Evaluating Solar Wind Forecast Using Magnetic Maps That Include Helioseismic Far-Side Information

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%.

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation C. Nick Arge, Carl J. Henney, Irene Gonzalez Hernandez, W. Alex Toussaint, Josef Koller, Humberto C. Godinez; Modeling the corona and solar wind using ADAPT maps that include far-side observations. AIP Conference Proceedings 13 June 2013; 1539 (1): 11–14. https://doi.org/10.1063/1.4810977 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

The current progression toward solar maximum provides a unique opportunity to use multi-perspective spacecraft observations together with numerical models to better understand the evolution and propagation of coronal mass ejections (CMEs). Of interest to both the scientific and forecasting communities are the Earth-directed “halo” CMEs, since they typically produce the most geoeffective events. However, determining the actual initial geometries of halo CMEs is a challenge due to the plane-of-sky projection effects. Thus the recent 15 February 2011 halo CME event has been selected for this study. During this event the Solar TErrestrial RElations Observatory (STEREO) A and B spacecraft were fortuitously located ∼ 90° away from the Sun–Earth line such that the CME was viewed as a limb event from these two spacecraft, thereby providing a more reliable constraint on the initial CME geometry. These multi-perspective observations were utilized to provide a simple geometrical description that assumes a cone shape for a CME to calculate its angular width and central position. The event was simulated using the coupled Wang–Sheeley–Arge (WSA)-Enlil 3D numerical solar corona-solar wind model. Daily updated global photospheric magnetic field maps were used to drive the background solar wind. To improve our modeling techniques, the sensitivity of the modeled CME arrival times to the initial input CME geometry was assessed by creating an ensemble of numerical simulations based on multiple sets of cone parameters for this event. It was found that the accuracy of the modeled arrival times not only depends on the initial input CME geometry, but also on the accuracy of the modeled solar wind background, which is driven by the input maps of the photospheric field. To improve the modeling of the background solar wind, the recently developed data-assimilated magnetic field synoptic maps produced by the Air Force Data Assimilative Photospheric flux Transport (ADAPT) model were used. The ADAPT maps provide a more instantaneous snapshot of the global photospheric field distribution than that provided by traditional daily updated synoptic maps. Using ADAPT to drive the background solar wind, an ensemble set of eight different CME arrival times was generated, where the spread in the predictions was ∼ 13 hours and was nearly centered on the observed CME shock arrival time.

Accurate predictions of ambient solar wind conditions are a central component of space weather forecasting. A recent advancement is to use the distribution of electron density at a heliocentric distance of 8 R⊙, gained by applying coronal rotational tomography to coronagraph data, as an inner boundary condition for the time‐dependent Heliospheric Upwind eXtrapolation solar wind model. This approach requires conversion of densities into solar wind velocity at the inner boundary. Based on comparison of the distribution of in situ measurements of density and velocities, this work finds a scaled exponential equation relating the density and outflow velocity at 8 R⊙, with three key parameters found as a function of time between years 2007–2021. Based on this relationship, comparison of modeled and in situ measurements of velocities at Earth, STEREO A and STEREO B over the past solar cycle give a mean absolute error of 61.2, 69.0, and 66.1 km s−1 respectively. An analysis of thousands of events (defined as solar wind streams above 450 km s−1) gives an accuracy score of 76%. This agreement validates the density‐velocity relationship, and shows that an inner boundary based on coronagraph observations is a robust complement, or alternative, to commonly‐used magnetic model constraints for solar wind modeling and forecasting.

The properties and the spatial distribution of the large-scale structures of the Solar Corona (SC) determine the observed solar wind structure at 1 AU. Coronal Holes (CHs) are the primary source of the fast solar wind, which is the most geoeffective component of solar wind, and they appear as large dark patches in the Extreme Ultraviolet (EUV) images from the Atmospheric Imaging Assembly (AIA) on the Solar Dynamic Observatory (SDO) and the Extreme Ultraviolet Imaging Telescope (EIT) on the Solar and Heliospheric Observatory (SoHO). These observatories provide images of the SC at different wavelengths, which enables the identification of CH morphology and other large-scale structures along a given line of sight. It is crucial to understand the CH regions and their properties for effective space weather forecasting. This work is part of the WindTRUST project, with the primary goal of improving the reliability of solar wind models for space weather forecasting. Here, we aim to develop an automatic threshold-based CH detection tool for predictions across solar cycles 23, 24, and 25. We also plan to integrate this CH detection tool into a solar wind model validation pipeline, creating a fully automated validation system that provides a quantitative assessment of predictions. We categorized the large-scale features of the SC, such as active regions, solar flares, coronal mass ejections (CMEs), and filaments, based on their spatial distribution, phase of the solar cycle, and additional properties, including the GOES solar flare class. A Sequential Neural Network (NN) model was then trained by optimizing the architecture of the hidden layers to achieve higher predictive accuracy. The resulting model estimates the threshold required for integration into the Coronal Hole (CH) detection scheme, thereby enabling automated, consistent identification of CH boundaries in EUV images across solar cycles 23, 24, and 25. To interpret the performance of our NN model, we divided the predicted CH results into solar minimum and maximum cases across the solar cycles 23, 24, and 25. We also provide a comparison of our CH detection results with those obtained from other detection tools. Once we identify CH contours from our model, we validate them using a diagnostic test against CH contours from the Potential Field Source Surface (PFSS) model (non-MHD) and the WindPredict (WP) model (Polytropic and Alfven Wave) (MHD). Finally, we couple the CH detection tool with the validation pipeline to develop an automation tool for solar wind predictions.

We have developed a 2D semi-empirical MHD model (1) of the solar corona and the solar wind. The model uses empirically derived electron density profiles from the white light coronagraph data measured during the Whole Sun Month observing campaign of SOHO and an empirically derived model of the magnetic field which is fit to observed coronal hole boundary which come from white light and EUV observations. The electron density model is extended into interplanetary space by using electron densities derived from the Ulysses instrument. The model also requires an estimate of the solar wind velocity as a function of heliographic latitude and the radial component of the magnetic field at 1 AU, both of which can be provided by the Ulysses spacecraft. The model makes estimates as a function of radial distance and latitude of various fluid parameters of the plasma such as flow velocity V⃗, temperature Teff, and heat flux qeff which are derived from the equations of conservations of mass, momentum and energy, respectively in the rotating frame of the Sun. The term effective indicates possible wave contributions. Both the observations as well as the model results of the various plasma properties show a clear separation at the boundary of the fast and slow solar wind. This boundary is directly established by the global magnetic field topology inferred from observations.

Magnetic field line mapping between the Sun and the Earth is important to trace the nonthermal particles. We generalize a recently developed mapping approach (B stepping) where this approach allows us to map the magnetic field lines by stepping along the local magnetic field direction. We employ an advanced solar wind model which includes acceleration, angular momentum conservation, and intrinsic non-radial velocities and magnetic fields at the inner boundary / source surface. We map the field lines using Wind spacecraft data for two solar rotation periods: one near a solar minimum between CR2118 and CR2119 and other CR1992 near a solar maximum. Maps using the accelerating solar wind model are compared with the maps using a constant solar wind model. On a broad scale, maps using two solar wind models for the same solar rotation periods are very similar. However, in a small scale, there are significant differences, e.g. differences are evident in connectivities, paths, and winding angles. In addition, field lines using the accelerating solar wind model are more azimuthally oriented for during the solar maximum. These differences demonstrate the significance of inclusion of the accelerating radial speed profile, intrinsic azimuthal velocity and magnetic field components.

Kinetic models are necessary to describe the physical processes associated with non-Maxwellian velocity distribution functions (VDFs) of electrons or ions in the solar corona and wind. It is shown that pitch-angle scattering of electrons in the solar wind needs to be considered in kinetic solar wind models. Coulomb collisions are not efficient enough to provide this scattering, but resonant interaction with whistler waves is. A solar wind model for undisturbed fast wind is presented, and the influence of scattering on flare electron propagation is investigated. Furthermore, it is found that resonant interaction of electrons with whistler waves is capable of producing suprathermal tails of electron distributions even under quiet conditions without flare activity.

The ambient solar wind flows and fields influence the complex propagation dynamics of coronal mass ejections in the interplanetary medium and play an essential role in shaping Earth’s space weather environment. A critical scientific goal in the space weather research and prediction community is to develop, implement, and optimize numerical models for specifying the large-scale properties of solar wind conditions at the inner boundary of the heliospheric model domain. Here we present an adaptive prediction system that fuses information from in situ measurements of the solar wind into numerical models to better match the global solar wind model solutions near the Sun with prevailing physical conditions in the vicinity of Earth. In this way, we attempt to advance the predictive capabilities of well-established solar wind models for specifying solar wind speed, including the Wang–Sheeley–Arge model. In particular, we use the Heliospheric Upwind eXtrapolation (HUX) model for mapping the solar wind solutions from the near-Sun environment to the vicinity of Earth. In addition, we present the newly developed Tunable HUX (THUX) model, which solves the viscous form of the underlying Burgers equation. We perform a statistical analysis of the resulting solar wind predictions for the period 2006–2015. The proposed prediction scheme improves all the investigated coronal/heliospheric model combinations and produces better estimates of the solar wind state at Earth than our reference baseline model. We discuss why this is the case and conclude that our findings have important implications for future practice in applied space weather research and prediction.

The spatial distributions of the magnetic field, plasma density, and current at distances of (20–400)RS from the Sun (where RS is the solar radius) are investigated within a stationary axisymmetric MHD model of the solar wind (SW) at all latitudes in the inertial frame of reference with the origin at the center of the Sun. The model takes into account differential (with respect to the heliolatitude) rotation of the Sun and full corotation of plasma inside a boundary sphere of radius 20RS, which breaks down beyond this sphere. Self-consistent distributions of the plasma density, current, and magnetic field in the SW are obtained by numerically solving a set of time-independent MHD equations in spherical coordinates. It is demonstrated that the calculated results do not contradict observational data and describe a gradual transition from the fast SW at high heliolatitudes to the slow SW at low heliolatitudes, as well as the steepening of the profiles of the main SW characteristics with increasing distance from the Sun. The obtained dependences extend understanding of the SW structure at low and high latitudes and agree with the well-known Parker model in the limit of a small Ampère force.

The evolving ambient solar wind is one of the key links between the Sun and planetary bodies in our solar system. Here we present a comprehensive catalogue of solar wind properties, stream interaction regions, and coronal mass ejections at different locations in the inner heliosphere. Our database incorporates observational data products and also solar wind modelling results. The solar wind modelling is based on two different approaches for modelling the conditions in the ambient solar wind. While the WSA/THUX model combination solves the viscous form of the underlying Burgers equation to compute the two-dimensional solar wind conditions in our solar system, the second approach is a computationally fast machine learning method for predicting the ambient solar wind flows at Earth. Statistics of the ambient solar wind model results for more than 15 years in combination with a catalogue of coronal mass ejections observed at the Earth, Mars and STEREO satellites along with stream interaction regions provide a comprehensive overview of the past and present solar wind behaviour for shaping planetary space weather.

<div> <p>In terrestrial weather prediction, Data Assimilation (DA) has enabled huge improvements in operational forecasting capabilities. It does this by producing more accurate initial conditions and/or model parameters for forecasting; reducing the impacts of the “butterfly effect”. However, data assimilation is still in its infancy in space weather applications and it is not quantitatively understood how DA can improve space weather forecasts.</p> <p>To this effect, we have used a solar wind DA scheme to assimilate observations from STEREO A, STEREO B and ACE over the operational lifetime of STEREO-B (2007-2014). This scheme allows observational information at 1AU to update and improve the inner boundary of the solar wind model (at 30 solar radii). These improved inner boundary conditions are then input into the efficient solar wind model, HUXt, to produce forecasts of the solar wind over the next solar rotation.</p> <p>In this talk, I will be showing that data assimilation is capable of improving solar wind predictions not only in near-Earth space, but in the whole model domain, and compare these forecasts to corotation of observations from STEREO-B at Earth. I will also show that the DA forecasts are capable of reducing systematic errors that occur to latitudinal offset in STEREO-B’s corotation forecast.</p> </div>

As a star spins-down during the main sequence, its wind properties are affected. In this work, we investigate how Earth’s magnetosphere has responded to the change in the solar wind. Earth’s magnetosphere is simulated using 3D magnetohydrodynamic models that incorporate the evolving local properties of the solar wind. The solar wind, on the other hand, is modelled in 1.5D for a range of rotation rates Ω from 50 to 0.8 times the present-day solar rotation (Ω⊙). Our solar wind model uses empirical values for magnetic field strengths, base temperature, and density, which are derived from observations of solar-like stars. We find that for rotation rates ≃10 Ω⊙, Earth’s magnetosphere was substantially smaller than it is today, exhibiting a strong bow shock. As the Sun spins-down, the magnetopause standoff distance varies with Ω−0.27 for higher rotation rates (early ages, ≥1.4 Ω⊙) and with Ω−2.04 for lower rotation rates (older ages, <1.4 Ω⊙). This break is a result of the empirical properties adopted for the solar wind evolution. We also see a linear relationship between the magnetopause distance and the thickness of the shock on the subsolar line for the majority of the evolution (≤10 Ω⊙). It is possible that a young fast rotating Sun would have had rotation rates as high as 30–50 Ω⊙. In these speculative scenarios, at 30 Ω⊙, a weak shock would have been formed, but for 50 Ω⊙, we find that no bow shock could be present around Earth’s magnetosphere. This implies that with the Sun continuing to spin-down, a strong shock would have developed around our planet and remained for most of the duration of the solar main sequence.

Comets tails can reveal information about the local solar wind conditions. They can exhibit various signatures of interactions with the solar wind including bending, developing kinks, and sometimes undergoing tail disconnections. In this study, we investigate comet tail disconnection events observed in the STEREO HI data during the period of 2007–2023. Using the Heliospheric Upwind eXtrapolation model with a time-dependency (HUXt) solar wind model alongside novel solar wind data assimilation (DA) techniques, each disconnection event was investigated to determine its cause. The resulting statistical analysis led to three main conclusions: (1) for every heliospheric current sheet (HCS) crossing predicted by HUXt that occurs when the comet is within the region of influence of DA, a tail disconnection follows; (2) for HCS crossings that occur outside the region where DA can be applied, 54.5% are followed by a tail disconnection; and (3) there is an approximately linear relationship between the speed of the solar wind at the HCS crossing and the time delay to the onset of a disconnection given by the equation V rel (km s−1) = (2.23 ± 0.35)Δt (hr) + (400 ± 10)(km s−1).

<p><span>The ambient solar wind flows and fields influence the complex propagation dynamics of coronal mass ejections in the interplanetary medium and play an essential role in shaping Earth's space weather environment. A critical scientific goal in the space weather research and prediction community is to develop, implement and optimize numerical models for specifying the large-scale properties of solar wind conditions at the inner boundary of the heliospheric model domain. Here we present an adaptive prediction system that fuses information from in situ measurements of the solar wind into numerical models to better match the global solar wind model solutions near the Sun with prevailing physical conditions in the vicinity of Earth. In this way, we attempt to advance the predictive capabilities of well-established solar wind models such as the Wang-Sheeley-Arge model. We perform a statistical analysis of the resulting solar wind predictions for the years 2006 to 2015. The proposed prediction scheme improves all the coronal/heliospheric model combinations investigated by approximately 15-20 percent in terms of various comprehensive prediction validation measures. We discuss why this is the case, and conclude that our findings have important implications for future practice in applied space weather research and prediction.</span></p>