Multivariate Analysis of MODerate Resolution Imaging Spectroradiometer (MODIS) Aerosol Retrievals and the Statistical Hurricane Intensity Prediction Scheme (SHIPS) Parameters for Atlantic Hurricanes

• Main Authors: Mohammed M. Kamal, Ruixin Yang, John J. Qu
• Format: Article
• Language: English
• Published: MDPI AG 2012-09-01
• Series: Remote Sensing
• Subjects: Atlantic hurricane; dust aerosol; humidity; optical depth; MODIS; SHIPS; intensity change
• Online Access: http://www.mdpi.com/2072-4292/4/9/2846

MODerate Resolution Imaging Spectroradiometer (MODIS) aerosol retrievals over the North Atlantic spanning seven hurricane seasons are combined with the Statistical Hurricane Intensity Prediction Scheme (SHIPS) parameters. The difference between the current and future intensity changes were selected as response variables. For 24 major hurricanes (category 3, 4 and 5) between 2003 and 2009, eight lead time response variables were determined to be between 6 and 48 h. By combining MODIS and SHIPS data, 56 variables were compiled and selected as predictors for this study. Variable reduction from 56 to 31 was performed in two steps; the first step was via correlation coefficients (cc) followed by Principal Component Analysis (PCA) extraction techniques. The PCA reduced 31 variables to 20. Five categories were established based on the PCA group variables exhibiting similar physical phenomena. Average aerosol retrievals from MODIS Level 2 data in the vicinity of UTC 1,200 and 1,800 h were mapped to the SHIPS parameters to perform Multiple Linear Regression (MLR) between each response variable against six sets of predictors of 31, 30, 28, 27, 23 and 20 variables. The deviation among the predictors Root Mean Square Error (RMSE) varied between 0.01 through 0.05 and, therefore, implied that reducing the number of variables did not change the core physical information. Even when the parameters are reduced from 56 to 20, the correlation values exhibit a stronger relationship between the response and predictors. Therefore, the same phenomena can be explained by the reduction of variables.