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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 The trajectories of coronal mass ejections (CMEs) are often seen to deviate substantially from a purely radial propagation direction. Such deviations occur predominantly in the corona and have been attributed to “channeling” or deflection of the eruptive flux by asymmetric ambient magnetic fields. Here, we investigate an additional mechanism that does not require any asymmetry of the preeruptive ambient field. Using magnetohydrodynamic numerical simulations, we show that the trajectories of CMEs through the solar corona can significantly deviate from the radial direction when propagation takes place in a unipolar radial field. We demonstrate that the deviation is most prominent below ∼15R_⊙and can be attributed to an “effectiveI×Bforce” that arises from the intrusion of a magnetic flux rope with a net axial electric current into a unipolar background field. These results are important for predictions of CME trajectories in the context of space-weather forecasts, as well as for reaching a deeper understanding of the fundamental physics underlying CME interactions with the ambient fields in the extended solar corona. 

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.