Objective Estimation of Tropical Cyclone Wind Structure from Infrared Satellite Data

Abstract Geostationary infrared (IR) satellite data are used to provide estimates of the symmetric and total low-level wind fields in tropical cyclones, constructed from estimations of an azimuthally averaged radius of maximum wind (RMAX), a symmetric tangential wind speed at a radius of 182 km (V182), a storm motion vector, and the maximum intensity (VMAX). The algorithm is derived using geostationary IR data from 405 cases from 87 tropical systems in the Atlantic and east Pacific Ocean basins during the 1995–2003 hurricane seasons that had corresponding aircraft data available. The algorithm is tested on 50 cases from seven tropical storms and hurricanes during the 2004 season. Aircraft-reconnaissance-measured RMAX and V182 are used as dependent variables in a multiple linear regression technique, and VMAX and the storm motion vector are estimated using conventional methods. Estimates of RMAX and V182 exhibit mean absolute errors (MAEs) of 27.3 km and 6.5 kt, respectively, for the dependent samples. A m...

[1]  Charles R. Sampson,et al.  Real-Time Internet Distribution of Satellite Products for Tropical Cyclone Reconnaissance. , 2001 .

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

[3]  Christopher S. Velden,et al.  Satellite-Based Tropical Cyclone Intensity Estimation Using the NOAA-KLM Series Advanced Microwave Sounding Unit (AMSU) , 2003 .

[4]  Mark D. Powell,et al.  The HRD real-time hurricane wind analysis system , 1998 .

[5]  V. F. Dvorak Tropical cyclone intensity analysis using satellite data , 1984 .

[6]  W. Paul Menzel,et al.  The Impact of Satellite-derived Winds on Numerical Hurricane Track Forecasting , 1992 .

[7]  V. F. Dvorak Tropical Cyclone Intensity Analysis and Forecasting from Satellite Imagery , 1975 .

[8]  Donald W. Hillger,et al.  Detection of Important Atmospheric and Surface Features by Employing Principal Component Image Transformation of GOES Imagery , 2003 .

[9]  Christopher S. Velden,et al.  Application of surface-adjusted GOES low-level cloud-drift winds in the environment of Atlantic tropical cyclones. Part I: Methodology and validation , 2002 .

[10]  William Carlisle Thacker,et al.  Fitting models to inadequate data by enforcing spatial and temporal smoothness , 1988 .

[11]  Christopher S. Velden,et al.  Development of an Objective Scheme to Estimate Tropical Cyclone Intensity from Digital Geostationary Satellite Infrared Imagery , 1998 .

[12]  John A. Knaff,et al.  NOTES AND CORRESPONDENCE Improvement of Advanced Microwave Sounding Unit Tropical Cyclone Intensity and Size Estimation Algorithms , 2006 .

[13]  J. Franklin,et al.  GPS Dropwindsonde Wind Profiles in Hurricanes and Their Operational Implications , 2003 .

[14]  Mark DeMaria,et al.  Optimization of a Hurricane Track Forecast Model with the Adjoint Model Equations , 1993 .

[15]  Roy W. Spencer,et al.  Atlantic Tropical Cyclone Monitoring with AMSU-A: Estimation of Maximum Sustained Wind Speeds , 2001 .