An online severe weather tool known as SWAT (Severe WeAther Tool) has been developed to allow the public to classify thunderstorm systems as being of one of ten types: isolated cells, clusters of cells, broken lines of cells, squall lines without stratiform rain, squall lines with trailing stratiform rain, squall lines with leading stratiform rain, squall lines with parallel stratiform regions, bow echoes, nonlinear systems, and mixed-complex systems. SWAT then determines the severe storm reports associated with each of the identified types. SWAT accesses two publicly available datasets, the daily image archive of radar data available through the National Center for Atmospheric Research, with 30- minute temporal resolution, and the Storm Data database of the National Centers for Environmental Information. A web-based interface allows users to log into the activity (so that information is available for later analysis of users and possible weighting based on how well a user matches classifications performed by experts), and then either choose “About” to learn more about what the user will be doing, or begin classifying systems. If the user chooses to begin classifying systems, the user is shown 9 different regions from around the country, and after they select a region, they are then shown a random radar image from the warm season of 2015. After enough users have performed classifications so that each system in 2015 has been classified by multiple users, the selection of a date will be left to the user, and the time period will be expanded (data are available to eventually include 1996 through the present). Once a user selects a region, the user sees an enlarged view of the radar data for that part of the country, along with templates of 10 classification types for thunderstorm systems that they can choose from as being most like the system they focused on in the radar data. Information is available to the user at the start of the activity or at any time through the “About” tab, that provides instructions on what they will be doing and the reasons why it is important. They are also told to concentrate on one system and to use the time navigation buttons (Next or Previous) so that they can continue to classify the system every 30 minutes throughout its lifetime (typically a few hours). When the user reaches the end of the system’s lifetime, they are instructed to draw a rubber band box around the general region affected by their system.
An algorithm was created that applies rules used in two prior published studies (Gallus et al., Weather Forecasting, 2008; Duda and Gallus, Weather Forecasting, 2010) to assign a dominant classification to a period in the lifetime of the system only when continuity is maintained for at least an hour period (3 images). Once these consistent classifications are assigned, the code accesses the Storm Data database making use of the latitude and longitude of the box drawn by the user, and then finds all severe weather reports that occurred due to that thunderstorm system. These reports include hail of three sizes, severe wind gusts of two strengths, tornadoes, and flash floods. Once these data are collected, the user is then told of the dominant types (of the 10 morphologies) of their system along with the final outcome in terms of amount of severe weather (tornadoes, hail, wind, flooding). These data are maintained in a large table that is not made available to the users, but instead can be used in follow-up studies for scientific research, such as comparisons over different time periods or regions than in the prior two studies. The use of this citizen science activity will allow for a longer period of time to be studied, along with a broader region of the country.
Although the activity was designed in a way that it can be used by anyone with internet access (including on mobile devices), and coordination has taken place to increase its access to users by having the site be hosted by Zooniverse with other citizen science initiatives, it will be tested with freshmen geoscience students in a freshman learning community in the Department of Geological and Atmospheric Sciences at Iowa State University during the fall semester of 2016. The development of the activity was motivated by a goal to have as many students as possible conduct authentic scientific research in their freshman year at college. Preliminary feedback from students along with possible impacts on student impressions of science will be presented based on the initial implementation in the learning community.