S'COOL : Science Project/Science Fair Ideas

Here are a few ideas on science projects you could do with the S'COOL data. You can probably think of many other things, starting from these suggestions.

If you come up with a good project, or if you win a prize for your project, please let us know so we can feature it here.

Observation Database

Using ground observations only

We still have only a few satellite observations in the database, due to a variety of instrument calibration, data transfer, and processing problems. So, projects which concentrate on only the ground observations may be best for now.

• Retrieve all the observations for your location, and make graphs or charts. Are there any trends? If you have a long record, can you detect effects of El Niño? Of changes in instrumentation (i.e., a different set of student observers)? Instrument changes are a big problem in long term climate records.
• Retrieve all observations for your local region and compare to what you reported. Make maps of clouds, temperature, and pressure. See whether you can detect weather systems. Which way does weather move where you live? Can you use this to make forecasts?
• Retrieve observations from other parts of the globe. Compare to what you reported. Can you identify local features which make the climate elsewhere different from yours?
• A few participants have reported data more than once a day. If you find such records, can you find trends in cloudiness with time of day? How does this affect a satellite which measures only once per day?

Comparing to satellite data

There are currently 99 instances of matched satellite and surface observations in the database. Most are before Sept. 1998. Looking at those matched data:

• Analyze the whole set of comparisons and look for trends.
• Pick a single matched case and analyze it in detail. Do the two observations agree? If not, can you explain why not? Consider:
  • Perspective differences (top vs bottom)
  • Field of view differences (did the satellite look at exactly the same piece of sky as the ground observer?)
  • Definition issues (i.e., CERES splits mid- vs high clouds at exactly 6km, which is not always what nature does)
  • Clear sky determination - did the satellite miss small or thin clouds; or did it see such clouds where none exist?
• Multilayer clouds are a big issue in CERES. You could take a look at the database and identify how often they occur. You could also study how often we cannot say whether they occur, due to the presence of thick low clouds (for the ground observer) or thick high clouds (for the satellite). Is there an area where multilayer clouds are more common (coastal area, interior, land/ocean)?