A tropical cyclone bogus data assimilation scheme in the MM5 3D-Var system and numerical experiments with typhoon rusa (2002) near landfall

A typhoon bogus data assimilation (BDA) scheme is built in the MM5 3D-Var system with mainly three components: a) An algorithm to specify the typhoon bogus sea-level pressure (SLP) and wind profiles is constructed in the MM5 3D-Var system based on the typhoon report issued by the regional typhoon center and the 3D-Var background; b) The errors of the bogus observations are empirically assigned according to our knowledge of the typhoon SLP and wind distributions; c) The MM5 3D-Var system includes typhoon bogus observation operators. Since typhoon bogus observations can have large differences from the 3D-Var background fields, special treatment is made to allow the typhoon bogus data retained in the minimization procedure. Numerical experiments are conducted using Typhoon Rusa (2002) case in the Northwestern Pacific Ocean near landfall to the Korean Peninsula. It is indicated that the 3D-Var bogus algorithm works well and can improve the prediction of typhoon track and intensity. With MM5 3D-Var BDA, the initial typhoon vortex is balanced with the dynamical and statistical balance embedded in the 3D-Var system. It can generate a well-defined warm core structure in the typhoon initialization. Compared with bogussing in the 3D-Var background fields, MM5 3D-Var BDA results in less spin-down/up problems in the subsequent typhoon forecast. Sensitivity experiments show that assimilation of the only bogus SLP data produces too strong a typhoon intensity, while assimilation of the only bogus wind data produces much weaker intensity prediction than the observations. Assimilation of both bogus SLP and wind data obtains the best initialization and prediction for this typhoon case. Its landfall location, time and intensity are very close to the observation in the experiment.

[1]  Zhaoxia Pu,et al.  Evaluation of Bogus Vortex Techniques with Four-Dimensional Variational Data Assimilation , 2001 .

[2]  Philippe Courtier,et al.  Unified Notation for Data Assimilation : Operational, Sequential and Variational , 1997 .

[3]  X. Zou,et al.  A Numerical Study of the Effect of GOES Sounder Cloud-Cleared Brightness Temperatures on the Prediction of Hurricane Felix , 2001 .

[4]  Yubao Liu,et al.  A Multiscale Numerical Study of Hurricane Andrew (1992). Part I: Explicit Simulation and Verification , 1997 .

[5]  Wang Bin,et al.  Initialization and simulation of a typhoon using 4-dimensional variational data assimilation — Research on typhoon Herb (1996) , 2003 .

[6]  Y. Kurihara,et al.  An Initialization Scheme of Hurricane Models by Vortex Specification , 1993 .

[7]  Anthony Hollingsworth,et al.  The statistical structure of short-range forecast errors as determined from radiosonde data , 1986 .

[8]  Tetsuya Theodore. Fujita,et al.  Pressure distribution within typhoon , 1952 .

[9]  Yoshio Kurihara,et al.  Improvements in Tropical Cyclone Track and Intensity Forecasts Using the GFDL Initialization System , 1993 .

[10]  Wei Huang,et al.  A Three-Dimensional Variational Data Assimilation System for MM5: Implementation and Initial Results , 2004 .

[11]  Yoshio Kurihara,et al.  Improvements in the GFDL Hurricane Prediction System , 1995 .

[12]  M. Ueno.  A study on the impact of asymmetric components around tropical cyclone center on the accuracy of bogus data and the track forecast , 1995 .

[13]  X. Zou,et al.  Studies on the Initialization and Simulation of a Mature Hurricane Using a Variational Bogus Data Assimilation Scheme , 2000 .

[14]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

[15]  X. Zou,et al.  Initialization and Simulation of a Landfalling Hurricane Using a Variational Bogus Data Assimilation Scheme , 2000 .

[16]  J. Derber,et al.  Variational Data Assimilation with an Adiabatic Version of the NMC Spectral Model , 1992 .

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

[18]  Lance M. Leslie,et al.  On the bogussing of tropical cyclones in numerical models: A comparison of vortex profiles , 1995 .

[19]  A. Suh,et al.  GFDL-Type Typhoon Initialization in MM5 , 2002 .

[20]  P. Courtier,et al.  A strategy for operational implementation of 4D‐Var, using an incremental approach , 1994 .

[21]  N. B. Ingleby,et al.  The Met. Office global three‐dimensional variational data assimilation scheme , 2000 .

[22]  T. N. Krishnamurti,et al.  Vortex initialization for typhoon track prediction , 1992 .