In an attempt to overcome some of these issues, students of the Oklahoma Weather Lab were challenged to forecast a random, historical (1948-2016) severe weather event. Python software was created to develop a full set of products for the students to sort through when developing their forecast. Unlike most case studies, this event utilized actual depreciated data sources (e. g. WSR-57 radar), to break students out of typical forecasting habits and force them to find ways to sort through unfamiliar data in order to solve the forecasting problem. Special care was taken to conceal the event type and remove as many non meteorological context clues as possible from the data.
The participants of this challenge enjoyed the unexpected and unfamiliar nature of the forecasting challenge. Many of them were surprised by their own performance and the performance of tools that have long since been replaced in operational forecasting. The success behind this exercise suggests that the element of surprise can be used effectively in creative ways when in teaching forecasting.