Southwest Pacific Tropical Cyclone Frequency and Intensity Related to Observed and Modeled Geophysical and Aerosol Variables

Abstract

The dissertation focuses on western region of Southwest Pacific Ocean (SWPO) basin (135E - 180, and 5S - 35S) tropical cyclone (TC) climatology using observed and modeled data. The classification-based machine learning approach identifies the synoptic geophysical and aerosol environment favorable or unfavorable for TC intensification and intensity change prior to landfall incorporating observational and satellite data. A multiple poisson regression model with varying temporal monthly lags was used to build a relationship between the number of monthly TC days with basin wide average dust aerosol optical depth (AOD), sea surface temperature (SST), and upper ocean temperature (UOT). This idea was expanded by building a prediction model of TC count to see how changes in one unit of dust, SST, and UOT can contribute to changes in monthly TC days. A decision tree and random forest classifier was used to discriminate tropical depression (TD) and tropical storm (TS) events and examined their classification ability of unseen data. The goal was to derive a robust model that can balance correct and incorrect classifications and provide higher prediction accuracy. Classification decisions are determined by training selected classifiers using variables assigned to hundreds of storm event samples and identified the most influencing predictors in the classification decision. Mean composite maps of the most important geophysicaland aerosol variables during classification decisions using each set of TD and TS case was developed to facilitate geophysical comparisons during different environments. The spatiotemporal climatology of influential variables is important to better understand the TC climatology. The study used hexagonal tessellation and geographically weighted regression to spatially model the TC minimum central pressure, SST, 1000 mb relative humidity, and sea salt AOD relationship to better understand the spatio-temporal TC climatology over the space. This research further developed a classification and prediction model for whether a TC will intensity or weaken just before making landfall using a random forest classifier, geophysical, and aerosol data including physical observations of TCs 24-hours prior to landfall. Initial intensity, sea skin temperature (SkT), and longitude identified as the most important variables for the classification decision for the mainland and island landfall cases. The predicted intensity prior to landfall should lead to a higher success rate of informed decisions along the coast which will alleviate coastal Australia and SWPO islands TC related risk.