published_or_final_version / Geography and Geology / Master / Master of Philosophy

published_or_final_version / Geography and Geology / Master / Master of Philosophy

A logistic regression model (LRRI) and a neural network model (NNRI) for RI forecasting of TCs are developed for the period 2000--2007. The five significant predictors are intensity change in the previous 12 h, intensification potential, lower-level relative humidity, eddy flux convergence at 200 hPa, and vertical wind shear. The verification of forecasts in 2008 typhoon season shows that NNRI outperforms LRRI for RI detection. / Despite improvements in statistical and dynamic models in recent years, the prediction of tropical cyclone (TC) intensity still lags that of track forecasting. Recent advances in satellite remote sensing coupled with artificial intelligence techniques offer us an opportunity to improve the forecasting skill of typhoon intensity. / In this study rapid intensification (RI) of TCs is defined as over-water minimum central pressure fall in excess of 20 hPa over a 24-h period. Composite analysis shows satellite-based surface latent heat flux (SLHF) and inner-core rain rate (IRR) are related to rapid intensifying TCs over the western North Pacific, suggesting SLHF and IRR have the potential to add value to TC intensity forecasting. / Several linear regression models and neural network models are developed for the intensity prediction of western North Pacific TC at 24-h, 48-h, and 72-h intervals. The datasets include Japan Meteorological Agency (JMA) Regional Specialized Meteorological Center Tokyo (RSMC Tokyo) best track data, the National Centers for Environmental Prediction (NCEP) Global Forecasting System Final analysis, the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager sea surface temperature (SST), the Objectively Analyzed Air-sea Fluxes (OAflux) SLHF and TRMM Multisatellite Precipitation Analysis (TMPA) rain rate data. The models include climatology and persistence (CLIPER), a model based on Statistical Typhoon Intensity Prediction System (STIPS), which serves as the BASE model, and a model of STIPS with additional satellite estimates of IRR and SLHF (STIPER). A revised equation of TC maximum potential intensity (MPI) is derived using TMI Optimally Interpolated Sea Surface Temperature data (OISST) with higher temporal and spatial resolutions. Analysis of the resulting models indicates that the STIPER model reduces the mean absolute intensity forecast error by 6% for TC intensity forecasts out to 72 h compared to the CLIPER and BASE. Neural network models with the same predictors as STIPER can provide up to 28% error reduction compared to STIPER. The largest improvement is the intensity forecasts of the rapidly intensifying and rapidly decaying TCs. / Gao, Si. / Adviser: Long Song Willie Chiu. / Source: Dissertation Abstracts International, Volume: 73-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 94-105). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Tropical cyclones are among the most devastating natural disasters for human beings and the natural and manmade assets near to Atlantic basin. Estimating the current and future intensity of these powerful storms is crucial to protect life and property. Many methods and models exist for predicting the evolution of Atlantic basin cyclones, including numerical weather prediction models that simulate the dynamics of the atmosphere which require accurate measurements of the current state of the atmosphere (NHC, 2019). Often these models fail to capture dangerous aspects of storm evolution, such as rapid intensification (RI), in which a storm undergoes a steep increase in intensity over a short time. To improve prediction of these events, scientists have turned to statistical models to predict current and future intensity using readily collected satellite image data (Pradhan, 2018). However, even the current-intensity prediction models have shown limited success in generalizing to unseen data, a result we confirm in this study. Therefore, building models for the estimating the current and future intensity of hurricanes is valuable and challenging. In this study we focus on to estimating cyclone intensity using Geostationary Operational Environmental Satellite images. These images represent five spectral bands covering the visible and infrared spectrum. We have built and compared various types of deep neural models, including convolutional networks based on long short term memory models and convolutional regression models that have been trained to predict the intensity, as measured by maximum sustained wind speed. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

The recent elevated North Atlantic hurricane activity has generated considerable interests in the interaction between tropical cyclones (TCs) and climate change. The possible connection between TCs and the changing climate has been indicated by observational studies based on historical TC records; they indicate emerging trends in TC frequency and intensity in some TC basins, but the detection of trends has been hotly debated due to TC track data issues. Dynamical climate modeling has also been applied to the problem, but brings its own set of limitations owing to limited model resolution and uncertainties. The final goal of this study is to project the future changes of North Atlantic TC behavior with global warming for the next 50 years using the Nested Regional Climate Model (NRCM). Throughout the course of reaching this goal, various uncertainties and limitations in simulating TCs by the NRCM are identified and explored. First we examine the TC tracking algorithm to detect and track simulated TCs from model output. The criteria and thresholds used in the tracking algorithm control the simulated TC climatology, making it difficult to objectively assess the model's ability in simulating TC climatology. Existing tracking algorithms used by previous studies are surveyed and it is found that the criteria and thresholds are very diverse. Sensitivity of varying criteria and thresholds in TC tracking algorithm to simulated TC climatology is very high, especially with the intensity and duration thresholds. It is found that the commonly used criteria may not be strict enough to filter out intense extratropical systems and hybrid systems. We propose that a better distinction between TCs and other low-pressure systems can be achieved by adding the Cyclone Phase technique. Two sets of NRCM simulations are presented in this dissertation: One in the hindcasting mode, and the other with forcing from the Community Climate System Model (CCSM) to project into the future with global warming. Both of these simulations are assessed using the tracking algorithm with cyclone phase technique. The NRCM is run in a hindcasting mode for the global tropics in order to assess its ability to simulate the current observed TC climatology. It is found that the NRCM is capable of capturing the general spatial and temporal distributions of TCs, but tends to overproduce TCs particularly in the Northwest Pacific. The overpredction of TCs is associated with the overall convective tendency in the model added with an outstanding theory of wave energy accumulation leading to TC genesis. On the other hand, TC frequency in the tropical North Atlantic is under predicted due to the lack of moist African Easterly Waves. The importance of high-resolution is shown with the additional simulation with two-way nesting. The NRCM is then forced by the CCSM to project the future changes in North Atlantic TCs. An El Nino-like SST bias in the CCSM induced a high vertical wind shear in tropical North Atlantic, preventing TCs from forming in this region. A simple bias correction method is applied to remove this bias. The model projected an increase both in TC frequency and intensity owing to enhanced TC genesis in the main development region, where the model projects an increased favorability of large-scale environment for TC genesis. However, the model is not capable of explicitly simulating intense (Category 3-5) storms due to the limited model resolution. To extrapolate the prediction to intense storms, we propose a hybrid approach that combines the model results and a statistical modeling using extreme value theory. Specifically, the current observed TC intensity is statistically modeled with the General Pareto distribution, and the simulated intensity changes from the NRCM are applied to the statistical model to project the changes in intense storms. The results suggest that the occurrence of Category 5 storms may be increased by approximately 50% by 2055.