Use of the advanced microwave sounding unit data to improve typhoon prediction

Abstract The effects of incorporating the advanced microwave sounding unit (AMSU-A) data with a modified Zhu–Zhang–Weng vortex-bogussing algorithm on typhoon prediction are examined through the use of the PSU/NCAR Mesoscale Model version 5 (MM5). The AMSU-A data contain the vertical distribution of the retrieved temperature from satellite brightness temperature, with the geopotential height and wind fields derived through a series of statistical and diagnostic calculations. The advantages of the modified vortex-bogussing algorithm include the incorporation of realistic asymmetric typhoon structures, the balanced dynamics with the background field, the easiness to implement and the efficient computations. To test the efficiency of this vortex-bogussing algorithm, the Typhoon Dan event in 1999 is simulated by incorporating the derived AMSU-A fields into the initial conditions of the MM5 modeling system. Results show significant improvements in the track and intensity of the storm, as compared to the simulation without the AMSU-A data. Therefore, this modified vortex-bogussing algorithm can be easily implemented on any typhoon modeling system, which will improve the real-time forecast of tropical cyclones.

[1]  Tong Zhu,et al.  Numerical Simulation of Hurricane Bonnie (1998). Part I: Eyewall Evolution and Intensity Changes , 2004 .

[2]  Xiaofan Li,et al.  Cloud microphysical processes associated with the diurnal variations of tropical convection: A 2D cloud resolving modeling study , 2006 .

[3]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[4]  G. Grell Prognostic evaluation of assumptions used by cumulus parameterizations , 1993 .

[5]  R. Rasmussen,et al.  Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model , 1998 .

[6]  Alan K. Betts,et al.  The Betts-Miller Scheme , 1993 .

[7]  Da‐Lin Zhang,et al.  Impact of the Advanced Microwave Sounding Unit Measurements on Hurricane Prediction , 2002 .

[8]  F. H. Hawkins,et al.  Hurricane Hilda, 1964 II. Structure and Budgets of the Hurricane on October 1, 1964 , 1968 .

[9]  M. Janssen Atmospheric Remote Sensing by Microwave Radiometry , 1993 .

[10]  Tong Zhu,et al.  Numerical Simulation of Hurricane Bonnie (1998). Part II: Sensitivity to Varying Cloud Microphysical Processes , 2006 .

[11]  G. Holland An Analytic Model of the Wind and Pressure Profiles in Hurricanes , 1980 .

[12]  H. Hawkins,et al.  HURRICANE HILDA, 1964: I. GENESIS, AS REVEALED BY SATELLITE PHOTOGRAPHS, CONVENTIONAL AND AIRCRAFT DATA , 1968 .

[13]  Noel E. Davidson,et al.  The BMRC High-Resolution Tropical Cyclone Prediction System: TC-LAPS , 2000 .

[14]  William T. Thompson,et al.  A vertically nested regional numerical weather prediction model with second-order closure physics , 1989 .

[15]  Lloyd J. Shapiro,et al.  The Response of Balanced Hurricanes to Local Sources of Heat and Momentum , 1982 .

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

[17]  Da‐Lin Zhang,et al.  A two-way interactive nesting procedure with variable terrain resolution , 1986 .

[18]  Kamal Puri,et al.  Tropical prediction using dynamical nudging, satellite-defined convective heat sources, and a cyclone bogus , 1992 .