Authors: N.V. Pogorelov (University of Alabama in Huntsville), C. N. Arge (Goddard Space Flight Center), P. Colella (Lawrence Berkeley National Laboratory), J. Linker (Predictive Science), B. Van Straalen (Lawrence Berkeley National Laboratory), L. M. Upton (Southwest Research Institite), R. Attie (Predictive Science), R. Caplan (Predictive Science), C. Downs (Predictive Science), C. Gebhard (Lawrence Berkeley National Laboratory), D. V. Hegde (University of Alabama in Huntsville), S. Jones (Goddard Space Flight Center), T. K. Kim (University of Alabama in Huntsville), A. Marble (University of Colorado, Boulder), S. Raza (University of Alabama in Huntsville), T. Singh (University of Alabama in Huntsville), M. Stulajter (Predictive Science), J. Turtle (Predictive Science), M. S. Yalim (University of Alabama in Hunstville)

Improving Space Weather (SWx) predictions requires, on the one hand, to recognize that we are working with an interrelated Sun-heliosphere system and, on the other hand, address the problem by systematically enhancing each component that affects the accuracy of forecasts. To address Objective II of the National Space Weather Strategy and Action Plan ‘Develop and Disseminate Accurate and Timely Space Weather Characterization and Forecasts’ and US Congress PROSWIFT Act 116–181, our team is developing a new set of open-source software that ensures substantial improvements of SWx predictions. While focusing on the development of data-driven models, we ensure that each individual component of our software has higher accuracy with a dramatically improved performance. This is done by the application of new computational technologies and enhanced data sources. The development of such software paves way for improved SWx forecasts accompanied with an appropriate uncertainty quantification. This makes it possible to monitor hazardous SWx effects on the space-borne and ground-based technological systems, and on human health. Our models involve (1) a new, open-source solar magnetic flux model (OFT), which evolves information to the back side of the Sun and its poles, and updates the model flux with new observations using data assimilation methods; (2) a new potential field solver (POT3D) associated with the Wang-Sheeley-Arge coronal model, and (3) a new adaptive, 4-th order of accuracy solver (HelioCubed) for the Reynolds-averaged MHD equations implemented on mapped multiblock grids (cubed spheres). We describe the software and results obtained with it, including the application of machine learning to modeling coronal mass ejections, which makes it possible to improve SWx predictions by decreasing the time-of-arrival mismatch. The extensive tests show that our software is formally more accurate and performs much faster than its predecessors used for SWx predictions.