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Charles J. Neumann

Abstract

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Charles J. Neumann

Abstract

One of the major problems concerning meteorologists at the Kennedy Space Center, Fla, involves the forecasting of thunderstorm activity and associated adverse weather phenomena. The purpose of the study is to outline some of the more successful diagnostic tools which have been developed to aid the forecaster. These involve a variety of statistical procedures including conditional probabilities, exposure-period probabilities, and systems of multiple-regression equations based on nonlinear predictors.

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Charles J. Neumann

Abstract

The occurrence of large surface hail is extremely rare in low latitudes. In an effort to explain this deficiency, this paper presents a mesoscale analysis of an isolated case of large hail over Miami, Florida, in March 1963. For this analysis, a dense network of hail size and frequency sensors was conveniently provided by the hall-punctured overhead portions of the many screened patio and swimming pool enclosures which are part of the Miami environment. A study of this damage pattern along with considerable mesosynoptic data on pressure, rainfall and wind revealed intimate details of the storm's behavior and showed that it displayed many of the features generally associated with Midwest tornadic hailstorms.

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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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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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