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In a previous study, Huang et al. used the Alfvén Wave Solar atmosphere Model, one of the widely used solar wind models in the community, driven by ADAPT-GONG magnetograms to simulate the solar wind in the last solar cycle and found that the optimal Poynting flux parameter can be estimated from either the open field area or the average unsigned radial component of the magnetic field in the open field regions. It was also found that the average energy deposition rate (Poynting flux) in the open field regions is approximately constant. In the current study, we expand the previous work by using GONG magnetograms to simulate the solar wind for the same Carrington rotations and determine if the results are similar to the ones obtained with ADAPT-GONG magnetograms. Our results indicate that similar correlations can be obtained from the GONG maps. Moreover, we report that ADAPT-GONG magnetograms can consistently provide better comparisons with 1 au solar wind observations than GONG magnetograms, based on the best simulations selected by the minimum of the average curve distance for the solar wind speed and density.

Solar wind forecasting plays a crucial role in space weather prediction, yet significant uncertainties persist duet to incomplete magnetic field observations of the Sun. Isolating the solar wind forecasting errors due to these effects is difficult. This study investigates the uncertainties in solar wind models arising from these limitations. We simulate magnetic field maps with known uncertainties, including far-side and polar field variations, as well as resolution and sensitivity limitations. These maps serve as input for three solar wind models: the Wang–Sheeley–Arge, the Heliospheric Upwind eXtrapolation, and the European Heliospheric FORecasting Information Asset. We analyze the discrepancies in solar wind forecasts, particularly the solar wind speed at Earth’s location, by comparing the results of these models to a created ground truth magnetic field map, which is derived from a synthetic solar rotation evolution using the Advective Flux Transport model. The results reveal significant variations within each model with a root mean square error ranging from 59 to 121 km s−1. Further comparison with the thermodynamic Magnetohydrodynamic Algorithm outside a Sphere model indicates that uncertainties in the different models can lead to even larger variations in solar wind forecasts compared to those within a single model. However, predicting a range of solar wind velocities based on a cloud of points around Earth can help mitigate uncertainties by up to 20%–77%.

We apply nested-sampling Bayesian analysis to a model for the transport of magnetohydrodynamic-scale solar wind fluctuations. The dual objectives are to obtain improved constraints on parameters present in the turbulence transport model (TTM) and to support quantitative comparisons of the quality of distinct versions of the transport model. The TTMs analyzed are essentially the 1D steady-state ones presented in Breech et al. that describe the radial evolution of the energy, correlation length, and normalized cross helicity of the fluctuations, together with the proton temperature, in prescribed background solar wind fields. Modeled effects present in the TTM include nonlinear turbulence interactions, shear driving, and energy injection associated with pickup-ions. Each of these modeled effects involves adjustable parameters that we seek to constrain using Bayesian analysis. We find that, given the TTMs and observational data sets analyzed, the most appropriate TTM to recommend corresponds to 2D fluctuations and has von Kármán–Howarth parameters of α ≈ 0.16 and β ≈ 0.10, along with reasonably standard values for the other adjustable parameters. The analysis also indicates that it is advantageous to include pickup ion effects in the lengthscale evolution equation by assuming Z2β/αλ is locally conserved. Such Bayesian analysis is readily extended to more sophisticated solar wind models, space weather models, and might lead to improved predictions of, for example, solar flare and coronal mass ejection interactions with the Earth.

We explore the capabilities of time-dependent (TD) magnetohydrodynamic (MHD) solar wind simulations with the coupled Wang–Sheeley–Arge (WSA) model of the solar corona and the Grid Agnostic MHD for Extended Research Applications model of the inner heliosphere. We compare TD with steady-state (SS) simulations and in situ observations from multiple spacecraft (Earth, STEREO-A, Parker Solar Probe). We show that TD predictions, although better than SS predictions, substantially mispredict the solar wind at different heliospheric locations. We identified three reasons for that: (1) the uncalibrated WSA velocity formula used to generate solar wind velocities at the inner boundary of a heliospheric domain, (2) the extraction of the WSA boundary conditions for input into MHD models very high in the corona, and (3) the abrupt and partial emergence of active regions from the solar east limb. Evaluation of 1 year of TD predictions at the Earth and STEREO-A locations shows that tuning accordingly the WSA relationship when used with MHD models and extracting the WSA boundary conditions lower in the corona (at 5 Rs instead of 21.5 Rs) can lead to improved predictions. However, the abrupt emergence of active regions from the east limb of the Sun, which can highly disrupt the magnetic field topology in the corona, is a difficult task to deal with since complete knowledge of the conditions on the solar far side is not currently available. Solar Orbiter Polarimetric and Helioseismic Imager data can help mitigate this effect; however, unless we get a 4π view of the Sun, we will be unable to completely address it.

The physical processes in the solar corona that shape the solar wind remain an active research topic. Modeling efforts have shown that energy and plasma exchanges near the transition region play a crucial role in modulating solar wind properties. Although these regions cannot be measured in situ, plasma parameters can be inferred from coronal spectroscopy and ionization states of heavy ions, which remain unchanged as they escape the corona. We introduce a new solar wind model extending from the chromosphere to the inner heliosphere, capturing thermodynamic coupling across atmospheric layers. By including neutral and charged particle interactions, we model the transport and ionization processes of the gas through the transition region and corona and into the solar wind. Instead of explicitly modeling coronal heating, we link its spatial distribution to large-scale magnetic field properties. Our results confirm that energy deposition strongly affects wind properties through key mechanisms involving chromospheric evaporation, thermal expansion, and magnetic flux expansion. For sources near active regions, the model predicts significant solar wind acceleration, with plasma outflows comparable to those inferred from coronal spectroscopy. For winds from large coronal holes, the model reproduces the observed anticorrelation between charge state and wind speed. However, the predicted charge state ratios are overall lower than observed. Inclusion of a population of energetic electrons enhances both heavy ion charge states and solar wind acceleration, improving agreement with observations.

Jupiter's polar aurorae deliver significant heating at the poles, thought to spread across the planet through atmospheric winds. Additionally, ground‐based Keck observations have revealed a large‐scale high‐temperature region, spatially distinct from the aurorae. Here, we investigate the origins and characteristics of the feature using Keck data, in‐situ Juno spacecraft measurements, and solar wind modeling. Juno exited the magnetosphere on approach to Jupiter, coinciding with modeled high‐speed solar wind impact that compressed the magnetosphere. This hot feature may be dynamic, transported equatorward by winds following auroral activity enhancements from magnetospheric compression akin to a large‐scale traveling ionospheric disturbance on Earth, or driven by the inner magnetosphere particle precipitation. Exploring the dynamic case, we calculated equatorward velocities ranging from 0.46 to 2.02 km s−1${\\mathrm{s}}^{-1}$ , similar to those seen at Earth. Our study underscores the importance of the solar wind at all planets, exemplified by its ability to alter Jupiter's upper‐atmospheric energy balance globally. Plain Language Summary Jupiter's powerful aurorae release vast amounts of energy into the planet's upper atmosphere, primarily in the polar regions. Normally, temperatures decrease gradually toward the equator, reflecting how auroral energy is redistributed across the planet. However, a recent discovery revealed a large, high‐temperature region far from the aurorae, disrupting this typical pattern. In this study, data from NASA's Juno spacecraft and solar wind models indicate that strong solar winds likely compressed Jupiter's magnetic field several hours before this hot region appeared. This compression may have intensified auroral heating, driving the hot region away from the auroral zone. Alternatively, the region could have been heated by a yet unknown process. In either case, prior solar wind activity appears to have been the key trigger. Key Points Jupiter's sub‐auroral upper‐atmospheric temperature was seen 200 K elevated in a region measuring 180° longitude by 8° latitude Juno data and solar wind modeling demonstrate that the Jovian magnetosphere was compressed several hours prior by fast solar wind streams The hot feature may drift equatorward from the aurora at 1.1 ±$\\pm $  0.2 km s−1${\\mathrm{s}}^{-1}$ , or be driven by a novel magnetospheric energy source

Optical observations of the solar corona provide key information on its magnetic geometry. The large-scale open field of the corona plays an important role in shaping the ambient solar wind and constraining the propagation dynamics of the embedded structures, such as interplanetary coronal mass ejections. Rigorous analysis of the open-flux coronal regions based on coronagraph images can be quite challenging because of the depleted plasma density, resulting in low signal-to-noise ratios. In this paper, we present an in-depth description of a new image segmentation methodology, the Quasi-Radial Field-line Tracing (QRaFT), enabling the detection of optical coronal features indicating the orientation of the steady-state open magnetic field. The methodology is tested using synthetic coronagraph images generated by a three-dimensional magnetohydrodynamic model. The results of the numerical tests indicate that the extracted optical features are aligned within ∼4°–7° with the local magnetic field in the underlying numerical solution. We also demonstrate the performance of the method on real-life coronal images obtained from a space-borne coronagraph and a ground-based camera. We argue that QRaFT outputs contain valuable empirical information about the global steady-state morphology of the corona, which could help improve the accuracy of coronal and solar wind models and space weather forecasts.

The von Kármán-Howarth equations give a starting basis for the classical turbulence theory. The formula for the magnetohydrodynamics von Kármán decay rate represents an energy source in many solar wind models with turbulence as the driver. However, it still lacks the radial trend comparison between the von Kármán decay rate, the energy supply rate, and the perpendicular heating rate based on direct observations of the solar wind. Here we carry out this kind of comparison for the first time using Parker Solar Probe measurements from its first three orbits. We find that the radial variation of the von Kármán decay rate is consistent with that of both the energy supply rate and the heating rate in the slow solar wind. These results support the idea that the von Kármán decay law is an active process responsible for solar wind heating. These results also suggest a new idea that both the von Kármán decay law and the low-frequency break sweeping may be controlled by the same nonlinear process. Some limitations of the present study are also addressed.