This study examines the global tropical cyclones (TC) patterns and distributions from 2001-2010. The objective of the study was to find out the possible relationship between the TC activities between different oceans. The TC track record was obtained from the Hurricane Data Centre at Unisys Weather webpage and it has been plotted and transferred to a spread sheet format. By combining the data with the oceanic maps from General Bathymetric Chart of the Oceans (GEBCO) digital Atlas, we could conclude the trend and distributions of the TC in each year. Various data were also collected from the National Oceanic and Atmospheric Administration (NOAA), Climate Prediction Center and Bureau of Meteorology (BOM) so as to further explain the TC relationships in different regions. It is concluded that the TC distributions has no significant relationship between oceans in the same hemisphere but it shows certain pattern throughout the ten years record. / published_or_final_version / Applied Geosciences / Master / Master of Science

published_or_final_version / Geography and Geology / Master / Master of Philosophy

Mode of access: World Wide Web. / Thesis (Ph. D.)--University of Hawaii at Manoa, 2004. / Includes bibliographical references (leaves 153-164). / Electronic reproduction. / Also available by subscription via World Wide Web / xviii, 164 leaves, bound ill. (some col.) 29 cm

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.

Thesis (M. S.)--Earth and Atmospheric Sciences, Georgia Institute of Technology, 2010. / Committee Chair: Curry, Judith A.; Committee Member: Black, Robert X.; Committee Member: Deng, Yi. Part of the SMARTech Electronic Thesis and Dissertation Collection.

published_or_final_version / Geography and Geology / Master / Master of Philosophy

Thesis (M.S.)--University of Hawaii at Manoa, 2006. / Includes bibliographical references (leaves 28-29). / vii, 58 leaves, bound 29 cm

Zhang, Wei. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 184-203). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

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.

Thesis (MEng (Civil Engineering))--University of Stellenbosch, 1999. / 100 leaves single printed pages, preliminary pages and numberd pages 1.1-9.1.Includes bibliography. List of figures, tables, symbols and acronyms. Scanned with a HP Scanjet 8250 Scanner to pdf format (OCR). / ENGLISH ABSTRACT: A tropical cyclone is a non-frontal, synoptic scale, low-pressure system over tropical or subtropical waters with organised convection and a definite cyclonic surface wind circulation. The system varies in size between a hundred and a few thousand kilometres in diameter with high winds circulating around a central low pressure. The process of bringing the lower atmospheric layers into thermodynamic equilibrium with the warm tropical waters add the energy to the atmosphere and lower the surface pressure. If favourable climatic conditions exist this leads to the formation of a warm core vortex, which can develop into a tropical cyclone. The occurrence of tropical cyclones follows seasonal variations, the tropical cyclone season for the Southwest Indian Ocean being between November and March. The occurrences peak along the Southern African East Coast between Mid-January and Mid-February. The data on the location and intensity of tropical cyclones along the Southeast Africa coastline were obtained from the Joint Typhoon Warning Centre and span the period between 1848 and 1999. The available data before 1945 consist of tropical cyclone tracks that influenced populated areas or were encountered by ships. It was assumed that a number of tropical cyclones before 1945 were not recorded and therefore data collected before 1945 were disregarded in the analysis. The development of radar in 1945 significantly improved the detection of tropical cyclones. Some of the tropical cyclone tracks recorded between 1945 and 1956 contain information about the intensity of the tropical cyclone. Since the dawn of the satellite age in the mid 1980's, the detection of tropical cyclones and intensity measurements have improved vastly. Monte Carlo simulation techniques were used to create long term data series based on the available measured data. Statistical distributions were fitted for each characteristic describing the tropical cyclone at its nearest position to the site under investigation. Tropical cyclones frequently occur along the Southern African East Coast. The region where more than one tropical cyclone per 100 years can be expected is bordered by latitudes 2.5°S to 32.5°S. The design parameters for structures in these regions should provide for the influence that a tropical cyclone will have on the site. The occurrence rate and expected maximum intensity of tropical cyclones with a 1DO-year return period vary with latitude along the Southern African East Coast. The maximum number of tropical cyclones in a 1DO-year period occurs at latitude 15°S with an expected number of tropical cyclones of 157.2 per 100 years. The maximum expected tropical cyclone intensity in a 100-year period is 143.5 knots at latitude 17.5°S. / AFRIKAANSE OPSOMMING: Tropiese siklone is nie-frontale laagdrukstelsels wat hulle ontstaan het oor tropiese en subtropiese oseane. 'n Stelsel bestaan uit 'n sentrale laagdrukstelsel met sirkulerende winde daar om. 'n Sikloon se deursnee kan wissel van 'n honderd tot 'n paar duisend kilometer. 'n Laagdrukstelsel ontstaan as gevolg van 'n termodinamiese wanbalans tussen die atmosfeer en die warm oseaanwater in die trope. Indien die benodigde atmosferiese toestande heers kan die laagdrukstelsel in 'n tropiese sikloon ontwikkel. In die Suidwestelike Indiese Oseaan vorm tropiese siklone tussen November en Maart. Die meeste siklone kom hier voor vanaf middel Januarie tot middel Februarie. Data is verkry vanaf die "Joint Typhoon Warning Centre" vir die Suidwestelike Indiese Oseaan en strek vanaf 1848 tot 1999. Die data voor 1945 verteenwoordig slegs die tropiese siklone wat bewoonde areas of skeepsvaart beinvloed het. Daar is aangeneem dat 'n betekenisvolle getal van die tropiese siklone voor 1945 nie gedokumenteer is nie en derhalwe is slegs data van sikloon voorkomste na 1945 gebruik in die studie. Vanaf 1945 het die ontwikkeling van radar die opsporing van siklone in onbewoonde areas moontlik gemaak. Die gebruik van weersatelliete vanaf die middel 1980's het die kwaliteit van die data nog verder verbeter. Monte Carlo simulasie tegnieke is gebruik om langtermyn data vir spesifieke posisies langs die kus te genereer. Statistiese verdelings is gepas op die eienskappe wat die sikloon beskryf wanneer dit die naaste posisie aan die terrein bereik. Die passing van die verdelings is gedoen op die beskikbare historiese data. Die verdelings is dan gebruik om langtermyn data stelle te skep vir die terrein. Tropiese siklone kom gereeld in die Suidwestelike Indiese Oseaan voor en beinvloed die Suid-Afrikaanse Ooskus. Meer as een tropiese sikloon kan elke 100 jaar verwag word in kusgebiede tussen breedtegrade 2.5° S en 32.5° S. Die ontwerpe vir strukture in die gebied moet dus voorsiening maak vir die invloed van tropiese siklone. Die voorkoms en intensiteit van tropiese siklone varieer met breedtegraad langs die Suid-Afrikaanse Ooskus. Die meeste siklone word verwag by breedtegraad 15°S met 'n gemiddelde van 157.2 siklone per 100 jaar. Die mees intensiewe siklone kom voor by breedtegraad 17.5°S met 'n verwagte 1:100 jaar intensiteit van 143.5 knope.