Search Results

You are looking at 1 - 10 of 15 items for

  • Author or Editor: Nathaniel B. Guttman x
  • Refine by Access: All Content x
Clear All Modify Search
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.

Full access
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.

Full access
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.

Full access
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%.

Full access
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.

Full access
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.

Full access
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.

Full access
Nathaniel B. Guttman and Richard K. Jeck

Abstract

Radiosonde temperature and humidity data were used to deduce the vertical distribution of clouds and aircraft icing conditions near Washington, D.C. when low ceilings occurred with surface temperatures near freezing. Twenty-three soundings from 12 cold, low ceiling episodes during the winter of 1981/82 were examined for this study.

Results indicate the following: (a) generally, a deep, apparently unbroken cloud layer existed above the low ceilings; (b) typically, a cold surface layer existed under a relatively strong inversion; (c) while icing conditions above the cold, low ceilings are mitigated by inversion or isothermal layers, 70% of the cases still required flight into significant icing conditions; and (d) local geographic effects can noticeably influence ceiling height and visibility. Automated predictions of the icing probability, type, and.severity were generated from the radiosonde data and were supplemented with pilot reports; reasonable agreement was found.

Full access
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.

Full access
Nathaniel B. Guttman and C. Bruce Baker

The Automated Surface Observing System is currently replacing conventional observations at the National Weather Service, the Federal Aviation Administration, and other stations that report hourly observations. From a climatological viewpoint, it is necessary to compare the data from the old and new measuring systems in order to gain an understanding of their differences. These differences may become important when using time series for applications such as the computation of climatic normals, the development of homogeneous datasets for long periods of record for the investigation of climatic change, the placing of events into historical perspective, or the analysis of extreme weather events. This exploratory study of temperature data was undertaken to determine first whether there is a data continuity problem between the two observing systems and second, if there is a problem, to identify the magnitude of the problem. The most important conclusion from this study is that differences in site characteristics, even at the same airport, play as much, if not more, of a role in assessing the comparability of measurements from the two observing systems as does the instrument system bias. The instrument bias at most stations is on the order of a few tenths of a degree Fahrenheit, but the siting differences can lead to biases on the order of a couple of degrees. Not only is there a difference in the magnitude of the biases, but there is also a difference in the direction; the instrument bias is usually negative, but the siting biases can be either positive or negative.

Full access