This document describes a database containing monthly temperature and precipitation data for 1221 stations in the contiguous United States. This network of stations, known as the United States Historical Climatology Network (US HCN), and the resulting database were compiled by the National Climatic Data Center, Asheville, North Carolina. These data represent the best available data from the United States for analyzing long-term climate trends on a regional scale. The data for most stations extend through December 31, 1994, and a majority of the station records are serially complete for at least 80 years. Unlike many data sets that have been used in past climate studies, these data have been adjusted to remove biases introduced by station moves, instrument changes, time-of-observation differences, and urbanization effects. These monthly data are available free of charge as a numeric data package (NDP) from the Carbon Dioxide Information Analysis Center. The NDP includes this document and 27 machine-readable data files consisting of supporting data files, a descriptive file, and computer access codes. This document describes how the stations in the US HCN were selected and how the data were processed, defines limitations and restrictions of the data, describes the format and contents of the magnetic media, and provides reprints of literature that discuss the editing and adjustment techniques used in the US HCN.

This document describes a data base containing daily observations of maximum and minimum temperature and precipitation amounts from 138 US stations. These stations are a specially chosen subset of the 1219-station US Historical Climatology Network (HCN), compiled by the National Climatic Data Center (Asheville, North Carolina). The daily data network (herein referred to as the HCN/D) consists of stations considered to be the best of those from the HCN, selected to provide reasonably homogeneous spatial coverage of the contiguous US after considering the temporal homogeneity of each station's observing times, instrument types/positions, and surroundings. The data for each station extend through 1987, and most station records are complete for at least 80 years. The daily resolution of these data lends maximum flexibility for studies attempting to detect and monitor long-term climatic changes on a regional scale. Studies using daily data may be able to detect changes in regional climate that would not be apparent from analysis of the more commonly used monthly temperature and precipitation data. Such studies may include analyses of trends in maximum/minimum temperatures, temperature extremes, daily temperature range, precipitation ''event size'' frequency, and the magnitude and duration of wet and dry periods. Other applications of the data include planning and risk assessment in areas such as agriculture, natural resource exploration, and construction. This document describes how the stations in the HCN/D were selected, defines limitations and restrictions of the data, describes the format and contents of the magnetic tape, and provides reprints of literature pertinent to the collection and application of daily climate data.

This thesis studies anomalies and error estimates for the contiguous United States temperature and precipitation data from the U.S. Historical Climatology Network (Version 2) in the period of 1895-2010. Statistical inference procedures including Welch's t-test and non-parametric methods are presented in the context of changes of climate regimes and extremes. Four temperature and two precipitation regimes have been identified and used to analyze the US climate variations from 1895 to 2010. Analyzed are time series of temperature and precipitation, probability densities and statistical moments for the different climate regimes. Further, spatial distribution of the anomalies and errors are considered to determine the impact on different regions of the US from extreme temperature and precipitation. A ranking of the top ten extremist years is established. Drought impact from climate extremes is analyzed for Dust Bowl period and the warming after 1998. Main findings are the rigorous justification of significant differences of the different climate regimes and different climate extremes. The analysis also explains some physical causes of the frequency and spatial distribution of extreme climate anomalies, especially for droughts in the 1930s, 1950s, and after 1998.

This thesis addresses two approaches to describe the complexity of US climate data. The basis for our analyses is provided by a partition of the contiguous United States into 120 grid boxes of size 2:5° x 3:5° by latitude and longitude, respectively. The first part deals with the determination of spatial degrees of freedom (DOF) for monthly United States Historical Climatology Network, version 2 (USHCN V2) temperature and precipitation data from 1961 to 1990. The DOF can be interpreted as the effective number of statistically independent spatial locations of a climate field. The X2 and S methods are used to estimate the DOFs for each month and four different data sets. Estimates from the S method suggest that around two to six grid boxes suffice to explain temperature data while precipitation data is more complex and requires 6 to 30 grid boxes. The X2 method apparently underestimates the DOF in most cases but agrees with the S method in that climate in the summer months is affected by more independent influences than in the winter months. Based on the results from the S method three different methods are used to find the supposedly most independent subsets of grid boxes. Each method computes a value of interest for all underlying subsets. The X2 and S DOF maximization methods find the subset with the highest DOF value from the respective DOF estimation method if only data from the considered subset is used for estimation. The correlation minimization method minimizes a so-called correlation value. By visual judgment most of the determined subsets depict a fairly uniform distribution. The second part starts by discussing theory on the Akaike information criterion (AIC) and Bayesian information criterion (BIC) and gives an example of their application to a linear regression model. Subsequently an optimal average method is introduced which serves to determine optimal weights for empirical orthogonal functions (EOF) in the approximation of mean squared errors (MSE) between theoretical values of temperature anomaly averages and a discrete sum to compute them in practice using the partition of the contiguous US into grid boxes. The AIC and BIC criteria are applied to MSEs computed for USHCN V2 temperature data from 1897 to 2010 in an attempt to determine optimal modes, i.e. cutoff points for the MSE approximation. Based on the results an inconsistency in the theory is discovered: the idea of MSEs considered in the optimal average method doesn't correspond to MSEs in a regression model. Thus AIC and BIC are not appropriate to determine the optimal modes.