A ML Multispectral Time Series Data set Prepared from the SOHO Mission for Space Weather Readiness скачать в хорошем качестве

A ML Multispectral Time Series Data set Prepared from the SOHO Mission for Space Weather Readiness

Authors: Carl Shneider, Andong Hu, Mandar Hemant Chandorkar, Ajay Kumar Tiwari, Monica G. Bobra, Jannis Teunissen, Enrico Camporeale Abstract: We present a flexible framework that allows user-defined input selection criteria and a range of pre-processing steps, for generating ready-to-use machine-learning Solar and Heliospheric Observatory (SOHO) multispectral time series data cubes. SOHO is a collaborative international project between NASA and ESA and this mission’s data is selected for both its high spatial and temporal resolution, covering the full Solar Cycle 23. The framework utilizes SunPy’s Federated Internet Data Obtainer (Fido) interface with the Virtual Solar Observatory (VSO) tool. Using SOHO ML-ready images for forecasting space weather parameters is a potentially valuable resource for the community. We illustrate the use of this data with an application of a deep convolutional neural network (CNN) to a subset of the full data set in an effort to provide a 3-5 day-ahead forecast of the north-south component of the of the interplanetary magnetic field (IMF) observed at L1. Baselines for future model comparison are also provided.