Master's Theses

Date of Award

Spring 2018

Degree Name

Master of Science (MS)




Dr. Paul Dixon


Identifying trends in aspects of meteorology is becoming increasingly important to understanding how climate can be expected to change, and how those affected may plan contingencies. Analyzing spatial patterns of precipitation trends allows for associations to be discovered to better understand regional climatology. For this study, daily precipitation data were collected from The National Oceanic and Atmospheric Administration (NOAA) Global Historical Climate Network (GHCN) at stations across the continental United States, with selection based on distance from each other within a state, as well as percent completeness of observation data. Two stations per state were selected, with some exceptions for smaller states. The data were organized by year, and six different variables were examined for each station. Mean annual precipitation per event, annual standard deviation, frequency of days with more than 0.5 inches of precipitation, frequency of days with more than 1.0 in. of precipitation, annual 90th percentile value, and frequency of days with precipitation amount greater than the 90th percentile value for the entire period were tested for trends with a Mann-Kendall trend test. The stations were then mapped to identify the regions where trends were identified. Over 546 trend tests, there were 122 positive trends and 11 negative trends detected. Hot spots in both positive and negative trends were detected, and there were statistically hot spots in each of the six variables.


Copyright 2018 Shayne O'Brien


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