No description available.

No description available.

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. in Meteorology)--Naval Postgraduate School, March 2006. / Thesis Advisor(s): Patrick A. Harr. "March 2006." Includes bibliographical references (p. 89). Also available online.

"The study was restricted to cyclones which appeared over Europe for at least two consecutive days during 1950"--Page v. The statistical relationships between five parameters which may be used to represent an atmospheric vortex are studied. These parameters are: the central height, ?h, the space change of height, ?h, a size factor, D, the mean gradient, h, and the "gradient-area index", I. The primary purpose of the study is to determine the relationship between the central height and each of the other parameters. The study was restricted to cyclones which appeared over Europe for at least two consecutive days during 1950. All measurements were made on the 500-mb chart. Each parameter was evaluated for 263 cyclones and the 24-hr change of each parameter was computed for 208 cyclones. The methods used to evaluate each parameter are discussed. For each pair of parameters, linear correlation coefficients were computed from grouped data. / "A Paper." / Typescript. / "Submitted to the Graduate Council of Florida State University in partial fulfillment of the requirements for the degree of Master of Science." / Advisor: Thomas A. Gleeson, Professor Directing Paper. / Author's name handwritten on cover: Robert B. DesJardins. / Includes bibliographical references.

Microwave satellite imagery is a valuable tool for the observation of mature tropical cyclones. This study examined the application of microwave data to the extratropical transition (ET) process. During ET a tropical cyclone (TC) moves into an area of large gradients in many atmospheric parameters. The data sparse regions in which these gradients exist make the examination of key physical mechanisms responsible for the ET process difficult. The Advanced Microwave Sounding Unit (AMSU) was used to investigate temperature and water vapor gradients. Advanced Microwave Scanning Radiometer was used to examine WV gradients and precipitation intensities. These observations were combined with diagnostic analysis of frontogenesis during the ET process of a TC that re-intensified as an extratropical cyclone and a TC that dissipated. Although the different outcomes of ET were related to differences in coupling between the decaying TC and the midlatitude environment, microwave data were useful for defining specific characteristics that either inhibited or enhanced the coupling of the decaying TC with the midlatitude environment.

During the 2005 North Atlantic hurricane season, an objective tropical cyclone vortex identification and tracking technique was applied to analyzed and forecast fields of three global operational numerical models- the National Centers for Environmental Prediction Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the United Kingdom Meteorological Office model (UKMET). For the purpose of evaluating each model's performance with respect to forecasting tropical cyclone formation, 14 relevant parameters are cataloged for every tropical vortex. In this study, nine of the fourteen parameters are subjected to a linear discriminant analysis applied to all forecast vortices that exceed vorticity and warm core thresholds. The goal is to determine the combination of parameters for each model, at each 12-h forecast period to 120h, that best discriminates between a vortex that is correctly forecast to intensify into a tropical cyclone (developer) and a vortex that is forecast to intensify into a tropical cyclone, but does not (false alarm). The performance of the resulting discriminant functions are then assessed using the Heidke Skill Score and Receiver Operating Characteristic curves. Overall, the methodology applied to forecasts from the UKMET model shows the most skill with regard to identifying correct forecasts of tropical cyclone formation. / US Navy (USN) author.

Whereas the Joint Typhoon Warning Center (JTWC) has ten track forecasts to 72 h, only four dynamical model forecasts are available at 96 h and 120 h. Forming a selective consensus (SCON) by proper removal of a likely erroneous track forecast is hypothesized to be more accurate than the non-selective consensus (NCON) of all four models. Conceptual models describing large track error mechanisms, which are related to known tropical cyclone motion processes being misrepresented in the dynamical fields, are applied to forecasts by the Navy Operational Global Atmospheric Prediction System (NOGAPS), U.S. Navy version of the Geophysical Fluid Dynamics Laboratory Model (GFDN), United Kingdom Meteorological Office (UKMO), and National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) during the 2005 western North Pacific typhoon season. A systematic error in the GFDN was identified in which the model built a false anticyclone downstream of the Tibetan Plateau, which explained over 50% of the large GFDN track errors. In the GFS model, 95% of the large errors occurred due to an incorrect depiction of the vertical structure of the tropical cyclone. The majority of NOGAPS and UKMO large errors were caused by an incorrect depiction of the midlatitude system evolutions. Characteristics of the erroneous forecast tracks and corresponding model fields are documented and illustrative case studies are presented. By applying rules of the Systematic Approach, the average SCON error was 222 n mi (382 n mi) less than NCON (JTWC) in 20% of all 120-h forecasts. / US Air Force (USAF) author.

A new Tropical Cyclone (TC) surface wind speed probability product from the National Hurricane Center (NHC) takes into account uncertainty in track, maximum wind speed, and wind radii. A Monte Carlo (MC) model is used that draws from probability distributions based on historic track errors. In this thesis, distributions of forecast track errors conditioned on forecast confidence are examined to determine if significant differences exist in distribution characteristics. Two predictors are used to define forecast confidence: the Goerss Predicted Consensus Error (GPCE) and the Global Forecast System (GFS) ensemble spread. The distributions of total-, along-, and crosstrack errors from NHC official forecasts are defined for low, average, and high forecast confidence. Also, distributions of the GFS ensemble mean total-track errors are defined based on similar confidence levels. Standard hypothesis testing methods are used to examine distribution characteristics. Using the GPCE values, significant differences in nearly all track error distributions existed for each level of forecast confidence. The GFS ensemble spread did not provide a basis for statistically different distributions. These results suggest that the NHC probability model would likely be improved if the MC model would draw from distributions of track errors based on the GPCE measures of forecast confidence / US Air Force (USAF) author.

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Russell L. Elsberry, Patrick A. Harr. Includes bibliographical references (p. 69). Also available online.