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Abstract 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 would ensure substantial improvements of Space Weather (SWx) predictions. On the one hand, our focus is on the development of data-driven solar wind models. On the other hand, each individual component of our software is designed to have accuracy higher than any existing SWx prediction tools 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 predictions accompanied with an appropriate uncertainty quantification. This makes it possible to forecast hazardous SWx effects on the space-borne and ground-based technological systems, and on human health. Our models include (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 tests show that our software is formally more accurate and performs much faster than its predecessors used for SWx predictions.
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Open-Source High-Performance Flux Transport
The NASA-NSF sponsored Space Weather with Quantified Uncertainty (SWQU) project's main objective is to develop a data-driven, time-dependent, open source model of the solar corona and heliosphere. One key component of the SWQU effort is using a data-assimilation flux transport model to generate an ensemble of synchronic radial magnetic field maps as boundary conditions for the coronal field model. To accomplish this goal, we are developing a new Open-source Flux Transport (OFT) software suite. While there are a number of established flux transport models in the community, OFT is distinguished from many of these efforts in 3 key attributes: (1) It is based on modern computing techniques that will allow many realizations to be rapidly computed on multi-core systems and/or GPUs, (2) it is designed to be easily extensible, and (3) OFT will be released as an open source project. OFT consists of three software packages: 1) OFTpy: a python package for data acquisition, database organization, and Carrington map processing, 2) ConFlow: a Fortran code that generates super granular convective flows, and 3) High-Performance Flux Transport (HipFT): a modular, GPU-accelerated Fortran code for modeling surface flux transport with data assimilation. Here, we present the current state of the OFT project, key features and methods of OFTpy, ConFlow, and HipFt, and real-world examples of data-assimilation and flux transport with HipFT. Validation and performance tests are shown, including generating an ensemble of OFT maps.
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- Award ID(s):
- 2028154
- PAR ID:
- 10436298
- Date Published:
- Journal Name:
- AGU Fall Meeting 2022
- Page Range / eLocation ID:
- SH15D-1508
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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