Collaborative Research Projects in Support of FNMOC Operational Mission

LONG-TERM GOAL: The long-term goal is to improve the prediction of tropical cyclone track and structure so that warnings to the Fleet units afloat and ashore are optimized.Award #: N0001499WR3000

Systematic Approach to Tropical Cyclone Track Forecasting

LONG-TERM GOALS: The long-term goals of this project, which is being pursued in collaboration with R. L. Elsberry and M. A. Boothe, are to improve not only the quantitative accuracy of official tropical cyclone (TC) track forecasts, but also the qualitative meteorological utility of those forecasts. Needed improve- ments in the accuracy of official TC track forecasts include: (i) reducing the severity and frequency of major track forecast "busts" for which the track forecast error at a particular time exceeds seasonal averages by a factor of two or more; (ii) widening the margin by which on average the official TC track forecast improves upon available numerical and other objective TC track forecast guidance; and (iii) better temporal consistency (i.e., watch-to-watch) of official TC track forecasts. Meteorological utility refers to the interpretative usefulness imparted (value added) to the official forecast track by the TC forecaster's formulation and articulation in narrative form of the meteorological reasoning behind the forecast, and should include a situation-specific assessment of the likely uncertainty in the forecast, and the range/probability of alternate scenarios that may be realized. Such reasoning often critically influences recommendations and decisions made by meteorologists and authorities responsible for TC-threatened areas. The long-term goal in this regard is to equip TC forecasters with the conceptual tools necessary to impart a high degree of meteorological utility to each forecast within the constraints of the current state of the science.Award # N0001497WR3002

Development of an Expert System Based on the Systematic Approach to Tropical Cyclone Track Forecasting

LONG-TERM GOALS: The long-term goals of this project are to improve the quantitative accuracy and interpretative utility of official tropical cyclone (TC) track forecasts by enabling forecasters to successfully recognize and skillfully compensate for periods when numerical TC track forecast models are likely to be making highly erroneous track forecasts. The conceptual methodology for accomplishing these goals is the Systematic Approach to Tropical Cyclone Track Forecasting (hereafter Systematic Approach) conceived by Carr and Elsberry (1994).Award # N0001499WR3004

Assessment of the Potential for Prediction of Tropical Cyclone Formation in the Navy Global Model

4D.7Some of the recent increase in skill of tropical cyclone track predictions has been attributed to increased accuracy of guidance from global dynamical models. Indeed, as the skill in dynamical predictions has been extended into the medium ranges, requirements for fi ve-day track predictions are being contemplated. However, a tropical cyclone may form, intensify, and move a long distance in five days to become a serious threat to maritime activities and coastal locations.This research is sponsored by the Office of Naval Research, Marine Meteorology Program

Tropical Cyclone Structure And Motion

Long-term goals: To improve tropical cyclone track and intensity prediction through a research program combining high resolution modeling and detailed observational studies to investigate physical processes by which the motion and structure of a tropical cyclone are modified.N0001400WR2017

A global evaluation of multi-model ensemble tropical cyclone track probability forecasts

At the Met Office, dynamic ensemble forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS-G), the European Centre for Medium-Range Weather Forecasts Ensemble (ECMWF ENS) and National Centers for Environmental Prediction Global Ensemble Forecast System (NCEP GEFS) global ensemble forecast models are post-processed to identify and track tropical cyclones. The ensemble members from each model are also combined into a 108-member multi-model ensemble. Track probability forecasts are produced for named tropical cyclones showing the probability of a location being within 120km of a named tropical cyclone at any point in the next 7-days, and also broken down in to each 24-hour forecast period. This paper presents the verification of these named-storm track probabilities over a two-year period across all global tropical cyclone basins, and compares the results from basin to basin. The combined multi-model ensemble is found to increase the skill and value of the track probability forecasts over the best-performing individual ensemble (ECMWF ENS), for both overall 7-day track probability forecasts and 24-hour track probabilities. Basin-based and storm-based verification illustrates that the best performing individual ensemble can change from basin to basin and from storm to storm, but that the multi-model ensemble adds skill in every basin, and is also able to match the best performing individual ensemble in terms of overall probabilistic forecast skill in several high-profile case studies. This study helps to illustrate the potential value and skill to be gained if operational tropical cyclone forecasting can continue to migrate away from a deterministic-focused forecasting environment to one where the probabilistic situation-based uncertainty information provided by the dynamic multi-model ensembles can be incorporated into operational forecasts and warnings

Tropical cyclone track and genesis forecasting using satellite microwave sounder data

Although many dynamical and statistical prediction schemes are available to forecasters, tropical cyclone track errors are still large. One primary difficulty is that tropical cyclones exist over the data-sparse tropical oceans. Satellite sounders, however, routinely provide numerous data over these areas. Mean layer temperatures from the Scanning Microwave Spectrometer on board the Nimbus 6 satellite are decomposed using empirical orthogonal functions, and the expansion coefficients are related to deviations from the persistence forecast location, to speed change, to direction change and to intensity change. The significance of the regression equations is tested by a null hypothesis of zero correlation coefficient. It appears that significant information about tropical cyclone motion exists in the satellite-estimated mean layer temperatures, especially at upper levels. A physical interpretation of the statistical results is offered, and a one-storm-out independent test is used to test the stability of the equations. Finally, some further work is suggested

Geodatabase-assisted storm surge modeling

Tropical cyclone-generated storm surge frequently causes catastrophic damage in communities along the Gulf of Mexico. The prediction of landfalling or hypothetical storm surge magnitudes in U.S. Gulf Coast regions remains problematic, in part, because of the dearth of historic event parameter data, including accurate records of storm surge magnitude (elevation) at locations along the coast from hurricanes. While detailed historical records exist that describe hurricane tracks, these data have rarely been correlated with the resulting storm surge, limiting our ability to make statistical inferences, which are needed to fully understand the vulnerability of the U.S. Gulf Coast to hurricane-induced storm surge hazards. This dissertation addresses the need for reliable statistical storm surge estimation by proposing a probabilistic geodatabase-assisted methodology to generate a storm surge surface based on hurricane location and intensity parameters on a single desktop computer. The proposed methodology draws from a statistically representative synthetic tropical cyclone dataset to estimate hurricane track patterns and storm surge elevations. The proposed methodology integrates four modules: tropical cyclone genesis, track propagation, storm surge estimation, and a geodatabase. Implementation of the developed methodology will provide a means to study and improve long-term tropical cyclone activity patterns and predictions. Specific contributions are made to the current state of the art through each of the four modules. In the genesis module, improved representative data from historical genesis populations are achieved through implementation of a stratified-Monte-Carlo sampling method to simulate genesis locations for the North Atlantic Basin, avoiding potential non-representative clustering of sampled genesis locations. In the track module, the improved synthetic genesis locations are used as the starting point for a track location and intensity methodology that incorporates storm strength parameters into the synthetic tracks and improves the positional quality of synthetic tracks. In the surge module, high-resolution, computationally intensive storm surge model results are probabilistically integrated in a computationally fast-running platform. In the geodatabase module, historic and synthetic tropical cyclone genesis, track, and surge elevation data are combined for efficient storage and retrieval of storm surge data

Persistent northward North Atlantic tropical cyclone track migration over the past five centuries

Accurately predicting future tropical cyclone risk requires understanding the fundamental controls on tropical cyclone dynamics. Here we present an annually-resolved 450-year reconstruction of western Caribbean tropical cyclone activity developed using a new coupled carbon and oxygen isotope ratio technique in an exceptionally well-dated stalagmite from Belize. Western Caribbean tropical cyclone activity peaked at 1650 A.D., coincident with maximum Little Ice Age cooling, and decreased gradually until the end of the record in 1983. Considered with other reconstructions, the new record suggests that the mean track of Cape Verde tropical cyclones shifted gradually north-eastward from the western Caribbean toward the North American east coast over the last 450 years. Since ~1870 A.D., these shifts were largely driven by anthropogenic greenhouse gas and sulphate aerosol emissions. Our results strongly suggest that future emission scenarios will result in more frequent tropical cyclone impacts on the financial and population centres of the northeastern United States