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The Sun’s corona is its tenuous outer atmosphere of hot plasma, which is difficult to observe. Most models of the corona extrapolate its magnetic field from that measured on the photosphere (the Sun’s optical surface) over a full 27-day solar rotational period, providing a time-stationary approximation. We present a model of the corona that evolves continuously in time, by assimilating photospheric magnetic field observations as they become available. This approach reproduces dynamical features that do not appear in time-stationary models. We used the model to predict coronal structure during the total solar eclipse of 8 April 2024 near the maximum of the solar activity cycle. There is better agreement between the model predictions and eclipse observations in coronal regions located above recently assimilated photospheric data. 

Abstract Coronal Mass Ejections (CMEs) are immense eruptions of plasma and magnetic fields that are propelled outward from the Sun, sometimes with velocities greater than 2000 km/s. They are responsible for some of the most severe space weather at Earth, including geomagnetic storms and solar energetic particle (SEP) events. We have developed CORHEL-CME, an interactive tool that allows non-expert users to routinely model multiple CMEs in a realistic coronal and heliospheric environment. The tool features a web-based user interface that allows the user to select a time period of interest, and employs Regularized Biot-Savart Law (RBSL) flux ropes to create stable and unstable pre-eruptive configurations within a background global magnetic field. The properties of these configurations can first be explored in a zero-beta magnetohydrodynamic (MHD) model, followed by complete CME simulations in thermodynamic MHD, with propagation out to 1 AU. We describe design features of the interface and computations, including the innovations required to efficiently compute results on practical timescales with moderate computational resources. CORHEL-CME is now implemented at NASA's Community Coordinated Modeling Center (CCMC) using NASA Amazon Web Services (AWS). It will be available to the public in early 2024. 

ABSTRACT Understanding and predicting the structure and evolution of coronal mass ejections (CMEs) in the heliosphere remains one of the most sought-after goals in heliophysics and space weather research. A powerful tool for improving current knowledge and capabilities consists of multispacecraft observations of the same event, which take place when two or more spacecraft fortuitously find themselves in the path of a single CME. Multiprobe events can not only supply useful data to evaluate the large-scale of CMEs from 1D in situ trajectories, but also provide additional constraints and validation opportunities for CME propagation models. In this work, we analyse and simulate the coronal and heliospheric evolution of a slow, streamer-blowout CME that erupted on 2021 September 23 and was encountered in situ by four spacecraft approximately equally distributed in heliocentric distance between 0.4 and 1 au. We employ the Open Solar Physics Rapid Ensemble Information modelling suite in ensemble mode to predict the CME arrival and structure in a hindcast fashion and to compute the ‘best-fitting’ solutions at the different spacecraft individually and together. We find that the spread in the predicted quantities increases with heliocentric distance, suggesting that there may be a maximum (angular and radial) separation between an inner and an outer probe beyond which estimates of the in situ magnetic field orientation (parametrized by flux rope model geometry) increasingly diverge. We discuss the importance of these exceptional observations and the results of our investigation in the context of advancing our understanding of CME structure and evolution as well as improving space weather forecasts. 

Abstract We describe, test, and apply a technique to incorporate full-Sun, surface flux evolution into an MHD model of the global solar corona. Requiring only maps of the evolving surface flux, our method is similar to that of Lionello et al., but we introduce two ways to correct the electric field at the lower boundary to mitigate spurious currents. We verify the accuracy of our procedures by comparing to a reference simulation, driven with known flows and electric fields. We then present a thermodynamic MHD calculation lasting one solar rotation driven by maps from the magnetic flux evolution model of Schrijver & DeRosa. The dynamic, time-dependent nature of the model corona is illustrated by examining the evolution of the open flux boundaries and forward-modeled EUV emission, which evolve in response to surface flows and the emergence and cancellation flux. Although our main goal is to present the method, we briefly investigate the relevance of this evolution to properties of the slow solar wind, examining the mapping of dipped field lines to the topological signatures of the “S-Web” and comparing charge state ratios computed in the time-dependently driven run to a steady-state equivalent. Interestingly, we find that driving on its own does not significantly improve the charge state ratios, at least in this modest resolution run that injects minimal helicity. Still, many aspects of the time-dependently driven model cannot be captured with traditional steady-state methods, and such a technique may be particularly relevant for the next generation of solar wind and coronal mass ejection models. 

Context.Coronal mass ejections (CMEs) are eruptions of plasma from the Sun that travel through interplanetary space and may encounter Earth. CMEs often enclose a magnetic flux rope (MFR), the orientation of which largely determines the CMEs’ geoeffectiveness. Current operational CME models do not model MFRs, but a number of research ones do, including the Open Solar Physics Rapid Ensemble Information (OSPREI) model. Aims.We report the sensitivity of OSPREI to a range of user-selected photospheric and coronal conditions. Methods.We modeled four separate CMEs observed in situ by Parker Solar Probe (PSP). We varied the input photospheric conditions using four input magnetograms (HMI Synchronic, HMI Synoptic, GONG Synoptic Zero-Point Corrected, and GONG ADAPT). To vary the coronal field reconstruction, we employed the Potential Field Source Surface (PFSS) model and varied its source-surface height in the range 1.5–3.0R_⊙with 0.1R_⊙increments. Results.We find that both the input magnetogram and PFSS source surface often affect the evolution of the CME as it propagates through the Sun’s corona into interplanetary space, and therefore the accuracy of the MFR prediction compared to in situ data at PSP. There is no obvious best combination of input magnetogram and PFSS source surface height. Conclusions.The OSPREI model is moderately sensitive to the input photospheric and coronal conditions. Based on where the source region of the CME is located on the Sun, there may be best practices when selecting an input magnetogram to use. 

Abstract Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. For the 2021–22 and 2022–23 influenza seasons, 26 forecasting teams provided national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one-to-four weeks ahead. Forecast skill is evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage. Six out of 23 models outperform the baseline model across forecast weeks and locations in 2021–22 and 12 out of 18 models in 2022–23. Averaging across all forecast targets, the FluSight ensemble is the 2^ndmost accurate model measured by WIS in 2021–22 and the 5^thmost accurate in the 2022–23 season. Forecast skill and 95% coverage for the FluSight ensemble and most component models degrade over longer forecast horizons. In this work we demonstrate that while the FluSight ensemble was a robust predictor, even ensembles face challenges during periods of rapid change. 

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.