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Abstract Global solar photospheric magnetic maps play a critical role in solar and heliospheric physics research. Routine magnetograph measurements of the field occur only along the Sun–Earth line, leaving the far side of the Sun unobserved. Surface flux transport (SFT) models attempt to mitigate this by modeling the surface evolution of the field. While such models have long been established in the community (with several releasing public full-Sun maps), none are open source. The Open-source Flux Transport (OFT) model seeks to fill this gap by providing an open and user-extensible SFT model that also builds on the knowledge of previous models with updated numerical and data acquisition/assimilation methods along with additional user-defined features. In this first of a series of papers on OFT, we introduce its computational core: the High-performance Flux Transport (HipFT) code (https://github.com/predsci/hipft). HipFT implements advection, diffusion, and data assimilation in a modular design that supports a variety of flow models and options. It can compute multiple realizations in a single run across model parameters to create ensembles of maps for uncertainty quantification and is high-performance through the use of multi-CPU and multi-GPU parallelism. HipFT is designed to enable users to write extensions easily, enhancing its flexibility and adaptability. We describe HipFT’s model features, validations of its numerical methods, performance of its parallel and GPU-accelerated code implementation, analysis/postprocessing options, and example use cases.
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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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