Search Results

You are looking at 1 - 10 of 11 items for :

  • Author or Editor: Edward S. Epstein x
  • Journal of Applied Meteorology and Climatology x
  • Refine by Access: All Content x
Clear All Modify Search
Edward S. Epstein

Abstract

The problem of decision making in applied meteorology is approached from the point of view of decision theory and subjectivist statistics. The modern concept of “utility” is discussed, and optional rules for decision making based on the availability of a limited amount of meteorological data are presented and discussed. Bayes' theorem forms the basis for the statistical estimation of the frequencies of various alternative weather events. The method is applied to a single example for the purpose of illustration, but it is emphasized that the generality of these techniques is great and that they warrant further study.

Full access
Edward S. Epstein

Abstract

Full access
Edward S. Epstein

Abstract

The likelihood ratio of the data for a hypothesis of some change, relative to the hypothesis of no change, is a suitable statistical measure for the detection of climate change. Likelihood ratios calculated on the basis of Angell and Korshover's (1977) global mean temperature, updated through 1980, do not show convincing evidence of recent climate change. It is possible to calculate probabilities of obtaining future values of likelihood ratios, depending on the postulated future climate change. A modest but significant climate change, such as that expected to occur from an increase of atmospheric carbon dioxide, is likely to be detected from global mean surface temperatures within ten years. The joint behavior of the troposphere and stratosphere is more likely to discriminate between climate change and no change than are surface temperatures. In this case, a climate change that can be attributed to carbon dioxide increase should be detectable by 1986.

Full access
Edward S. Epstein

Abstract

Full access
Edward S. Epstein

Abstract

Full access
Edward S. Epstein

Abstract

When the initial values, or the parameters, of prognostic equations are not known with certainty, there must also be errors in the solution. The initial conditions may be represented by an ensemble, each member of which is consistent with all available knowledge. The mean of this ensemble is a reasonable "best" solution to the prognostic equation. Following Gleeson, we have examined the behavior of the error in the forecast, as represented by the rms deviation of the ensemble members from their mean, for a few simple equations. We have further examined the time-dependent behavior of the ensemble mean, as opposed to the solution obtained by applying the prognostic equation to the original mean values. These are, in general, different. It is concluded that optimum procedures for forecasting, i.e., solving prognostic equations, require includingterms in the equations to represent the influence of the initial uncertainties. Since the nature of these uncertainties may also have profound influences on the error of the forecast, this aspect, too, must be taken into consideration.

Full access
Edward S. Epstein

Abstract

Full access
John A. Leese
and
Edward S. Epstein

Abstract

Pictures from TIROS have revealed that cumuliform clouds over the ocean quite often occur in the form of a complex pattern with lines and cells of horizontal dimensions in the range of 20 to 100 miles and with various orientations seemingly superimposed. Gross features of these patterns such as the horizontal dimensions and orientation which are predominant over particular areas should prove very useful as indicators of the prevailing meteorological conditions.

A two-dimensional extension of the familiar power spectrum analysis has been applied to the TIROS photographs. The object of the analysis is to identify and quantify the statistically preferred dimensions and orientations of the cloud patterns. Digital data are derived from the photographs by adopting a simple 5-level gray scale of cloud brightness. A Monte Carlo technique has yielded estimates of the reliability of the statistical analysis.

Results from these analyses have revealed patterns which tended to be obscured by the more dominant features in addition to cloud patterns which were obvious in the original picture. The method clearly enables one to distinguish different types of cloud patterns and offers a quantification of the pictures which should aid in the interpretation and utilization of the TIROS photographs.

Full access
Edward S. Epstein
and
Allan H. Murphy

Abstract

Full access
Allan H. Murphy
and
Edward S. Epstein

Abstract

The evaluation process is considered in some detail with particular reference to probabilistic predictions. The process consists of several ordered steps at each of which elements (of the process) are identified. Consideration of the purposes leads to the identification of two distinct forms of evaluation: operational evaluation concerned with the value of predictions to the user and empirical evaluation, or verification, concerned with the perfection of predictions, i.e., the association between predictions and observations. Attributes, i.e., desirable properties, of predictions are defined with reference to these purposes, and a number of measures of the attributes for empirical evaluation are considered. An artificial example of comparative verification in which different measures appear to yield contradictory results is used to demonstrate the importance of, and need for, a careful analysis of the evaluation process.

Full access