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James D. McQuigg
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James D. McQuigg and Wayne L. Decker

Abstract

If an outdoor job were being planned in an area which never received precipitation, all facilities could be scheduled with assurance that work could proceed to completion without interruption because of precipitation. In practice, many an outdoor job has been scheduled, only to be delayed several times before actual completion. It would be useful to be able to estimate the probability of completing a job as scheduled. A mathematical model is developed which includes:

(1) A scheduled beginning date,

(2) An amount of precipitation in one day that would interrupt a job,

(3) The number of days of “drying out” time required,

(4) The number of work days required.

For a particular place and job, the problem is to estimate the probability that a run of dry days will occur during a particular period of time. This could be estimated by counting runs in a sample period, or computed by several methods. In this study probabilities were computed by an approximation technique which yielded estimates comparable to those from counts of actual runs. Probability estimates for different beginning dates and lengths of drying and work periods are presented.

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M. Lawrence Nicodemus and James D. McQuigg

Abstract

A simulation model is presented which hopefully is consistent with known physical and statistical properties of atmospheric events, and consistent with criteria that might be applied in the management of actual experiments in the atmosphere. The process being simulated is the possible modification of daytime surface temperatures during the summer in central Missoui through the generation of contrail cirrus clouds. Monte Carlo techniques are used in the model to allow for the likelihood of failure of the experiment on any particular day, and to allow for variations in the degree of success on days when the experiment is considered to not be a failure.

The model is applied to an observed time series (1946-1965) of surface and upper air observations from Columbia, Mo. Estimates of the results are based on analysis of the relationship between temperatures on the cirrus and cirrus-free days. If it can be assumed that it is possible to create enough contrial cirrus to reduce the per cent of possible sushine from 15-35%, it appears that it might be possible at reduce daily maximum temperatures by from 3-5F on about half of the days when soil moisture values are below "desirable" levels of when temperatures are expected to be above some "critical" level

This is a relatively inexpensive way to estimate the order of magnitude of the effect of weather modification, compared to the cost of conducting an actual experiment over a long period of time.

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S. R. Johnson, James D. McQuigg, and Thomas P. Rothrock

Abstract

The electric power industry has long been known to be sensitive to weather events. In particular, daily temperatures in distribution areas are known to affect electric power consumption. In this paper the relationship between power consumption and daily temperatures is estimated using simple regression techniques. The resulting relationships permit an investigation of the consequences of temperature modification for 14 midwestern electric power production companies. Comparisons between power production costs for observed and modified historical and experimentally generated temperature series suggest that changes of 3–5F in average daily temperature can reduce costs substantially. Exact differentials in production cost which can be attributed to temperature modification are presented so as to be useful in firm, industry and public policy decisions.

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EMIL D. ATTANASI, S. R. JOHNSON, SHARON LeDUC, and JAMES D. McQUIGG

Abstract

Certain activities of highway construction are particularly sensitive to such weather conditions as soil moisture, precipitation, and daily temperature. Regression analysis is used to obtain three alternative probability models designed to translate observed weather conditions into probabilities for carrying out construction activities. The models were developed using generalized least squares, normit analysis, and logit analysis. The generalized least squares method was the most convenient computationally, but it had severe interpretative disadvantages. The results obtained by logit analysis gave the desired probabilistic interpretation most readily and had the best predictive ability. Comparison of sample observation and predicted work probabilities for common excavation during wet and dry months indicated that the logit analysis model could accurately translate weather conditions into probabilities that work would take place. Models for paving and asphalt work and for bridge and drainage structure are also estimated using logit analysis. These estimates indicate a strong sensitivity of the latter category of work to precipitation conditions. Such models may aid contract letting agencies is planning payment schedules, penalty clauses, and completion dates for new roads; construction firms may find such models valuable in planning effective use of men and equipment.

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