Solar radiation is fundamental to ecological processes and energy production. Despite growing networks of meteorological stations, the spatial and temporal variability of solar radiation remains poorly characterized. Many solar radiation models have been proposed to enhance predictions in areas without measurement instrumentation. However, these models do not fully take advantage of the increasing number of data collection sites, nor are they expandable to incorporate additional meteorological information when available. In this dissertation we: 1) developed a method of statistical analysis to summarize and communicate solar radiation reliability, 2) applied a beta regression model to leverage auxiliary meteorological information for enhanced solar radiation prediction, 3) refined the beta regression model and considered spatial auto-correlation to better predict solar radiation across space, 4) extended and evaluated these methods in a mountainous region. These advancements in the characterization and prediction of solar radiation are detailed in the following chapters of this dissertation.
|Advisor:||Marshall, Lucy A., McGlynn, Brian L.|
|Commitee:||Higgs, Megan D., Stoy, Paul C.|
|School:||Montana State University|
|Department:||Land Resources and Environmental Sciences|
|School Location:||United States -- Montana|
|Source:||DAI-B 73/11(E), Dissertation Abstracts International|
|Subjects:||Alternative Energy, Atmospheric sciences, Environmental science|
|Keywords:||Beta regression, Fraction of clear day, Montana, Solar radiation|
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