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Nathaniel B. Guttman

Abstract

Daily maximum and minimum temperature changes in January over 112 recorded years at Central Park, New York, were analyzed from a statistical viewpoint. The case study reveals that time series of mean and median changes are not smooth, that the data are highly variable, that a longer period of record is needed to develop stable statistical distributional relationships, and that there is a tendency, but not conclusive evidence, for a cooling event around 8 January and a “January thaw” around 25 January.

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Nathaniel B. Guttman

Abstract

Regional and national heating fuel demand is related to both weather and population density. This study analyzes the variability of population-weighted, seasonal heating degree days for the coterminous 48 states. A risk assessment of unusual weather-related beating energy demand, based on a Gaussian distribution model, is provided. Nationally, the 1970–80 population change reduced heating, but increased cooling demand. The net savings on total national heating and cooling costs are ∼$0.5 billion, or 1%.

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Nathaniel B. Guttman

An adequate description of climate is required to meet the informational needs of planners and policy-makers who use climate as a factor in their decision-making processes. Because normals have become firmly entrenched as a descriptor of climate, their history and their perception by the public are discussed. An “exploratory data analysis” approach is suggested.

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Nathaniel B. Guttman

Abstract

As part of a national study of water management during periods of drought, the U.S. Army Corps of Engineers is underwriting the preparation of a national drought atlas. One of the variables being analyzed for the atlas is precipitation. A statistical technique known as L-moments is the basis for the analysis. Central to the L-moment technique is the aggregation of site-specific precipitation data into homogeneous regions. This paper concerns a methodology for defining regions of similar precipitation climates that are homogeneous with respect to the statistical distribution of annual precipitation. Included are a discussion of the data, of the necessity for regionalization, and of the iterative use of clustering and an L-moment-based homogeneity test to determine the regions.

The methodology resulted in 104 precipitation regions within the continental United States. The number of stations in each region varied from 1 to 97. Problems were encountered mainly in mountainous and in and areas. They were, however, resolved in all but three regions by examining the orography and / or the data.

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Nathaniel B. Guttman

Abstract

Parametric probability distributions can be fit to a dataset by equating sample L moments to those Of the fitted distribution. This study examines the mean and mean squared departures of sample L moments of monthly precipitation data from large sample values as sample size increases. Mean departures decrease as the sample size increases with values near zero generally occurring with about 30 to 40 or more observations for the central tendency measure, about 40 to 50 or more for the dispersion measure, and about 60 or 70 for the skewness and kurtosis measures. It was also found that the root-mean-square departures appear to decrease linearly with the square root of the sample size. The results are intended to provide guidance for determining sample sizes when applying at-site L moments to monthly precipitation data.

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Nathaniel B. Guttman and Richard L. Lehman

Abstract

Degree-hours have many applications in fields such as agriculture, architecture, and power generation. Since daily mean temperatures are more readily available than hourly temperatures, the difference between mean daily degree-hours computed from daily mean temperatures and those computed from hourly data is examined.

Mean daily degree-hours were modeled assuming normal probability distributions for temperatures and homogeneous variances of hourly temperatures throughout a day. The validity of the assumptions, which is dependent upon time of year and location, as well as the effect of the assumptions on four models of daily degree-hours are discussed. Two of the models require mean hourly temperatures and two require the readily available daily mean temperatures as input. Comparisons among models and observed data show that estimates made from mean hourly temperatures are better than those made from daily mean temperatures. The difference is sizable during the transition months between warm and cool seasons. An aid to computing the difference is presented.

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Nathaniel B. Guttman and Robert G. Quayle

Abstract

The network of about 5600 cooperative stations in the United States provides the baseline temperature data upon which most climatologies are based. Since the data serve between 40 000 and 50 000 primary users each year, and untold secondary users, data review, a part of data quality control, is an important consideration. This paper reveals the magnitudes of data changes made during different levels of data review efforts. With a full-scale effort, only about one-half of one percent of all values are altered in some way. However, the magnitude of daily changes can be large, thereby affecting the computation of monthly statistics and affecting users' needs for daily data.

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Nathaniel B. Guttman and Marc S. Plantico

Abstract

The published 1951–80 daily normals of maximum and minimum temperatures were prepared by interpolating between average monthly values. This study compares the published normal and 30-yr average daily temperatures in the eastern half of the United States. It was determined that the published normals statistically differ from the series created by using daily data. It was also determined that 1-day persistence is a feature of the daily data. The possibility of climatic singularities as evidenced from the analysis of 30 temperature (1951–80) on selected dates became apparent and warrants further investigation.

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Nathaniel B. Guttman and Marc S. Plantico

Abstract

Guttman and Plantico reported on an additive model to describe daily temperature climates. This note reports on spectral analyses of the nonrandom residuals from the model. We concluded that quasi-periodic features are not present in the 1951–80 residual maximum and minimum temperature data.

Two areas of search for model components are suggested for future research. First, apparent singularities should be investigated and mathematically described. Second, nonstationary patterns over the period of record that may be linked to long term climatic variability should be understood and modeled.

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Nathaniel B. Guttman and Robert G. Quayle

The history of climatic divisions in the contiguous United States has been pieced together from fragmentary documentation. Each of the 48 contiguous states has been subdivided into climatic divisions. Divisional boundaries are now standardized, and a set of climatic variables for time-invariant divisional boundaries has been compiled for the period of record beginning in 1895. This paper documents the origins of climatic divisions, the computational methodology of an area-invariant divisional dataset maintained by the National Climatic Data Center, and the strengths and weaknesses of divisional data.

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