Space Weather (SWx) is defined by the transients in the space environment traveling from the Sun, through the heliosphere, to Earth. In the past decade, the difficult task of understanding and predicting violent solar eruptions and their terrestrial impacts has become a strategic national priority, as it affects the daily life of humans, including communication, transportation, power supplies, national security, space travel, and more. It is also crucial to the development and operations of space exploration missions, including the future Moon and Mars initiatives. Although SWx broadly encompasses a wide range of effects, one of the primary concerns is the interaction of Coronal Mass Ejections (CME) with the Earth's magnetosphere, ionosphere, atmosphere, and lithosphere. The strength of the CME-Earth interaction is controlled to a large extent by the speed of arriving CME and the amount of southward-pointing magnetic field component it carries. CMEs propagate through the ambient solar wind, which is frequently characterized by the presence of high-speed streams (HSSs) that originate from the coronal holes. HSSs can be especially effective in coupling with the Earth's ionosphere/magnetosphere because of the long-lasting recovery of the storms they produce.  

To address Objective II of the National Space Weather Strategy and Action Plan to "Develop and Disseminate Accurate and Timely Space Weather Characterization and Forecasts" and US Congress PROSWIFT Act 116-181, our team has developed a new set of open-source software that ensures substantial improvements of SWx forecasts. On the one hand, our focus has been on the development of data-driven solar wind models. On the other hand, each individual component of our software has been designed to have accuracy higher than any existing SWx prediction tools with a dramatically improved performance. This has been 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 (WSA) coronal model, resulting in SWiG 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). The tests show that our software is formally more accurate and performs much faster than its predecessors used for SWx forecasts. All components in the POT3D-OFT-SWiG-HelioCubed toolkit work on both CPUs and GPUs and have been made publicly available:

(1) POT3D: https://github.com/predsci/pot3d

(2) OFT/MagMAP: https://github.com/predsci/magmap

(3) OFT/HipFT: https://github.com/predsci/oft

(4) Magnetic field tracer MAPFL: https://github.com/predsci/mapfl

(5) SWiG: https://github.com/predsci/swig

(6) HelioCubed: https://github.com/dhami1234/HelioCubed.

In addition, they have been either implemented (OFT, WSiG) or submitted for implementation (HelioCubed) at the NASA Community-Coordinated Modeling Center (CCMC).

To quantify uncertainty in simulation results and, consequently, SWx forecasts, the team preformed extensive ensemble simulations and analyzed their performance using the WSA metrics. New uncertainty quantification procedures have been applied in simulations involving multiple spacecraft data, including Parker Solar Probe (PSP), Solar Orbiter (SolO), STEREO, and OMNI data base. These analyses have been applied both to the ambient (background) solar wind and to CMEs. From the forecasting perspective, the ensemble modeling approach significantly improves our ability to assess and communicate uncertainties in the ambient SW. By simulating a range of solar wind realizations, we were able to quantify the confidence bounds for each specific prediction. Our study represented the first step towards understanding of how uncertainties in the input synchronic maps propagate through the model chain. Importantly, such modeling also helped distinguish between the features that are robust across the ensemble members and those that are sensitive to boundary uncertainties, enabling forecasters to better assess the associated risks.

Our team directly demonstrated that a 12-hour, on the average, prediction window for the CME time of arrival may be directly related to subjective uncertainties in the identification of initial parameters of CMEs inserted into the background SW. This means that (1) theoretical CME models require further improvements and (2) more observational data is required for be used in the above-mentioned models. It has been demonstrated convincingly that application of ML/AI techniques is critically important for improving SWx forecasts at the current state of the art. By way of example, we used the time-elongation CME maps available from STEREO (now also from SolO) spacecraft for this purpose. As a result, we have been able to improve CME time of arrival predictions 2-3 times. Although our pioneering study involved a few dozens of CMEs, the proposed approach is certainly very promising and productive.

Last Modified: 06/24/2025
Modified by: Nikolai V Pogorelov