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A Model for Estimating Persistence Probabilities

Iver A. LundAir Force Geophysics Laboratory, Hanscom AFB, MA 01731

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Paul TsipourasAir Force Geophysics Laboratory, Hanscom AFB, MA 01731

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Abstract

A model for estimating the probability that a weather event will persist for at least n hours was developed on 13 years of hourly wind-speed data taken at nine stations in the eastern United States and tested on data at seven stations in the central United States. Probability estimates obtained from the model were generally in good agreement with empirical probabilities obtained from the 13 years of hourly wind-speed data. In order to apply the model it is necessary to know the unconditional probability of the event at the geographical location of interest and to know two parameter values.

Parameter values were also determined for two categories of sky cover, ceiling, visibility and temperature and for precipitation. Probability estimates obtained from the model, using a single pair of parameter values for each weather event, were in good agreement with empirical probabilities obtained from 13 years of hourly data, of these weather elements also. The model and parameter values given in this paper should be useful in estimating persistence probabilities for any location where the unconditional probability of the event of interest is known.

Abstract

A model for estimating the probability that a weather event will persist for at least n hours was developed on 13 years of hourly wind-speed data taken at nine stations in the eastern United States and tested on data at seven stations in the central United States. Probability estimates obtained from the model were generally in good agreement with empirical probabilities obtained from the 13 years of hourly wind-speed data. In order to apply the model it is necessary to know the unconditional probability of the event at the geographical location of interest and to know two parameter values.

Parameter values were also determined for two categories of sky cover, ceiling, visibility and temperature and for precipitation. Probability estimates obtained from the model, using a single pair of parameter values for each weather event, were in good agreement with empirical probabilities obtained from 13 years of hourly data, of these weather elements also. The model and parameter values given in this paper should be useful in estimating persistence probabilities for any location where the unconditional probability of the event of interest is known.

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