Prediction Interval of Interface Regions: Machine Learning Nowcasting Approach

Abstract

Stream interaction region (SIR) is one of the space weather phenomena that accelerates the upstream particles of the interface region in interplanetary space and causes geomagnetic storms. SIRs are large-scale structures that vary temporally and spatially, both in latitudinal and radial directions. Predicting the arrival times of interface regions (IRs) is crucial to protect our navigation and communication systems. In this work, a 1D ensemble system comprised of a Long-short-term memory (LSTM) model and a Convolution Neural Network (CNN) model—LCNN is introduced to classify the observed IR time series and give the prediction interval nowcast of its transit time to the observer. The outcomes of the two models are combined in a way to boost the accuracy of the predictor and prevent error propagation between them. The implemented technique is time series classification on datasets from STEREO A and B spacecrafts. The LCNN prediction system of IRs provides advanced Notice Time (NT) interval between [20, 160] minutes with sensitivity around 93% and geometric mean score gmean of 91.7%, and the skills decrease with increasing the prediction time. The LCNN demonstrates an enhancement in the prediction with respect to using only either the CNN or LSTM models. The predicted probabilities are recalibrated so that the predicted frequency of IRs becomes on average consistent with the observed frequency. Application of the method is useful to provide a classification of IRs by inputting a time series and estimating the likelihood of occurrence of an IR and its arrival time on the observer.