The largest errors in tropical cyclone intensity forecasting are usually the result of rapid intensification events, where rapid intensification is defined as a change in minimum central pressure of at least 42 hP sub a in a 24 hour period, or a satellite-inferred increase in maximum sustained winds of a least 23 m/s 1 per day when no measurement of central pressure is possible. An observational and theoretical study is made of the unique characteristics of rapidly intensifying typhoons in the western North Pacific. Climatological data, digital infrared satellite imagery and composited rawinsonde sounding data within 5 deg of the center of tropical cyclones were used to identify the distinctive features associated with rapid intensifiers when compared to other stratifications of non-rapid intensifiers and non-intensifiers. Rapid intensity change is indicated on satellite imagery as an extreme concentration of convection near the cyclone center. Relative concentrations of inner and outer deep cumulus convection reveal a predictive relationship between the ratio of inner core to outer core convection and the onset of rapid intensity change 12 hours later. This prediction technique was found to successfully forecast rapid or non-rapid intensification in over 90% of the cases in a three-year study of 70 northwest Pacific tropical cyclones. A physical explanation of the likely processes associated with rapid intensification is also presented. Rapid intensification is believed to be the result of weak asymmetrical wind flow across the cyclone at upper levels of the troposphere. Rapid intensifiers are shown to have large warm anomalies near the tropopause and weak vertical shear of tangential winds below 150 hP sub a. Theses. (jhd).

This book deals with recent advances in our understanding and prediction of tropical cyclogenesis, intensification and movement as well as landfall processes like heavy rainfall, gale wind and storm surge based on the latest observational and numerical weather prediction (NWP) modeling platforms. It also includes tropical cyclone (TC) management issues like early warning systems, recent high impact TC events, disaster preparedness, assessment of risk and vulnerability including construction, archiving and retrieval of the best tracking and historical data sets, policy decision etc., in view of recent findings on climate change aspects and their impact on TC activity. The chapters are authored by leading experts, both from research and operational environments. This book is relevant to cyclone forecasters and researchers, managers, policy makers, graduate and undergraduate students. It intends to stimulate thinking and hence further research in the field of TCs and climate change, especially over the Indian Ocean region and provides high-quality reference materials for all the users mentioned above for the management of TCs over this region.

Forecasting rapid intensification (RI) of tropical cyclones (TCs) is considered one of the most challenging problems for the TC operational and research communities and remains a top priority for the National Hurricane Center. Upon landfall, these systems can have detrimental impacts to life and property. To support continued improvement of TC RI forecasts, this study investigated large-scale TC environments undergoing RI in the North Atlantic basin, specifically identifying important diagnostic variables in three-dimensional space. These results were subsequently used in the development of prognostic machine learning algorithms designed to predict RI 24 hours prior to occurrence. Using three RI definitions, this study evaluated base-state and derived meteorological parameters through S-mode and T-mode rotated principal component analysis, hierarchical compositing analysis, and hypothesis testing. Additionally, nine blended intelligence ensembles were developed using three RI definitions trained on data from the Statistical Hurricane Intensity Prediction Scheme- Rapid Intensification Index, Global Ensemble Forecast System Reforecast, and Final Operational Global Analysis. Performance metrics for the intelligence ensembles were compared against traditional linear methods. Additionally, a tenth ensemble was created using forecast data generated from Weather Research and Forecasting model simulations of TC RI events in the open North Atlantic and compared against linear methods. Results revealed modest classification ability of machine learning algorithms in predicting the onset of RI 24 hours in advance by including TC environmental spatial information of temperature and moisture variables, as well as variables indicative of ambient environmental interactions.

Due to continual progress in the large-scale integration of semiconductor circuits, parallel computing principles can already be met in low-cost sys tems: numerous examples exist in image processing, for which special hard ware is implementable with quite modest resources even by nonprofessional designers. Principles of content addressing, if thoroughly understood, can thereby be applied effectively using standard components. On the other hand, mass storage based on associative principles still exists only in the long term plans of computer technologists. This situation is somewhat confused by the fact that certain expectations are held for the development of new storage media such as optical memories and "spin glasses" (metal alloys with low-density magnetic impurities). Their technologies, however, may not ripen until after "fifth generation" computers have been built. It seems that software methods for content addressing, especially those based on hash coding principles, are still holding their position firmly, and a few innovations have been developed recently. As they need no special hardware, one might expect that they will spread to a wide circle of users. This monograph is based on an extensive literature survey, most of which was published in the First Edition. I have added Chap. ?, which contains a review of more recent work. This updated book now has references to over 1200 original publications. In the editing of the new material, I received valuable help from Anneli HeimbUrger, M. Sc. , and Mrs. Leila Koivisto.