Natural Resources, School of


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A Thesis Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Natural Resource Sciences, Under the Supervision of Professors Kenneth G. Hubbard and David B. Marx. Lincoln, Nebraska: July, 2011

Copyright 2011 Andrea J. Coop


Climate data has become increasingly scrutinized for its accuracy because of the need for reliable predictions about climate change. The U.S. has taken great strides to keep up with the demand for accurate climate data. Over the last thirty years, vast improvements to instrumentation, data collection, and station siting have created more accurate data records. This study is to explore the accuracy of existing networks. This study analyzes three climate networks used in Nebraska: the U.S. Historical Climatology Network (HCN), the Automated Weather Data Network (AWDN), and the newest network, the U.S. Climate Reference Network (CRN). Each of these networks has its own instrumentation, collection methods and station sites. Maximum and minimum surface temperature from the three networks and the spatial structure of temperature variations at the surface are compared. Two different timeframes, 2005-2009 and 1985-2005, were used to include the newest network, CRN, in the analysis. Daily data were collected from each of these networks within the specified timeframe. Root mean square error (RMSE) between each candidate station and the surrounding stations within 500 kilometers were calculated and evaluated to determine spatial accuracy of the network. This study found that in the 5 year analysis, CRN versus AWDN, the two networks were not significantly different enough to denote the network with high spatial accuracy. For the 21 year analysis, HCN versus AWDN, AWDN stations showed higher spatial accuracy (smaller error) than HCN stations for the variable of maximum temperature. The error for the two networks were not significantly different enough to decipher the network with the higher spatial accuracy.

Advisors: Kenneth G. Hubbard & David B. Marx