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Charles J. Neumann
and
Elie A. Randrianarison

Abstract

The derivation of a system of regression equations for the prediction of tropical cyclone motion over the Southwest Indian Ocean is described. The equations use the same predictors that are typically used as storm selection criteria by analog models. In this sense, the prediction model simulates an analog forecast. To complete the simulation, a method is described whereby the prediction errors (residuals) of the development data are used to construct equi-probability ellipses similar to those used by analog models. Testing the prediction equations on three years of independent data for the years 1970, 1971 and 1972 indicates a forecast accuracy closely approximating that realized by a similar system of prediction equations in operational use in the Atlantic.

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Charles J. Neumann
and
John R. Hope

Abstract

Statistical tropical cyclone prediction systems typically fall into one of three categories: 1) those using meteorological predictors derived from observed synoptic data; 2) those using purely empirical predictors such as climatology, present motion, past motion, analogs, etc.; and 3) those using combinations of both synoptic and empirical predictors. The variance-reducing potential of each of these prediction systems on given acts of dependent data is examined in detail. In general, it is found that empirical prediction systems are always superior in the shorter range forecast periods and even for extended forecast periods before storm recurvature. During and after storm recurvature, however, the synoptic-type predictors provide a better means of reducing the variance of tropical cyclone motion. It is shown that statistical tropical cyclone forecasting systems should make judicious use of both synoptic and empirical predictors.

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JOHN R. HOPE
and
CHARLES J. NEUMANN

Abstract

The HURRAN (hurricane analog) technique for selecting analogs for an existing tropical storm or hurricane is described. This fully computerized program examines tracks of all Atlantic tropical storms or hurricanes since 1886, and those that have designated characteristics similar to an existing storm are selected and identified. Positions of storms selected as analogs are determined at 12, 24, 36, 48, and 72 hr after the initial time. Probability ellipses are computed from the resulting arrays and plotted on an x, y (CALCOMP) offline plotter. The program also has the option of computing the probability that the storm center will be located within a fixed distance of a given point at a specific time. Operational use of HURRAN during the 1969 hurricane season, including both its utility and limitations, is described.

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Arthur C. Pike
and
Charles J. Neumann

Abstract

Tropical cyclone track forecast error ranges widely among individual storms, as well as globally. To study its regional variability, structurally identical climatology-and-persistence (CLIPER) track forecast models were constructed for the six major tropical cyclone basins of the world. Developmental errors of the models are compared as forecast difficulty levels (FDLs). The range of FDLs among the basins is >2:1, with the more difficult basins having the more poleward-average storm latitudes.

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CHARLES J. NEUMANN
and
JOHN R. HOPE

Abstract

The HURRAN (hurricane analog) technique, a fully computerized objective forecast aid making use of past tracks in forecasting hurricane motion, was developed prior to the 1969 hurricane season. Encouraging operational results during the 1969 and 1970 hurricane seasons suggested further evaluation of the technique. To this end, HURRAN computations were made for approximately 1,000 forecast situations. Results are stratified according to initial direction and speed of movement of the sample storms and the number of analogs selected. The utility of the technique is discussed, and the importance of position accuracy at forecast time is demonstrated. Initial indications of the value of the technique are substantiated.

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Daniel S. Wilks
,
Charles J. Neumann
, and
Miles B. Lawrence

Abstract

U.S. National Hurricane Center (NHC) forecasts for tropical cyclone tracks and wind speeds are extended in time to produce spatially disaggregated probability forecasts for landfall location and intensity, using a weighted bootstrap procedure. Historical analogs, with respect to the forecast characteristics (location, heading, and wind speed) of a current storm, are selected. These are resampled by translating their locations to random positions consistent with the current forecast, and recent NHC forecast accuracy statistics. The result is a large number of plausible Monte Carlo realizations that jointly approximate a probability distribution for the future track and intensity of the storm. Performance of the resulting forecasts is assessed for U.S. tropical cyclone landfall probabilities during 1998–2006, and the forecasts are shown to be skillful and exhibit excellent reliability, even beyond the 120-h forecast horizon of the NHC advisory forecasts upon which they are based.

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Charles J. Neumann
,
Miles B. Lawrence
, and
Eduardo L. Caso

Abstract

Use of the F test in assessing the statistical significance of a regression equation developed from meteorological data and using the concept of stepwise screening of predictors presents problems in determining degrees of freedom. Some of these problems relate to characteristics of the data. The main problem, however, is the result of making a large number of predictors available to a screening program and retaining only a few. This adds an additional play of chance not ordinarily accounted for in the usual application of the F test. Unless proper compensation is made to degrees of freedom, the variance ratio is overestimated or underestimated, and a prediction equation can be judged significant when it is not, or not significant when it is. The derivation of a test-statistic to avoid this pitfall in the development of statistical models for the prediction of tropical cyclone motion is the subject of the present paper.

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Harold L. Crutcher
,
Charles J. Neumann
, and
Joseph M. Pelissier

Abstract

This study focuses on the use of the bivariate normal distribution model to describe spatial distributions of tropical cyclone forecast errors. In this connection, it is found that forecast errors from the entire Atlantic tropical cyclone basin (Gulf of Mexico, Caribbean and North Atlantic) are multimodal and the fitting of these collective data to the usual unimodal bivariate normal distribution will be judged invalid by the usual statistical goodness-of-fit tests. While this is a recognized pitfall in classical statistics, it is often overlooked in meteorological application. The isolation of the clusters (components) and their statistical characteristics permit the issuance of forecast positions accompanied by more representative error ellipses.

The study continues with a bivariate clustering analysis of a set of 979 tropical cyclone 24 h forecast errors for the Gulf of Mexico, Caribbean and North Atlantic. These errors were collected from the entire tropical cyclone basin without regard to season or geography. The analysis shows that these errors could be drawn from two or possibly three parent bivariate normal distributions. A further analysis of the two clusters was made and it is shown that group membership is essentially a function of forecast “difficulty.” One group (essentially storms located in the Caribbean and Gulf of Mexico) has about one-half the component standard errors of the other group (the more northerly storms). A physical interpretation of the more complex three-mode clustering was not accomplished.

The study has application with regard to the future development of statistical prediction models and in connection with a recently inaugurated tropical cyclone “strike” probability concept.

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Kenneth R. Knapp
,
Michael C. Kruk
,
David H. Levinson
,
Howard J. Diamond
, and
Charles J. Neumann

The goal of the International Best Track Archive for Climate Stewardship (IBTrACS) project is to collect the historical tropical cyclone best-track data from all available Regional Specialized Meteorological Centers (RSMCs) and other agencies, combine the disparate datasets into one product, and disseminate in formats used by the tropical cyclone community. Each RSMC forecasts and monitors storms for a specific region and annually archives best-track data, which consist of information on a storm's position, intensity, and other related parameters. IBTrACS is a new dataset based on the best-track data from numerous sources. Moreover, rather than preferentially selecting one track and intensity for each storm, the mean position, the original intensities from the agencies, and summary statistics are provided. This article discusses the dataset construction, explores the tropical cyclone climatology from IBTrACS, and concludes with an analysis of uncertainty in the tropical cyclone intensity record.

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