Measuring event probabilities in uncertain scalar datasets using Gaussian processes

Abstract

In this paper, we show how the concept of Gaussian process regression can be used to determine potential events in scalar data sets. As a showcase, we will investigate climate data sets in order to identify potential extrem weather events by deriving the probabilities of their appearances. The method is implemented directly on the GPU to ensure interactive frame rates and pixel precise visualizations. We will see, that this approach is especially well suited for sparse sampled data because of its reconstruction properties.