ropical cyclone-induced tornadoes (TCIT) exacerbate the devastation that landfalling tropical cyclones have on the United States. This research applied machine learning techniques in conjunction with midlatitude severe weather parameters to create an artificial intelligence (AI) capable of predicting TCIT occurrence. Severe weather diagnostic variables were collected at thousands of gridpoints from the North American Regional Reanalysis (NARR) to characterize the environments within tropical cyclones between 1991 and 2011. A support vector machine (SVM) was generated in various configurations to obtain the most effective AI. This approach revealed many parameters that were ineffective at predicting TCITs (primarily those utilizing the effective inflow layer). In addition, the most highly configured AI were capable of predicting TCIT occurrence with a Heidke Skill Score around 0.48.

Decades of research have fostered a greater understanding of the environmental controls that drive tropical cyclone (TC) intensity change, yet the community has achieved only small improvements in intensity forecasting. Numerous environmental factors impact TC intensity, such as vertical wind shear and sea surface temperatures (SSTs), but little research has focused on establishing if SST change under the TC, or SST gradients, influence these intensity changes. This study investigated three methods to compute SST gradients. The first method calculated the SST change within fixed distances along the track. In the second and third methods, the SST was calculated over the distance traversed by the TC in two separate six-hour periods. By examining 455 24-hour weakening episodes in the eastern North Pacific, this study revealed that the first SST gradient method explained the highest 24-hour weakening variance for TCs located within SSTs at or lower than 26.5 degrees C.

京都大学 / 新制・課程博士 / 博士(地球環境学) / 甲第24766号 / 地環博第238号 / 新制||地環||46(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 小林 広英, 准教授 落合 知帆, 准教授 深町 加津枝 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

While the standard wind product (L2B) available operationally in near-real time from SeaWinds on QuikSCAT is only 25 km in resolution, QuikSCAT data can be enhanced to yield a 2.5 km ultra-high resolution (UHR) product. The latter can be used to help estimate Tropical Cyclone (TC) characteristics such as TC eye center and wind radii. Two studies are conducted in this thesis, in which QuikSCAT UHR wind product's effectiveness in estimating these TC characteristics is evaluated. First, a comparison is made between the analyst's choice of eye location based on UHR images and interpolated best-track position. In this analysis, the UHR images are divided into two categories, based on the analyst's confidence level of finding the eye center location. In each category, statistical error quantities are computed. UHR images within the high confidence category can provide, for a given year and basin, mean error distance as small as 19 km with a 10 km standard deviation. Second, a visual comparison of QuikSCAT's performance in estimating wind radii is made. QuikSCAT's performance is gauged against H*wind dataset and the Extended Best-Track (EBT) dataset. Results show that QuikSCAT UHR data yields a correct 34-kt wind radius most of the time regardless of the TC category when compared to both H*wind and EBT, whereas the 50- and 64-kt wind radii visual estimates do not always agree with H*wind and EBT. A more sophisticated method is also implemented to automatically estimate wind radii based on a model fit to QuikSCAT data. Results from this method are compared with EBT wind radii. Wind radii obtained from QuikSCAT model fit are generally highly correlated with EBT estimated wind radii. These two studies show that QuikSCAT UHR wind products are helpful in estimating TC eye location and wind radii, thus improving TC forecasting and analysis.

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This thesis includes a numerical comparison of different turbulence models and particle models in terms of convergence time and physical accuracy. A cyclone is used as the computational domain. Cyclones are common devices for separating two or more substances. The work is divided into an experimental part and a numerical part. In the experiments, characteristics of the cyclone were measured. This data is then used to evaluate different numerical modeling approaches. The numerical part consists of two parts, namely single phase flow and multiphase flow, where different modeling aspects are examined and presented. Furthermore, important parameters that characterize a cyclone, such as pressure drop and separation efficiency, are calculated. The separation efficiency, i.e. how much dust that actually goes to the dust bin, is calculated for two different types of dust. The software used for the numerical simulations has been Star-CCM+.