Michal Ben-Nun

The magnetic fields that make up the internal structure of coronal mass ejections (CMEs) are thought to be organized in a flux rope configuration, consisting of twisted magnetic fields that wind about a central axis. Remote-sensing observations of CMEs show a wide range of morphologies, dynamics, and evolution, including rotation, nonuniform expansion, deflection, and interaction with the ambient solar wind. In situ measurements, however, typically consist of a single 1D spacecraft trajectory through a large 3D structure (or a few at best), restricting our ability to determine how CME magnetic structure varies in space and time. In this work, we analyze the magnetic configuration of two idealized CMEs during their early evolution in the range of 1–30 R _⊙ using the Magnetohydrodynamic Algorithm Outside a Sphere code. The initial flux ropes differ only in their orientation with respect to the global dipole and erupt from a bipolar active region below the streamer belt in a simplified coronal environment. We deploy a fleet of synthetic spacecraft throughout the CMEs’ paths at different combinations of latitudes, longitudes, and heliocentric distances. We identify and examine flux rope signatures in the synthetic in situ profiles to characterize radial variations as well as latitudinal/longitudinal ones. We find that, even in a simplified CME erupting under solar minimum–like conditions, the sampling location significantly affects the global structure that would be deduced from common flux rope reconstruction and analysis techniques used for in situ measurements. Additionally, we consider possible in situ signatures that may serve as indicators of the source region’s topology.

Forecasting influenza hospitalizations informs public health preparedness, yet questions remain about which types of forecasts best guide action. We evaluate categorical trend forecasts, which communicate probabilities of upcoming increases or decreases in epidemic trajectories, submitted to CDC’s FluSight Forecasting Challenge between Fall-2024 and Spring-2026. Teams submitted probability distributions over five categories describing direction and magnitude of week-over-week changes in laboratory-confirmed influenza hospital admissions. We assessed performance using Ranked Probability Skill Score, Brier Skill Score, and measures of forecast-observation agreement. Most models outperformed an equal-probability baseline; the FluSight ensemble ranked among the top three in the 2024–25 and 2025–26 seasons. Forecasts were most accurate during stable periods and least during periods of rapid change, with most models underestimating observed trends. Conclusions were robust to choice of scoring metric and reference model. These results support categorical trend ensembles as an approach to communicating infectious disease forecasts that may inform public health decision-making.

In recent years, the use of multi-model ensemble projections in infectious disease modeling has become an established methodological approach to account for and integrate across uncertainties and structural differences present in individual models. However, the creation of long-term ensemble projections through these coordinated efforts is resource-intensive, demanding the input of multiple research teams and substantial computational power. This typically limits the ability to refine projections, update the selection of plausible epidemic trajectories, or expand the number of scenarios that can be assessed, even as new empirical data become available. To address this challenge, we define an adaptive ensemble approach that, analogously to a multi-model particle filtering method, dynamically selects individual model trajectories based on observed data throughout the epidemic projection period. We demonstrate the effectiveness of this methodology using the U.S. Flu Scenario Modeling Hub (SMH) projections for influenza hospitalizations in the United States during the 2023-2024 and 2024-2025 winter seasons. Our findings show that the adaptive ensemble yields improved predictive accuracy with respect to the original SMH ensemble projections across several scoring rules and geographical resolutions. Furthermore, the adaptive ensemble approach offers two additional applications: i) the dynamic assignment of posterior probabilities to epidemic scenarios, identifying the most plausible scenario, and representing how reality is captured by a combination of scenarios, and ii) the potential use for short-term forecasting. The adaptive ensemble approach is able to identify the most likely scenarios for the 2023-2024 and 2024-2025 U.S. influenza seasons, even in the early stages of the epidemic. It outperforms, retrospectively, a baseline model in short-term forecasting of influenza hospitalizations in the United States during the two seasons across various horizons and scoring rules, showing potential to contribute to real-time collaborative forecasting challenges such as CDC’s FluSight. The proposed approach offers an efficient or low-resource strategy to increase the impact of multi-model epidemic projections by providing real-time support to modeling teams, public health authorities, and decision-makers.

Six years after its emergence, SARS-CoV-2 continues to have a substantial burden, however, the impact of vaccination and the optimal timing of its rollout remain uncertain. To explore these uncertainties, the US Scenario Modeling Hub convened its 19th round of ensemble projections for COVID-19 hospitalizations and deaths in the United States. Eight teams provided outcomes for each US state and nationally from April 2025 to April 2026 under five scenarios regarding vaccine recommendations and timing. We assessed recommendations with two eligibility scenarios (high-risk individuals only and all-eligible) and two timing scenarios (classic start: mid-August, earlier start: late June). These were crossed to create four scenarios and were compared against a counterfactual scenario with no vaccination. We found that compared to no vaccination, our ensemble projections estimated 90,000 (95% PI 53,000-126,000) hospitalizations averted in the high-risk and classic timing scenario across the US. Expanding coverage averted an additional 26,000 (95% PI 14,000-39,000) hospitalizations, which when coupled with earlier vaccination timing further reduced national hospitalizations by 15,000 (95% PI −3,000-33,000). These findings estimate significant benefits from a broad all-eligible vaccination recommendation, and suggest an additional benefit is likely to be gained from an earlier vaccination campaign.

Coronal mass ejections are known drivers of large-scale waves in the low corona. However, wave dynamics in the extended corona and inner heliosphere remain largely unexplored. Here, we report the first observational and numerical evidence of coherent global compressive oscillations in the outer corona and inner heliosphere, revealed by white-light Solar and Heliospheric Observatory Large Angle and Spectrometric Coronagraph (LASCO) C3 data and an MHD simulation. Analyzing the coronal mass ejection event of 2012 July 23 using spectral proper orthogonal decomposition, we isolate two distinct wave signatures: (1) a directional fast-mode shock-like compressive wave that dissipates completely within 3 hr, and (2) a large-scale global circular wave front consistent with fast-mode MHD behavior, lasting 7 hr and extending across the LASCO C3 field of view, marking the first detection of such a global oscillation. Our findings reveal a previously unrecognized component of coronal mass ejection–driven wave activity, providing new constraints on the dynamics of the extended corona and inner heliosphere.

Coronal mass ejections (CMEs) are known drivers of large-scale waves in the low corona. However, wave dynamics in the extended corona and inner heliosphere remain largely unexplored. Here, we report the first observational and numerical evidence of coherent global compressive oscillations in the outer corona and inner heliosphere, revealed by white-light SOHO/LASCO C3 data and an MHD simulation. Analyzing the CME event of 2012 July 23 using Spectral Proper Orthogonal Decomposition (SPOD), we isolate two distinct wave signatures: (1) a directional fast-mode shock-like compressive wave that dissipates completely within ~3 hours, and (2) a large-scale global circular wavefront consistent with fast-mode MHD behavior, lasting ~7 hours and extending across the LASCO C3 field of view, marking the first detection of such a global oscillation. Our findings reveal a previously unrecognized component of CME-driven wave activity, providing new constraints on the dynamics of the extended corona and inner heliosphere.

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.

Understanding the large-scale three-dimensional structure of the inner heliosphere, while important in its own right, is crucial for space weather applications, such as forecasting the time of arrival and propagation of coronal mass ejections (CMEs). This study uses sunRunner3D (3D), a 3-D magnetohydrodynamic (MHD) model, to simulate solar wind (SW) streams and generate background states. SR3D employs the boundary conditions generated by corona-heliosphere (CORHEL) and the PLUTO code to compute the plasma properties of the SW with the MHD approximation up to 1.1 AU in the inner heliosphere. We demonstrate that SR3D reproduces global features of corotating interaction regions (CIRs) observed by Earth-based spacecraft (OMNI) and the Solar Terrestial Relations Observatory (STEREO)-A for a set of Carrington rotations (CRs) that cover a period that lays in the late declining phase of solar cycle 24. Additionally, we demonstrate that the model solutions are valid in the corotating and inertial frames of references. Moreover, a comparison between SR3D simulations and in situ measurements shows reasonable agreement with the observations, and our results are comparable to those achieved by Predictive Science Inc.’s Magnetohydrodynamic Algorithm outside a Sphere (MAS) code. We have also undertaken a comparative analysis with the Space Weather Adaptive Simulation Framework for Solar Wind (SWASTi-SW), a PLUTO physics-based model, to evaluate the precision of various initial boundary conditions. Finally, we discuss the disparities in the solutions derived from inertial and rotating frames.

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^nd most accurate model measured by WIS in 2021–22 and the 5^th most 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.

This paper explores the effects of numerical algorithms on global magnetohydrodynamics simulations of solar wind (SW) in the inner heliosphere. To do so, we use sunRunner3D, a 3-D magnetohydrodynamics model that employs the boundary conditions generated by CORHEL and the PLUTO code to compute the plasma properties of the SW with the ideal magnetohydrodynamics approximation up to 1.1 AU in the inner heliosphere. Mainly, we define three different combinations of numerical algorithms based on their diffusion level. This diffusion level is related to the way of solving the magnetohydrodynamics equations using the finite volume formulation, and, therefore, we set in terms of the divergence-free condition methods, Riemann solvers, variable reconstruction schemes, limiters, and time-steeping algorithms. According to the simulation results, we demonstrate that sunRunner3D reproduces global features of Corotating Interaction Regions observed by Earth-based spacecraft for a set of Carrington rotations that cover a period that lays in the late declining phase of solar cycle 24, independently of the numerical algorithms. Moreover, statistical analyses between models and in-situ measurements show reasonable agreement with the observations, and remarkably, the high diffusive method matches better with in-situ data than low diffusive methods.