It has been a quiet summer here in Asheville and on CycloneCenter.org. We tend to have fewer classifications in the summer, likely due to all those vacations that you’re taking.
As you come back from break, though, don’t forget to classify some storm images. We recently had a paper accepted for publication in a scientific journal (more on that in later posts) and we’re planning some big things for our fourth birthday. So check out the recent stats and go make some classifications!
Hello Classifiers and Friends! There have been a number of recent developments in Cyclone Center world in recent weeks. Have a read and then head over to the Cyclone Center website and help us keep the classifying momentum!
New Cyclone Center Journal Article Accepted
CC scientist Dr. Ken Knapp from the U.S. National Centers for Environmental Information (NCEI) in Asheville, NC is the lead author on a new paper just recently accepted into the journal Monthly Weather Review. Titled “Identification of tropical cyclone ‘storm types’ Read More…
Originally launched in September of 2012, Cyclone Center has gathered over a half million classifications from citizen scientists in nearly every country. We use your classifications to clarify inconsistencies in historical tropical cyclone wind records. Your contributions have resulted in the publication of two papers, numerous scientific presentations, and educational opportunities from K-12 through college.
There is still much to do; we need your help to finish classifying our 32-year data set of tropical cyclone images. Log on to cyclonecenter.org and join our expanding group of citizen scientists today.
One of the great things about crowd sourcing is that we have the luxury of using numerous classifications to determine an answer for one image. The responses of 15 citizen scientists is much more powerful than a response from one person, even if that person is an expert.
We have gone through every single storm image on Cyclone Center that has been classified by at least 10 citizen scientists. All classifications were used to determine the variance of the image – or, how similar one classification was to the others. Ambiguous cloud patterns will have a higher variance than one with a clear eye, for example.