Paul Roebber is a professor of Atmospheric Sciences at the University of Wisconsin - Milwaukee whose research focuses on the improvement of weather forecasting systems. Mr. Roebber has his Doctorate in Atmospheric Science from McGill University in Montreal, Quebec Canada.
Mr. Roebber has been using NeuroSolutions along with the Custom Solution Wizard and Source Code Library for the past 8-years. Using NeuroSolutions, Mr. Roebber developed a snow density system that is currently employed by the National Weather Service to assist in the prediction of snow fall depths. Mr Roebber's system uses several different types of neural network architectures including the Principal Component Analysis (PCA) and Multilayer Perceptron (One and Two Hidden Layer) to complete his ensemble of 10 artificial neural networks.
The ensemble correctly diagnoses 60.4% of the snow event cases examined, which is a substantial improvement over the 41.7% correct using the sample climatology, 45% correct using the 10-to-1 ratio (see table), and 51.7% correct using the National Weather Service "new snowfall to estimated meltwater conversion" table. The Heidke skill score measures the fraction of correct forecasts after eliminating those forecasts which would be correct due purely to random chance. The ensemble technique attains Heidke skill scores of 0.34 - 0.42, which is an increase of 75% - 183% over the next most skillful approach!
Mr. Roebber had no problem choosing NeuroSolutions as "nothing else seemed competitive to NeuroSolutions". When asked about what he liked best about NeuroSolutions, he said it was the ability to "get you up to speed in terms of building the neural network" and also the "good documentation on the background of the neural networks".
We would like to thank Mr. Roebber for sharing his great success story with us and using NeuroSolutions to help improve modern day weather forecasting for the National Weather Service.
You can read all 3 published articles on the American Meteorological Society website