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We employ Parker Solar Probe (PSP) observations during the latest solar minimum period (years 2018 -2021) to calibrate the version of the Wang-Sheeley-Arge (WSA) coronal model used in the European space weather forecasting tool EUHFORIA. WSA provides a set of boundary conditions at 0.1 au necessary to initiate the heliospheric part of EUHFORIA, namely, the domain extending beyond the solar Alfvenic point. To calibrate WSA, we observationally constrain four constants in the WSA semi-empirical formula based on PSP observations. We show how the updated (after the calibration) WSA boundary conditions at 0.1 au are compared to PSP observations at similar distances, and we further propagate these conditions in the heliosphere according to EUHFORIAs magnetohydrodynamic (MHD) approach. We assess the predictions at Earth based on the Dynamic Time Warping technique. Our findings suggest that, for the period of interest, the WSA configurations which resembled optimally the PSP observations close to the Sun, were different than the ones needed to provide better predictions at Earth. One reason for this discrepancy can be attributed to the scarcity of fast solar wind velocities recorded by PSP. The calibration of the model was performed based on unexpectedly slow velocities that did not allow us to achieve generally and globally improved solar wind predictions, compared to older studies. Other reasons can be attributed to missing physical processes from the heliospheric part of EUHFORIA but also the fact that the currently employed WSA relationship, as coupled to the heliospheric MHD domain, may need a global reformulation beyond that of just updating the four constant factors that were taken into account in this study.

Coronal holes are recognized as the primary sources of heliospheric open magnetic flux (OMF). However, a noticeable gap exists between in-situ measured OMF and that derived from remote sensing observations of the Sun. In this study, we investigate the OMF evolution and its connection to solar structures throughout 2014, with special emphasis on the period from September to October, where a sudden and significant OMF increase was reported. By deriving the OMF evolution at 1au, modeling it at the source surface, and analyzing solar photospheric data, we provide a comprehensive analysis of the observed phenomenon. First, we establish a strong correlation between the OMF increase and the solar magnetic field derived from a Potential Field Source Surface (PFSS) model ($cc_{\mathrm{Pearson}}=0.94$). Moreover, we find a good correlation between the OMF and the open flux derived from solar coronal holes ($cc_{\mathrm{Pearson}}=0.88$), although the coronal holes only contain $14-32\%$ of the Sun's total open flux. However, we note that while the OMF evolution correlates with coronal hole open flux, there is no correlation with the coronal hole area evolution ($cc_{\mathrm{Pearson}}=0.0$). The temporal increase in OMF correlates with the vanishing remnant magnetic field at the southern pole, caused by poleward flux circulations from the decay of numerous active regions months earlier. Additionally, our analysis suggests a potential link between the OMF enhancement and the concurrent emergence of the largest active region in solar cycle 24. In conclusion, our study provides insights into the strong increase in OMF observed during September to October 2014., Comment: accepted in ApJ

A plethora of coronal models, from empirical to more complex magnetohydrodynamic (MHD) ones, are being used for reconstructing the coronal magnetic field topology and estimating the open magnetic flux. However, no individual solution fully agrees with coronal hole observations and in situ measurements of open flux at 1~AU, as there is a strong deficit between model and observations contributing to the known problem of the missing open flux. In this paper we investigate the possible origin of the discrepancy between modeled and observed magnetic field topology by assessing the effect on the simulation output by the choice of the input boundary conditions and the simulation set up, including the choice of numerical schemes and the parameter initialization. In the frame of this work, we considered four potential field source surface based models and one fully MHD model, different types of global magnetic field maps and model initiation parameters. After assessing the model outputs using a variety of metrics, we conclude that they are highly comparable regardless of the differences set at initiation. When comparing all models to coronal hole boundaries extracted by extreme ultraviolet (EUV) filtergrams we find that they do not compare well. This miss-match between observed and modeled regions of open field is a candidate contributing to the open flux problem.

The concept of surface-flux transport (SFT) is commonly used in evolving models of the large-scale solar surface magnetic field. These photospheric models are used to determine the large-scale structure of the overlying coronal magnetic field, as well as to make predictions about the fields and flows that structure the solar wind. We compare predictions from two SFT models for the solar wind, open magnetic field footpoints, and the presence of coronal magnetic null points throughout various phases of a solar activity cycle, focusing on the months of April in even-numbered years between 2012 and 2020, inclusive. We find that there is a solar cycle dependence to each of the metrics considered, but there is not a single phase of the cycle in which all the metrics indicate good agreement between the models. The metrics also reveal large, transient differences between the models when a new active region is rotating into the assimilation window. The evolution of the surface flux is governed by a combination of large scale flows and comparatively small scale motions associated with convection. Because the latter flows evolve rapidly, there are intervals during which their impact on the surface flux can only be characterized in a statistical sense, thus their impact is modeled by introducing a random evolution that reproduces the typical surface flux evolution. We find that the differences between the predicted properties are dominated by differences in the model assumptions and implementation, rather than selection of a particular realization of the random evolution., Comment: Accepted for publication in The Astrophysical Journal

The arrival time prediction of Coronal mass ejections (CMEs) is an area of active research. Many methods with varying levels of complexity have been developed to predict CME arrival. However, the mean absolute error (MAE) of predictions remains above 12 hours, even with the increasing complexity of methods. In this work we develop a new method for CME arrival time prediction that uses magnetohydrodynamic simulations involving data-constrained flux-rope-based CMEs, which are introduced in a data-driven solar wind background. We found that, for 6 CMEs studied in this work, the MAE in arrival time was ~8 hours. We further improved our arrival time predictions by using ensemble modeling and comparing the ensemble solutions with STEREO-A&B heliospheric imager data. This was done by using our simulations to create synthetic J-maps. A machine learning (ML) method called the lasso regression was used for this comparison. Using this approach, we could reduce the MAE to ~4 hours. Another ML method based on the neural networks (NNs) made it possible to reduce the MAE to ~5 hours for the cases when HI data from both STEREO-A&B were available. NNs are capable of providing similar MAE when only the STEREO-A data is used. Our methods also resulted in very encouraging values of standard deviation (precision) of arrival time. The methods discussed in this paper demonstrate significant improvements in the CME arrival time predictions. Our work highlights the importance of using ML techniques in combination with data-constrained magnetohydrodynamic modeling to improve space weather predictions.

Flux-rope-based magnetohydrodynamic modeling of coronal mass ejections (CMEs) is a promising tool for the prediction of the CME arrival time and magnetic field at Earth. In this work, we introduce a constant-turn flux rope model and use it to simulate the 12-July-2012 16:48 CME in the inner heliosphere. We constrain the initial parameters of this CME using the graduated cylindrical shell (GCS) model and the reconnected flux in post-eruption arcades. We correctly reproduce all the magnetic field components of the CME at Earth, with an arrival time error of approximately 1 hour. We further estimate the average subjective uncertainties in the GCS fittings, by comparing the GCS parameters of 56 CMEs reported in multiple studies and catalogs. We determined that the GCS estimates of the CME latitude, longitude, tilt, and speed have average uncertainties of 5.74 degrees, 11.23 degrees, 24.71 degrees, and 11.4% respectively. Using these, we have created 77 ensemble members for the 12-July-2012 CME. We found that 55% of our ensemble members correctly reproduce the sign of the magnetic field components at Earth. We also determined that the uncertainties in GCS fitting can widen the CME arrival time prediction window to about 12 hours for the 12-July-2012 CME. On investigating the forecast accuracy introduced by the uncertainties in individual GCS parameters, we conclude that the half-angle and aspect ratio have little impact on the predicted magnetic field of the 12-July-2012 CME, whereas the uncertainties in longitude and tilt can introduce a relatively large spread in the magnetic field predicted at Earth.

We explore the use of observed polar coronal holes (CHs) to constrain the flux distribution within the polar regions of global solar magnetic field maps in the absence of reliable quality polar field observations. Global magnetic maps, generated by the Air Force Data Assimilative Photospheric flux Transport (ADAPT) model, are modified to enforce field unipolarity thresholds both within and outside observed CH boundaries. The polar modified and unmodified maps are used to drive Wang-Sheeley-Arge (WSA) models of the corona and solar wind (SW). The WSA predicted CHs are compared with the observations, and SW predictions at the WIND and Ulysses spacecraft are also used to provide context for the new polar modified maps. We find that modifications of the polar flux never worsen and typically improve both the CH and SW predictions. We also confirm the importance of the choice of the domain over which WSA generates the coronal magnetic field solution but find that solutions optimized for one location in the heliosphere can worsen predictions at other locations. Finally, we investigate the importance of low-latitude (i.e., active region) magnetic fields in setting the boundary of polar CHs, determining that they have at least as much impact as the polar fields themselves., Comment: 23 pages, 10 figures, 2 tables, 4 appendices, 1 data repository. Accepted for publication in ApJ

Progress in space weather research and awareness needs community-wide strategies and procedures to evaluate our modeling assets. Here we present the activities of the Ambient Solar Wind Validation Team embedded in the COSPAR ISWAT initiative. We aim to bridge the gap between model developers and end-users to provide the community with an assessment of the state-of-the-art in solar wind forecasting. To this end, we develop an open online platform for validating solar wind models by comparing their solutions with in situ spacecraft measurements. The online platform will allow the space weather community to test the quality of state-of-the-art solar wind models with unified metrics providing an unbiased assessment of progress over time. In this study, we propose a metadata architecture and recommend community-wide forecasting goals and validation metrics. We conclude with a status update of the online platform and outline future perspectives.

Coronal holes are the observational manifestation of the solar magnetic field open to the heliosphere and are of pivotal importance for our understanding of the origin and acceleration of the solar wind. Observations from space missions such as the Solar Dynamics Observatory now allow us to study coronal holes in unprecedented detail. Instrumental effects and other factors, however, pose a challenge to automatically detect coronal holes in solar imagery. The science community addresses these challenges with different detection schemes. Until now, little attention has been paid to assessing the disagreement between these schemes. In this COSPAR ISWAT initiative, we present a comparison of nine automated detection schemes widely-applied in solar and space science. We study, specifically, a prevailing coronal hole observed by the Atmospheric Imaging Assembly instrument on 2018 May 30. Our results indicate that the choice of detection scheme has a significant effect on the location of the coronal hole boundary. Physical properties in coronal holes such as the area, mean intensity, and mean magnetic field strength vary by a factor of up to 4.5 between the maximum and minimum values. We conclude that our findings are relevant for coronal hole research from the past decade, and are therefore of interest to the solar and space research community., Comment: Accepted for publication in The Astrophysical Journal. (Received January 20, 2021; Accepted March 25, 2021)

For the past 30+ years, the magnetic expansion factor ($f_s$) has been used in empirical relationships to predict solar wind speed ($v_{obs}$) at 1 AU, based on an inverse relationship between these two quantities. Coronal unipolar streamers (i.e. pseudostreamers) undergo limited field line expansion, resulting in $f_s$-dependent relationships to predict fast wind associated with these structures. However, case studies have shown that in situ observed pseudostreamer solar wind was much slower than that derived with $f_s$. To investigate this further, we conduct a statistical analysis to determine if $f_s$ and $v_{obs}$ are inversely correlated for a large sample of periods when pseudostreamer wind was observed at multiple 1 AU spacecraft (i.e. ACE, STEREO-A/B). We use the Wang-Sheeley-Arge (WSA) model driven by Air Force Data Assimilative Photospheric Flux Transport (ADAPT) photospheric field maps to identify 38 periods when spacecraft observe pseudostreamer wind. We compare the expansion factor of the last open field lines on either side of a pseudostreamer cusp with the corresponding in situ measured solar wind speed. We find only slow wind ($v_{obs}$ $<$ 500 km/s) is associated with pseudostreamers, and that there is not a significant correlation between $f_s$ and $v_{obs}$ for these field lines. This suggests that field lines near the open-closed boundary of pseudostreamers are not subject to the steady-state acceleration along continuously open flux tubes assumed in the $f_s$-$v_{obs}$ relationship. In general, dynamics at the boundary between open and closed field lines such as interchange reconnection will invalidate the steady-state assumptions of this relationship., Comment: 18 pages, 7 figures, 2 tables, appendix with 3 additional tables

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 (WSA) 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 time 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 magnetic fields of interplanetary coronal mass ejections (ICMEs), which originate close to the Sun in the form of a flux rope, determine their geoeffectiveness. Therefore, robust flux rope-based models of CMEs are required to perform magnetohydrodynamic (MHD) simulations aimed at space weather predictions. We propose a modified spheromak model and demonstrate its applicability to CME simulations. In this model, such properties of a simulated CME as the poloidal and toroidal magnetic fluxes, and the helicity sign can be controlled with a set of input parameters. We propose a robust technique for introducing CMEs with an appropriate speed into a background, MHD solution describing the solar wind in the inner heliosphere. Through a parametric study, we find that the speed of a CME is much more dependent on its poloidal flux than on the toroidal flux. We also show that the CME speed increases with its total energy, giving us control over its initial speed. We further demonstrate the applicability of this model to simulations of CME-CME collisions. Finally, we use this model to simulate the 12 July 2012 CME and compare the plasma properties at 1 AU with observations. The predicted CME properties agree reasonably with observational data.

The ambient solar wind conditions in interplanetary space and in the near-Earth environment are determined by activity on the Sun. Steady solar wind streams modulate the propagation behaviour of interplanetary coronal mass ejections and are themselves an important driver of recurrent geomagnetic storm activity. The knowledge of the ambient solar wind flows and fields is thus an essential component of successful space weather forecasting. Here, we present an implementation of an operational framework for operating, validating and optimizing models of the ambient solar wind flow on the example of Carrington Rotation 2077. We reconstruct the global topology of the coronal magnetic field using the potential field source surface model (PFSS) and the Schatten current sheet model (SCS), and discuss three empirical relationships for specifying the solar wind conditions near the Sun, namely the Wang-Sheeley (WS) model, the distance from the coronal hole boundary (DCHB) model, and the Wang-Sheeley-Arge (WSA) model. By adding uncertainty in the latitude about the sub-Earth point, we select an ensemble of initial conditions and map the solutions to Earth by the Heliospheric Upwind eXtrapolation (HUX) model. We assess the forecasting performance from a continuous variable validation, and find that the WSA model most accurately predicts the solar wind speed time series. We note that the process of ensemble forecasting slightly improves the forecasting performance of all solar wind models investigated. We conclude that the implemented framework is well suited for studying the relationship between coronal magnetic fields and the properties of the ambient solar wind flow in the near-Earth environment.

The Parker Solar Probe (PSP) completed its first solar encounter in 2018 November, bringing it closer to the Sun than any previous mission. This allowed in situ investigation of the heliospheric current sheet (HCS) inside the orbit of Venus. The Parker observations reveal a well defined magnetic sector structure placing the spacecraft in a negative polarity region for most of the encounter. The observed current sheet crossings are compared to the predictions of both potential field source surface and magnetohydrodynamic models. All the model predictions are in good qualitative agreement with the observed crossings of the HCS. The models also generally agree that the HCS was nearly parallel with the solar equator during the inbound leg of the encounter and more significantly inclined during the outbound portion. The current sheet crossings at PSP are also compared to similar measurements made by the Wind spacecraft near Earth at 1 au. After allowing for orbital geometry and propagation effects, a remarkable agreement has been found between the observations of these two spacecraft underlying the large-scale stability of the HCS. Finally, the detailed magnetic field and plasma structure of each crossing is analyzed. Marked differences were observed between PSP and Wind measurements in the type of structures found near the HCS. This suggests that significant evolution of these small solar wind structures takes place before they reach 1 au.

The paper presents the results from a multi-year effort to develop and validate image processing methods forselecting the best physical models based on solar image observations. The approach consists of selecting the physical models based on their agreement with coronal holes extracted from the images. Ultimately, the goal is to use physical models to predict geomagnetic storms. We decompose the problem into three subproblems: (i) coronal hole segmentation based on physical constraints, (ii) matching clusters of coronal holes between different maps, and (iii) physical map classification. For segmenting coronal holes, we develop a multi-modal method that uses segmentation maps from three different methods to initialize a level-set method that evolves the initial coronal hole segmentation to the magnetic boundary. Then, we introducea new method based on Linear Programming for matchingclusters of coronal holes. The final matching is then performedusing Random Forests. The methods were carefully validatedusing consensus maps derived from multiple readers, manualclustering, manual map classification, and method validation for50 maps. The proposed multi-modal segmentation method significantly outperformed SegNet, U-net, Henney-Harvey, and FCNby providing accurate boundary detection. Overall, the methodgave a 95.5% map classification accuracy.

When a coronal mass ejection departs, it leaves behind a temporary void. That void is known as coronal dimming, and it contains information about the mass ejection that caused it. Other physical processes can cause parts of the corona to have transient dimmings, but mass ejections are particularly interesting because of their influence in space weather. Prior work has established that dimmings are detectable even in disk-integrated irradiance observations, i.e., Sun-as-a-star measurements. The present work evaluates four years of continuous Solar Dynamics Observatory Extreme Ultraviolet Experiment (EVE) observations to greatly expand the number of dimmings we may detect and characterize, and collects that information into James’s EVE Dimming Index catalog. This paper details the algorithms used to produce the catalog, provides statistics on it, and compares it with prior work. The catalog contains 5051 potential events (rows), which correspond to all robustly detected solar eruptive events in this time period as defined by >C1 flares. Each row has a corresponding 27,349 elements of metadata and parameterizations (columns). In total, this catalog is the result of analyzing 7.6 million solar ultraviolet light curves.