An Analysis of Urban Heat Islands in Kentucky

The purpose of this research is to increase understanding of the Urban Heat Island (UHI) effect in Kentucky by studying its three largest cities:  Louisville, Lexington, and Bowling Green.  By examining the UHIs of these three cities, three major attributes can be determined:  if there is a relationship between the size of the city by population and the UHI magnitude, how UHI magnitude has been changing for the past five years, and if UHI magnitude follows any daily and/or seasonal cycles.

Data will be collected from weather stations within the three major cities, as well as from weather stations located in the rural areas surrounding them.  The length of the time series being considered is from 12/01/2009 through 11/30/2014.  Urban stations are from the Automated Surface Observing System (ASOS) and the Cooperative Observer Program (COOP), both of which are quality-controlled weather data collection networks operated by the National Weather Service (NWS).  Rural stations are from the Kentucky Mesonet (KYMN), which is a mesoscale weather and climate observing network operated by the Kentucky Climate Center at WKU.  This is the only world-class research-grade network to operate in Kentucky.

Daily maximum and minimum temperature data, as well as monthly maximum and minimum temperature data will be obtained for each of the weather stations.  The pairwise difference between urban and rural observations is calculated, resulting in the UHI magnitude for each city.  The analysis and visualization is conducted using MATLAB, a sophisticated computing software.  These results will further help to determine if there is a relationship between city size and UHI magnitude and demonstrate diurnal or seasonal cycles of UHI.

In addition to graphs showing the general trends of each city’s UHI magnitude, individual case studies of extreme UHI magnitude events will be displayed.  This will help with understanding of UHI magnitude for each of the cities.  This data will be paired with air mass and precipitation data to create a more comprehensive view of the event.  This information can be useful to, for example, weather forecasters and urban emergency managers, as it may give insight into what conditions are most likely to result in heat strokes and heat exhaustion.