The segmentation and temporal tracking of fast flow structures called bursty bulk flows is an integral part of space weather study. This paper presents the first known topological segmentation and tracking technique to analyze bursty bulk flows. We apply contour tree-based segmentation to identify regions of high plasma velocity from data output from space weather simulations. We then employ a Kuhn-Munkers assignment algorithm with a modified Wasserstein metric to match features in adjacent time steps. A newly implemented algorithm to determine splits and merges of segments results in a fully topological framework for feature detection and tracking of fast flow channels.

Exascale computing will bring with it significant I/O limitations. One foreseeable consequence of such restrictions is that the user can save only a small fraction of complex simulation data to disk for subsequent analysis. An alternative is to fit statistical models to data in situ, that is, inside the simulation as it runs. This option requires extremely fast statistical estimation to avoid slowing down the simulation. Gaussian processes (GPs) have state-of-the-art predictive performance for modeling spatial data. However, standard estimation methods for GPs scale quite poorly to large data sets as parameter estimation requires inverting a covariance matrix to the size of the data set. In the presented work, we use a convolutional neural network (CNN) to predict the GP parameters for a spatial data set, from a simulation or otherwise, rather than optimize the parameters directly. Our presented case study models spatial data from E3SM, the Department of Energy’s Exascale climate model. The CNN is trained on synthetic data simulated from GP models with known parameters and then applied to data from the climate simulation. In the presented examples, the neural network scheme produces parameter estimates that compare well with standard methods such as maximum likelihood estimation in predictive performance but is obtained four orders of magnitude faster.

Due to I/O bandwidth limitations, intelligent in situ data reduction methods are needed to enable post-hoc workflows. Current state-of-the-art sampling methods save data points if they deem them spatially or temporally important. By analyzing the properties of the data values at each time-step, two consecutive steps may be very similar. This research follows the notion that if neighboring time-steps are very similar, samples from both are unnecessary, which leaves storage for adding more useful samples. Here, we present an investigation of the combination of spatial and temporal sampling to drastically reduce data size without the loss of valuable information. We demonstrate that, by reusing samples, our reconstructed data set reduces the overall data size while achieving a higher post-reconstruction quality over other reduction methods.