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Global and local surface temperature

Student(s):Brandon Szychoski, Tyler Yacono and Samantha Wittenberger
Grade Level:Secondary School (grades 9-12, ages 14-18)
GLOBE Educator(s):Peter Dorofy
Report Type(s):
Protocols:Air Temperature, Surface Temperature
Date Submitted:05/02/2013
Screen capture from presentation
Our research was on surface temperatures. The purpose of the project was to compare the fact that global remote sensing agree with local ground observations. Satellite pictures (p.4) show ocean water to have the least variation in temperature and land has largest amount. We wanted to compare what satellites saw to what we see at our school. We hypothesized that, because of the fact that black holds the most radiation/energy, that the asphalt would have the largest variation of change in heat. We also hypothesized that the concrete would change the least due to it's white color. Our procedure was, almost daily, that we would go outside and record the surface temperatures of four surfaces, the short grass field, the water of the pond, the asphalt, and the concrete around a school. Our result was that the temperature of the water had the least variation in temperature. Our conclusion was slightly off from our hypothesis. The water had the smallest amount of change in temperature.


Time of year is clearly crucial for the temperatures you measure. How could you analyze the data to remove that variable?
If I understand you correctly, what we could test for, is to see if the temperature changes are the same all year-round. Although at different times of the year the temperatures will be different, the change it temperatures may be relatively the same. Again, that's what we would test for.
In addition, the purpose of this project is just the first step to a much larger project in investigating tropical cyclone energy.
Why do you think that water has less of a variation in temperature than your other surfaces?

What else, other than adding an additional measurement time, would you change about your project?
What were the dates of your measurements?
Did you observe cloud cover on these days, and if so, did cloud cover variations result in any effects that would influence your results?

Nice charts. Also, I like your selection of different surfaces to measure and your idea of adding a 3:00 PM measurement to further test your results.