The Analysis_Scripts_Climate_TimeSeries directory contains three R Scripts. The R Scripts were used to do Exploratory Data Analysis (EDA) and Extreme Event Analysis (EVA) on climate data obtained from ground-based climate stations and projected climate values from medium and high emission climate conditions. The user can use the scripts for preliminary analysis of time series climate data. The extreme event analysis was done for the selected parameters, and details on different types of climate analysis used in the script can be seen in Mehan et al., (2017). The user can modify the script to meet their specific requirements. The script was made for specific stations and climate projection scenarios listed in Mehan et al., (2017). For ease of use, detailed comments are contained within the script. The user should change the names of the stations as required in order to avoid any errors based on file names. More information on extreme events can be found at https://www.ncdc.noaa.gov/climate-information/extreme-events.
Climate data for sixteen different climate stations in Western Lake Erie Basin (WLEB) was generated as a part of the dissertation by Sushant Mehan, Ph.D. Candidate in the Purdue University Department of Agricultural and Biological Engineering, under the guidance of Dr. Margaret W. Gitau. All related datasets for this dissertation may be accessed here: http://doi.org/10.4231/R70000B4.
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These scripts were prepared for the specific purpose listed in Mehan et al., 2017. The scripts are provided “as is”. Users are free to edit or modify the scripts for their specific requirements provided they credit the original work. Users are responsible for verifying the accuracy of their results. Everyone is allowed to copy, modify, distribute, and perform the work using the dataset. The authors make no warranties and are not liable for any errors arising out of or in connection to access or use of the dataset and associated materials.
Mehan, S., Guo, T., Gitau, M. W., & Flanagan, D. C. (2017). Comparative study of different stochastic weather generators for long-term climate data simulation. Climate, 5(2), 26.