Nowcasting hurricane properties by Principal Component Analysis (PCA) of Doppler velocity data

A novel approach based on Principal Component Analysis (PCA) of Doppler radar data establishes hurricane properties in real time. The 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 typically represent over 90% of the total variance in the data; one eigenvector from each pair is analyzed separately to estimate particular hurricane properties. These properties include the bearing and range to the hurricane's circulation center and the radius of maximum wind. Greater accuracy is achievable and fewer restrictions apply in comparison to other methods.

The PCA method was tested on the archive level II data of Hurricanes Erin (1995) and Bret (1999) recorded by the WSR-88D radar facilities at Mobile, Alabama (KMOB) and Corpus Christi, Texas (KCRP). In both cases, the similarity of the eigenvectors to their theoretical counterparts was striking even in the presence of significant missing data. Results obtained from several PPI scans of Hurricane Erin were in agreement with concurrent aircraft observations of the wind center corrected for the storm motion. The PCA results of a single PPI scan of Hurricane Bret agreed very well with the results of the GBVTD-simplex method. 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.