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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. 

Graduated Cylindrical Shell (GCS) model is a widely used tool to determine direction, kinematic and orientation properties of Coronal Mass Ejections (CME) using multi-viewpoint observations from SOHO and STEREO A&B coronagraphs. In this study, we estimate the subjective uncertainties typically seen while deriving these CME properties by comparing the GCS model results reported in multiple studies and catalogs for 56 CMEs. We find that the GCS estimates of latitude, longitude, and tilt show an average uncertainty of 5.7, 11.2, and 24.7 degrees with standard deviation of 5.5, 12.7, and 19.7 degrees respectively. We found that the uncertainties in estimated latitudes are correlated with uncertainties in estimated longitude, tilt, and speed, showing that some CMEs are inherently difficult to fit than others. We then introduced these uncertainty values in our 3-D magnetohydrodynamic flux rope based modified spheromak CME model to figure out their consequences for space weather prediction. We find that much better CME observations are required to reliably predict magnetic field of CMEs at 1 AU using flux rope based models, since the uncertainties in estimated GCS values can result in large differences in 1 AU signatures, especially for CMEs launched away from the Sun-Earth line.