Hurricane properties by principal component analysis of Doppler radar data

A novel approach based on Principal Component Analysis (PCA) of Doppler radar data establishes hurricane properties such as the positions of the circulation centre and wind maxima. The method was developed in conjunction with a new Doppler radar wind model for both mature and weak hurricanes.;The tangential wind (Vt) is modeled according to Vtzetax = constant, where zeta is the radius, and x is an exponent. The maximum Vt occurs at the Radius of Maximum Wind (RMW). For the mature (weak) hurricane case, x = 1 ( x < 1) within the RMW, and x = 0.5 ( x = 0) beyond the RMW. The radial wind is modeled in a similar fashion in the radial direction with up to two transition radii but it is also varied linearly in the vertical direction. This is the first Doppler radar wind model to account for the vertical variations in the radial wind.;The new method employs an S2-mode PCA on the Doppler velocity data taken from a single PPI scan and arranged sequentially in a matrix according to their azimuth and range coordinates. The first two eigenvectors of both the range and azimuth eigenspaces represent over 95% of the total variance in the modeled data; one eigenvector from each pair is analyzed separately to estimate particular hurricane properties. These include the bearing and range to the hurricane's circulation centre, the RMW, and the transition radii of the radial wind. Model results suggest that greater accuracy is achievable and fewer restrictions apply in comparison to other methods.;The PCA method was tested on the Doppler velocity data of Hurricane Erin (1995) and Typhoon Alex (1987). In both cases, the similarity of the eigenvectors to their theoretical counterparts was striking even in the presence of significant missing data. Results from Hurricane Erin were in agreement with concurrent aircraft observations of the wind centre corrected for the storm motion. Such information was not available for Typhoon Alex, however, the results agreed with those from other methods. The success of these and future tests should vindicate the PCA method as a valuable tool for the diagnosis and subsequent forecasting of hurricane properties.