Seasonal Variations of the Urban Heat Island Effect:

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1 Seasonal Variations of the Urban Heat Island Effect: Examining the Differences in Temperature Between the City of Philadelphia and its Outlying Suburbs By: Frank Vecchio 1 P a g e

2 We re calling for a high of 85 in the city and highs in the upper 70 s to lower 80 s in the suburbs. Temperatures should dip down to around 53 tonight in the city and into the 40 s in the suburbs. This may sound familiar. This phenomenon is known as the Urban Heat Island Effect. The purpose of this study is to determine how the Urban Heat Island Effect in the City of Philadelphia has changed seasonally over the last decade, as well as if the increasingly higher population of the suburbs has created a lessening of the contrast between urban temperatures and nearby rural temperatures. Image analyses using Infrared Satellite images which detect how much outgoing longwave radiation is present in a given area via the Global Visualization Tool from the United States Geological Survey, as well as a statistical analysis using temperature validations from the Pennsylvania State Climatologist website were used to determine this. After thoughtful analyzing and investigation of the entirety of the data collected, I came to the conclusion that the urban heat island effect is more governing during the warmer months of the year compared to the cooler months. In addition, I determined that there is a good possibility that the escalating population of the surrounding suburbs has created a lessening of the Urban Heat Island Effect (the distinction of temperatures between Philadelphia and its suburbs may be decreasing). In rural areas, during the day, the solar energy absorbed near the ground evaporates water from the vegetation and soil. Therefore, while there is a net solar energy gain, this is reimbursed to some degree by evaporative cooling. However, in cities, where there is much less vegetation, the buildings, streets and sidewalks absorb the majority of solar energy input. After this occurs, the heat gained at the surface will be radiated back into space in the form of longwave radiation and also as sensible heat, rather than latent heat. 2 P a g e

3 This paper will examine the differences by season and by year in how the temperature of the City of Philadelphia and its outlying suburbs have changed (increased, decreased, etc.) over the past 10 years. I believe this is relevant because the urban heat island effect may be a contributor to the upward trend in global temperatures of the last 10 years. However, some data sets already control for the urban heat island effect and thereby take it into account when computing the average global temperature. I will use images from the Global Visualization Tool (GLOVIS) via the United States Geological Survey (USGS) site to determine how much absorption from solar radiation is taking place in the city. After the images are collected, a statistical verification will be completed by retrieving average monthly low temperature data from within the months in which the satellite images are representing. The reasoning for the low temperature data is due to the fact that the urban heat island effect is more prevalent at night compared to during the day (Ball, 2008). It is very common for the television meteorologist on the various news stations in the Philadelphia area to first mention the high and low temperature for the city and then subsequently tell the viewer the high and low temperatures for the outlying suburbs. A large majority of the time, the high and low temperature of Philadelphia will be significantly warmer than the suburbs by at least a few degrees Fahrenheit. For Philadelphia, is the Urban Heat Island Effect more dominant during the warmer months or is it more dominant in the cooler months? Also, how has the urban heat island effect changed within these months in the last 10 years? Could the population increase of the city s suburbs decrease the consequence of the urban heat island effect? 3 P a g e

4 According to the paper, An Analytic Framework for Estimating the Urban Effect on Climate, The urban effect becomes more important as the fraction of urban land cover to the total increases. Urbanization in Chester County and surrounding areas increased from 11% in 1987 to 19% in In 1996, urban land cover produced the largest proportionate sensible (21.4 W/m 2 ) and latent (14.2 W/m 2 ) heat fluxes during winter. During the 1996 summer, urban and vegetation land cover produced the largest proportionate sensible heat (59.2 W/m 2 ) while urban land cover produced the second largest proportionate latent heat flux (39.5 W/m 2 ). (Lamptey, 2010). Chester County is where I have resided my entire life. I can say first hand that the population of my county and the other outlying suburbs has increased substantially in the last couple decades. However, the population of these same areas has increased even more abruptly in the past 10 years. This will consequently result in an increase in homes, buildings, parking lots, pavements, asphalt, etc (Espy, 2005). The outcome of this will be displayed on the following images. It will be determined if the evening out of the populations between Philadelphia and its suburbs will have an effect on the appearance of the outgoing longwave radiation on the images taken from GLOVIS. It will also be determined if this has also created a more noticeable variation in seasonal data. My first assumption is that the effect of urban heat will be more visible in the warmer months compared to the cooler months. My second assumption is that the evidence of the urban heat island effect between Philadelphia and its outlying suburbs has decreased in the past 10 years, given the rapid increase in the population of the suburbs. 4 P a g e

5 Using the GLOVIS tool from the USGS site, I acquired numerous images from southeast Pennsylvania to southern New Jersey, which included the city of Philadelphia. The differences in longwave radiation between the city and the surrounding, outlying suburbs should determine how much of an urban heat island effect is actually present. I will analyze these images from the cooler months and the warmer months over the span of the last 10 years to determine if the urban heat island effect is more prevalent in the warmer months than in the cooler months. The analyzing of these images will also determine if the effect of urban heat has decreased in the last 10 years. I examined satellite detected/infrared detected temperature readings of buildings, streets, asphalt, etc. in the city of Philadelphia, which would directly relate the differences in the temperature of the city and the surrounding suburbs to the urban heat island effect. The data will be collected for days in which there were clear skies and light winds. I do not want any cloud interference, as that will lower the amount of infrared radiation being emitted back into space. By having clear skies and light winds, I can see the full effect of the comparison between urban heat and suburban heat. As mentioned above, the data analysis used will be the GLOVIS tool from The USGS site at The specific data collection used was the ASTER Level-1B U.S. Day. This data collection uses Advanced Spaceborne Thermal Emission and Reflection 5 P a g e

6 Radiometer to represent daily Visible and Near Infrared (VNIR), Thermal Infrared (TIR), and Shortwave Infrared (SWIR) bands in the United States from the year 2000 to the present. However, there are some data limitations. When acquiring this data, there was unfortunately not an available image for every specific month of a specific year for this area. This made the analysis not as consistent as I would like. For instance, an image for December, 2003 was not available, so I had to settle for November, Figure 1: A six image seasonal analysis from August, 2002 to March, 2008 which displays the effect of urban heat shown on a satellite image. Each image is either from a warmer month or a cooler month. 6 P a g e

7 Month, Year Location Average Low Temperature Month, Year Location Average Low Temperature August, 2002 Philadelphia, PA 70.4ºF October, 2005 Philadelphia, PA 54.0ºF Allentown, PA 62.4ºF Allentown, PA 50.9ºF Reading, PA 65.4ºF Reading, PA 51.6ºF Wilmington, DE 67.6ºF Wilmington, DE 52.4ºF November, 2003 Philadelphia, PA 42.3ºF September, 2007 Philadelphia, PA 63.7ºF Allentown, PA 37.3ºF Allentown, PA 58.6ºF Reading, PA 37.7ºF Reading, PA 60.8ºF Wilmington, DE 40.4ºF Wilmington, DE 61.1ºF May, 2004 Philadelphia, PA 59.9ºF March, 2008 Philadelphia, PA 37.0ºF Allentown, PA 55.2ºF Allentown, PA 34.2ºF Reading, PA 57.0ºF Reading, PA 34.5ºF Wilmington, DE 59.2ºF Wilmington, DE 35.8ºF Figure 2: Monthly average low temperature retrieval of four locations that correspond to the six specific (months, years) that the satellite data refers to. Months coded in orange are the warmer months. Months coded in light blue are the cooler months. After the satellite images were collected, an average monthly temperature verification for the six corresponding months from the satellite images were collected from four different locations (see Figure 2 above). Those four locations were: the City of Philadelphia, and three outlying suburb locations: Allentown, PA, Reading, PA, and Wilmington, DE. This temperature verification will be used to determine if the satellite images do indeed validate with average monthly temperatures. The temperature verification came from the Pennsylvania State Climatologist website at The validation of the temperatures with the satellite images is what is known as Ground Truth. 7 P a g e

8 After carefully observing all six of the satellite images, I have noticed that the most contrast between the urban area of Philadelphia and its outlying suburbs lies with August, 2002, May, 2004, and September, 2007 images. All three of these images occur in the warmer months of the year. The least amount of contrast occurred in the November, 2003, October, 2005, and March, 2008 images. All three of these images occur in the cooler months of the year. The bluish color in these images represents the actual city of Philadelphia. It is warmer here because of higher amount of longwave radiation being emitted back into space. The reddish color surrounding the blue color represents the cooler areas (the suburbs) surrounding Philadelphia. Less outgoing longwave radiation is being emitted in these areas. Regardless of what satellite image is obtained in any given month, a mild to major urban heat island effect will be evident for this area. The temperature verification in Figure 2 corresponded fairly well with the satellite images. It is quite noticeable that the temperature gradient is larger between Philadelphia and the other three locations during the warmer months compared to the cooler months. Therefore, the satellite imagery and the temperature verification go hand in hand with one another. My first hypothesis stated that I believe that the effect of urban heat will be more visible in the warmer months compared to the cooler months. According to my satellite imagery analysis and my temperature verification, the urban heat island effect is more evident in the warmer months compared to the cooler months which goes along with my hypothesis. This can be advantageous for some cities during the winter as warmer temperatures can reduce heating energy needs and lend a hand in melting ice and snow on the roads. Alternatively, these cities in summer will experience increased air pollution, greenhouse gas emissions, and heat-related illness (Ball, 2008). 8 P a g e

9 The existence of vegetation in rural areas assists in evaporating water following any absorption of solar energy. However, the solar energy input in cities is absorbed by the buildings, streets, tar, asphalt, etc. As the pavements in cities are largely nonporous, evaporative cooling is less. This contributes to raising the air temperatures. Heat generated by cars and trains eventually makes its way into the atmosphere. This heat is often as much as onethird of that received from solar energy. (Ball, 2008). My second hypothesis states that I think that the evidence of the urban heat island effect between Philadelphia and its outlying suburbs has decreased in the past 10 years, given the rapid increase in the population of the suburbs. As mentioned, I unfortunately was not able to acquire images in that area for all given, specific dates in the calendar year. As a result, the time frames are between 2002 and As shown in Figure 3 below, I calculated the average temperature gradient between the three suburbs and Philadelphia for each of the six time periods. Month, Year Average Temperature Gradient Between Suburbs and Philadelphia August, November, May, October, September, March, Figure 3: Table showing the average temperature gradient between the three suburbs and the City of Philadelphia. According to this analysis, the temperature gradient has decreased overall as time increased towards the present day. 9 P a g e

10 Temperature Gradient (ºF) According to the table above and the graph below, the average temperature gradient of the warmer months are higher overall than the average temperature gradients of the cooler months. Furthermore, the average temperature gradient between the three selected suburb locations and Philadelphia has decreased overall as time increased towards the present. This goes hand in hand with my second hypothesis. This is by no means stating that the decrease in temperature gradient is completely caused by the increase in population, buildings, homes, parking lots, etc in the suburbs. However, it could certainly be a contributing factor. 6 Average Temperature Gradient Between Suburbs and Philadelphia Average Gradient Between Suburbs and Philadelphia 0 August, 2002 November, 2003 May, 2004 October, 2005 September, 2007 March, 2008 Corresponding Time Periods to Satellite Images Figure 4: A line graph depicting the change in the average temperature gradient between Philadelphia and its outlying suburbs from August, 2002 to March, P a g e

11 Both of my assumptions prior to conducting this project ended up being correct. The first assumption was that the urban heat island effect is more dominant during the warmer months in the calendar year compared to the cooler months. This is validated according to the Outgoing Longwave Radiation measurements observed on the GLOVIS tool s six satellite images retrieved and the corresponding temperature data for those same time periods. The second assumption was that the effect of urban heat would be less obvious as time increased towards the present. My theory was that this is due to the increase in citizens moving to the suburbs. This would cause an increase in the amount of homes, buildings, blacktop, parking lots, and other structures being built. Consequently, I believe that this has at least partially contributed to the evening out of the average temperature gradients between the city and the suburbs as time has increased towards the present. Overall, I believe that this project is certainly beneficial to future study. The urban heat island effect is a well known phenomenon that has been present for decades. Cities are known to have a higher population than its surrounding areas. However, now that there is an increase in population of the area surrounding Philadelphia (the suburbs), this may have caused a lessening of the urban heat island effect. In other words, the amount of outgoing longwave radiation measured is becoming more evenly distributed between Philadelphia and its suburbs. This will most likely leave an effect on future satellite images for this area, meaning that the urban heat island effect will increasingly become less noticeable on these types of satellite images. In addition, the daily climate reports of various locations within the suburbs will probably begin to more closely resemble that of the city. 11 P a g e

12 References 1. Ball, Tim, 2008, Urban Heat Island Effect. < 2. Espy J, 2005, Pennsylvania Suburbs Population Projection and Forecast, < dley_walden.pdf >. 3. Lamptey B, 2010, An Analytical Framework for Estimating the Urban Effect on Climate, v. 30, p Pennsylvania State Climatologist Office. FAA Hourly Data. < 5. United States Geological Survey, Global Visualization Tool. < 12 P a g e