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Dennis M. Driscoll

Abstract

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James E. Hansen
and
Dennis M. Driscoll

Abstract

A stochastic model for hourly temperatures for Big Spring, Tex., has been developed. The governing parameters were deduced from an 11-year developmental sample, and give hourly temperatures as a function of harmonics representing annual and diurnal variations, and a first-order Markov chain process. The latter incorporates adjustments for the seasonal variation of the serial (hour-to-hour) correlation coefficient, and for the seasonal and diurnal variations of the variability and non-normality of frequency distributions of hourly temperatures. Each of the characteristics is given explicitly as a function of hour of the year.

Two 10-year samples were generated and compared to the developmental sample. Criteria were established to determine how well the model duplicates nature. The variability of mean monthly temperature and the frequency of occurrence of low diurnal ranges are underestimated. However, the model gives good estimates of the duration of temperatures below 32°F, and above 65° and 90°F, and of the frequency distribution of monthly 3, 6, 12, 24, 72 and 144 h maximum and minimum temperatures.

The general applicability of the model and its utility are discussed. The model could be used to determine the effects of climatic trends, e.g., a gradual cooling, on the average length of the growing season, the mean number of heating/cooling degree days, and other temperature-related parameters.

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Judson W. Ladd
and
Dennis M. Driscoll

Abstract

An objective weather typing scheme first proposed by Christensen and Bryson (1966) was applied to surface and upper air variables for the period April–September of 1973–76 at Midland, Texas. Principal components analysis showed that moisture and temperature, which were represented by the first and second components, respectively, are most important in distinguishing day-to-day weather, while synoptic variables such as wind and pressure are relatively unimportant.

The days of the study period were then assigned to weather types after applying multiple regression analysis and an objective grouping method. The principal disadvantages of the latter procedure are the large number of untyped days and the relatively few days assigned to types after the second. Suggestions for improving this grouping method are offered.

Surface and 500 mb charts for the same period were examined and each day was typed according to a method specifying surface and upper air synoptic features. The two methods were then compared. The correspondence is very general because the variables manifest in synoptic representation, pressure and wind, are of only secondary significance in the principal components. Air mass changes are therefore more important in distinguishing day-to-day weather than are synoptic controls; this applies less so at the beginning and end of the convective season than in the middle of it.

In a specific application of both methods to convective activity levels as inferred from the number of initial echoes per day, the greater distinction was made by the objective method, with highest levels occurring in the warmest and most moist types.

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