.  Numerical Weather Map Analysis , 1955 .

[2]  G. P. Cressman.  AN OPERATIONAL OBJECTIVE ANALYSIS SYSTEM , 1959 .

[3]  O. Talagrand,et al.  Dynamic approach to meteorological data assimilation , 1974 .

[4]  J. Hoke,et al.  The Initialization of Numerical Models by a Dynamic-Initialization Technique , 1976 .

[5]  H. Davies,et al.  Updating prediction models by dynamical relaxation - An examination of the technique. [for numerical weather forecasting] , 1977 .

[6]  C. Dey.  Noise Suppression in a Primitive Equation Prediction Model , 1978 .

[7]  R. Daley.  Normal mode initialization , 1981 .

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

[9]  W. Bourke,et al.  A Scheme to Retain the Hadley Circulation During Nonlinear Normal Mode Initialization , 1982 .

[10]  W. Bourke,et al.  Incremental Linear Normal Mode Initialization in Four-Dimensional Data Assimilation , 1982 .

[11]  Takeo Kitade,et al.  Nonlinear Normal Mode Initialization with Physics , 1983 .

[12]  Ronald M. Errico,et al.  Convergence Properties of Machenhauer's Initialization Scheme , 1983 .

[13]  Philip J. Rasch.  Developments in Normal Mode Initialization. Part II: A New Method and its Comparison with Currently Used Schemes , 1985 .

[14]  A. Hollingsworth,et al.  Monitoring of observation and analysis quality by a data assimilation system , 1986 .

[15]  Mohan K. Ramamurthy,et al.  Four-Dimensional Data Assimilation in the Monsoon Region. Part I: Experiments with Wind Data , 1987 .

[16]  Wei Wang,et al.  Use of Four-Dimensional Data Assimilation by Newtonian Relaxation and Latent-Heat Forcing to Improve a Mesoscale-Model Precipitation Forecast: A Case Study , 1988 .

[17]  T. N. Krishnamurti,et al.  Reduction of the Spinup Time for Evaporation and Precipitation in a Spectral Model , 1988 .

[18]  Philip J. Rasch,et al.  A comparison of various normal-mode initialization schemes and the inclusion of diabatic processes , 1988 .

[19]  Mohan K. Ramamurthy,et al.  Four-Dimensional Data Assimilation in the Monsoon Region. Part II: Role of Temperature and Moisture Data , 1988 .

[20]  Philip J. Rasch,et al.  Cumulus Initialization in a Global Model for Numerical Weather Prediction , 1989 .

[21]  John Derber,et al.  A Global Oceanic Data Assimilation System , 1989 .

[22]  W. Wergen.  Normal mode initialization and atmospheric tides , 1989 .

[23]  R. Errico.  The Forcing of Gravitational Normal Modes by Condensational Heating , 1989 .

[24]  S. Bunker,et al.  A Numerical Model Study of Nocturnal Drainage Flows with Strong Wind and Temperature Gradients , 1989 .

[25]  R. Petersen,et al.  A Synoptic Evaluation of Normal Mode Initialization Experiments with the NMC Nested Grid Model , 1989 .

[26]  Ying-Hwa Kuo,et al.  Dynamic Initialization Using Observations from a Hypothetical Network of Profilers , 1989 .

[27]  K. Puri,et al.  The Use of Satellite Data in the Specification of Convective Heating for Diabatic Initialization and Moisture Adjustment in Numerical Weather Prediction Models , 1990 .

[28]  D. Stauffer,et al.  Use of Four-Dimensional Data Assimilation in a Limited-Area Mesoscale Model. Part I: Experiments with Synoptic-Scale Data , 1990 .

[29]  John Manobianco,et al.  The use of successive dynamic initialization by nudging to simulate cyclogenesis during GALE IOP 1 , 1991 .

[30]  R. S. Bell,et al.  The Meteorological Office analysis correction data assimilation scheme , 1991 .