Solar Wind Forecast Research Articles

Large TEC Variations Between Mars and Earth: Simulation and Observation Comparisons

AbstractLarge total electron content (TEC) variations along the line from Mars to Earth have been demonstrated by analyzing the ground received Tianwen‐1 differential one‐way range (DOR) signals when corona mass ejections (CMEs) and co‐rotating interaction regions (CIRs) pass through the signal path. Here, the TEC variations along the line from Mars to Earth are calculated from the Wang‐Sheeley‐Arge (WSA)‐Enlil space weather forecast model. The results are compared with the Tianwen‐1 line of sight (LOS) observations as well as the STEREO‐A in situ solar wind density measurements. It is found that the TEC variations calculated from the WSA‐Enlil model are usually much smaller in amplitude, especially in the cases of CIRs passing through the signal path. In addition, the timings of CMEs and CIRs passing through the DOR signal path in the WSA‐Enlil model can be half a day earlier or later than the observed ones. In comparison with in situ solar wind measurements, the ground received Tianwen‐1 DOR signals carry solar wind density information over a large spatial region along the signal path. Such remote sensing measurement can be valuable for constraining and improving global solar wind forecast models.

Using Tianwen-1 Differential One-way Range Signals to Probe Corotating Interaction Regions: Case Studies

The study reports the first detection of corotating interaction regions (CIRs) in the solar wind by the differential one-way range (DOR) signals transmitted by Tianwen-1. Differential phase delay is calculated for the DOR signals. Variations of the total electron content (TEC) along the signal path between Tianwen-1 and the ground stations are then derived from changes in the differential phase delay. Large TEC variations are found for 2021 February 11, 18, and 19 even after the influence from the Earth’s ionosphere has been removed. In situ solar wind measurements and the corresponding simulation results further show that the TEC variations are caused by the CIRs that passed through the signal path on these days. The study demonstrates that CIR structures can be probed using deep-space spacecraft range signals. Such measurements can be used to constrain global solar wind forecast models.

An Efficient, Time‐Dependent High Speed Stream Model and Application to Solar Wind Forecasts

AbstractPredicting space weather effects of the solar wind requires knowing the location and properties of any embedded high speed streams (HSSs) or stream interactions regions (SIRs) that form as the fast solar wind catches up to slow preceding wind. Additionally, this background solar wind affects the evolution of coronal mass ejections. We present the Mostly Empirical Operation Wind with a High Speed Stream (MEOW‐HiSS) model, which runs nearly instantaneously. This model is derived from magnetohydrodynamic (MHD) simulations of an idealized HSS emanating from a circular coronal hole. We split the MHD HSS radial profiles into small regions well‐described by simple functions (e.g., flat, linear, exponential, sinusoidal) that can be constrained using the MHD values. We then determine how the region boundaries and the constraining values change with CH area and the radial distance of the HSS front. MEOW‐HiSS only requires this area and distance to reproduce the corresponding radial profile with an error of less than 10% for most parameters. MEOW‐HiSS produces profiles at any subsequent times with almost no loss in accuracy. We compare MEOW‐HiSS results to four HSS observed in situ at 1 au. We present a method for determining MEOW‐HiSS inputs from extreme ultra‐violet images and use these values to hindcast the observed cases. We find average accuracies of 2.8 cm−3 in the number density (50% error over the full profile), 56.7 km/s in the radial velocity (10%), 2.2 nT in the absolute radial magnetic field (50%), 1.6 nT in the absolute longitudinal magnetic field (50%), and 7 × 104 K in the temperature (50%).

An Empirical Relationship Between Coronal Density and Solar Wind Velocity in the Middle Corona With Applications to Space Weather

AbstractAccurate 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.

Improvements to the Empirical Solar Wind Forecast (ESWF) model

The empirical solar wind forecast (ESWF) model in its current version 2.0 runs as a space-safety service in the frame of ESA’s Heliospheric Weather Expert Service Centre. The ESWF model forecasts the solar-wind speed at Earth with a lead time of 4 days. The algorithm uses an empirical relation found between the area of solar coronal-holes (CHs), as observed in EUV within a 15^{circ} meridional slice, and the in-situ measured solar-wind speed at 1 AU. This relation however, forecasts Gaussian type speed profiles, as the CH rotates in and out of the meridional slice, causing some discrepancy in the timing between forecasted and observed solar-wind speed. With adaptations to the ESWF 2.0 algorithm we improve the precision and accuracy of the ESWF speed profiles. For that we implement compression and rarefaction effects occurring between solar-wind streams of different velocities in interplanetary space. By considering the propagation times for plasma parcels between the Sun and Earth and their interactions, we achieve the asymmetrical shape of the speed profile that is characteristic of high-speed streams (HSS). By further implementing CH segmentation, co-latitude information and dynamic thresholding, we find that the newly developed ESWF 3.2 performs significantly better than ESWF 2.0. For a sample of 294 different HSSs, we derive a relative increase in hits of the timing and peak velocity by 13.9%. The Pearson correlation coefficient increases by 14.3% from 0.35 to 0.40.

Quantifying the Effect of ICME Removal and Observation Age for in Situ Solar Wind Data Assimilation

AbstractAccurate space weather forecasting requires advanced knowledge of the solar wind conditions in near‐Earth space. Data assimilation (DA) combines model output and observations to find an optimum estimation of reality and has led to large advances in terrestrial weather forecasting. It is now being applied to space weather forecasting. Here, we use solar wind DA with in‐situ observations to reconstruct solar wind speed in the ecliptic plane between 30 solar radii and Earth's orbit. This is used to provide solar wind speed hindcasts. Here, we assimilate observations from the Solar Terrestrial Relations Observatory and the near‐Earth data set, OMNI. Analysis of two periods of time, one in solar minimum and one in solar maximum, reveals that assimilating observations from multiple spacecraft provides a more accurate forecast than using any one spacecraft individually. The age of the observations also has a significant impact on forecast error, whereby the mean absolute error (MAE) sharply increases by up to 23% when the forecast lead time first exceeds the corotation time associated with the longitudinal separation between the observing spacecraft and the forecast location. It was also found that removing coronal mass ejections from the DA input and verification time series reduces the forecast MAE by up to 10% as it removes false streams from the forecast time series. This work highlights the importance of an L5 space weather monitoring mission for near‐Earth solar wind forecasting and suggests that an additional mission to L4 would further improve future solar wind DA forecasting capabilities.

Unifying the validation of ambient solar wind models

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.

Improving Solar Wind Forecasting Using Data Assimilation

AbstractData Assimilation (DA) has enabled huge improvements in the skill of terrestrial operational weather forecasting. In this study, we use a variational DA scheme with a computationally efficient solar wind model and in situ observations from STEREO‐A, STEREO‐B and ACE. This scheme enables solar‐wind observations far from the Sun, such as at 1 AU, to update and improve the inner boundary conditions of the solar wind model (at 30 solar radii). In this way, observational information can be used to improve estimates of the near‐Earth solar wind, even when the observations are not directly downstream of the Earth. This allows improved initial conditions of the solar wind to be passed into forecasting models. To this effect, we employ the HUXt solar wind model to produce 27‐day forecasts of the solar wind during the operational lifetime of STEREO‐B (November 01, 2007–September 30, 2014). In near‐Earth space, we compare the accuracy of these DA forecasts with both non‐DA forecasts and simple corotation of STEREO‐B observations. We find that 27‐day root mean square error (RMSE) for STEREO‐B corotation and DA forecasts are comparable and both are significantly lower than non‐DA forecasts. However, the DA forecast is shown to improve solar wind forecasts when STEREO‐B's latitude is offset from the Earth, which is an issue for corotation forecasts. And the DA scheme enables the representation of the solar wind in the whole model domain between the Sun and the Earth to be improved, which will enable improved forecasting of CME arrival time and speed.

Medium‐Range Forecasting of Solar Wind: A Case Study of Building Regression Model With Space Weather Forecast Testbed (SWFT)

AbstractThe Space Weather Forecast Testbed (SWFT) is developed by a team of space weather scientists and mathematicians at the University of Southern California (USC) and Jet Propulsion Laboratory (JPL) to foster the creation of models for space weather forecast by exploration of existing historic data using techniques of machine learning. As an example to demonstrate the potential power of SWFT, we present in this paper a multilinear regression‐based forecast model for solar wind. Solar wind is one of the key drivers for numerous physics‐based models for space weather including thermosphere and ionosphere models. Many attempts have been made to produce forecasts for the solar wind. SWFT provides a unified framework for forecast model formulation, training, and performance assessment. In particular, the preparation of training and validation data by SWFT takes into account the realistic constraints on data latency and forecast lead time. In developing a solar wind forecast model, SWFT allows fast exploration of many metaparameters such as the list of predictive variables and their time history used in constructing a model. We present the impact of metaparameter selection, as well as performance relative to existing solar wind forecast models.

Effect of Additional Magnetograph Observations From Different Lagrangian Points in Sun‐Earth System on Predicted Properties of Quasi‐Steady Solar Wind at 1 AU

AbstractModeling the space weather conditions for a near‐Earth environment depends on a proper representation of magnetic fields on the Sun. There are discussions in the community with respect to the value of observations taken at several Lagrangian points (L1–L5) in the Sun‐Earth system. Observations from a single (e.g., Earth/L1) vantage point are insufficient to characterize rapid changes in magnetic field on the far side of the Sun. Nor can they represent well the magnetic fields near the solar poles. However, if the changes in sunspot activity were moderate, how well would our predictions of the solar wind based on a single viewing point work? How much improvement could we see by adding magnetograph observations from L5, L4, and even L3? Here, we present the results of our recent modeling, which shows the level of improvement in forecasting the properties of the solar wind at Earth made possible by using additional observations from different vantage points during a period of moderate evolution of sunspot activity. As an example, we also show the improvements to the solar wind forecast from adding a single observation of the southern polar area from out‐of‐ecliptic spacecraft at −30° heliographic latitude vantage point.

Forecasting High‐Speed Solar Wind Streams Based on Solar Extreme Ultraviolet Images

AbstractHigh‐speed solar wind streams that originate from coronal holes play an important role in space weather disturbances, especially during the declining phase of the solar cycle. Space weather forecasters attempt to find good coronal hole indices that can be used to predict high‐speed streams days in advance. Several indices related to the coronal hole area, brightness, or magnetic field expansion factor have been reported in the literature. Empirical solar wind forecast models have been developed and used in operational service by several organizations by constructing prediction functions that relate the coronal hole index to the solar wind speed. In this paper, we present a new empirical modeling method and test its validity by comparing it with a previously reported method when applied to different coronal hole indices. In total, six empirical models are tested for a long period of time (2011–2018), with a 27‐day persistence model as a comparison benchmark. The results show that while all these empirical models can capture the temporal patterns of the solar wind observations well, the new modeling method and utilization of a composite coronal hole index PCH as an input parameter indeed improves the forecast accuracy. The high‐speed streams can be predicted approximately 3 days in advance, with a probability of detection of 0.78, a positive predictive value of 0.73, and a threat score of 0.61. The uncertainty of the high‐speed stream arrival time is approximately 1 day and the uncertainty of the peak speed is approximately 80 km/s.

Towards Construction of a Solar Wind \u201cReanalysis\u201d Dataset: Application to the First Perihelion Pass of Parker Solar Probe

Accurate reconstruction of global solar-wind structure is essential for connecting remote and in situ observations of solar plasma, and hence understanding formation and release of solar wind. Information can routinely be obtained from photospheric magnetograms, via coronal and solar-wind modelling, and directly from in situ observations, typically at large heliocentric distances (most commonly near 1 AU). Magnetogram-constrained modelling has the benefit of reconstructing global solar-wind structure, but with relatively large spatial and/or temporal errors. In situ observations, on the other hand, make accurate temporal measurements of solar-wind structure, but are highly localised. We here use a data assimilative (DA) approach to combine these two sources of information as a first step towards producing a solar-wind “reanalysis” dataset that optimally combines model and observation. The physics of solar wind stream interaction is used to extrapolate in heliocentric distance, while the assumption of steady-state solar-wind structure enables extrapolation in longitude. The major challenge is extrapolating in latitude. Using solar-wind speed during the interval of the first perihelion pass of Parker Solar Probe (PSP) in November 2018 as a test bed, we investigate two approaches. The first is to assume the solar wind is two-dimensional and thus has no latitudinal structure within the {pm}, 7^{circ } bounded by the heliographic equatorial and ecliptic planes. The second assumes in situ solar-wind observations are representative of some (small) latitudinal range. We show how observations of the inner heliosphere, such as will be provided by PSP, can be exploited to constrain the latitudinal representivity of solar-wind observations to improve future solar-wind reconstruction and space-weather forecasting.

Extracting Inner‐Heliosphere Solar Wind Speed Information From Heliospheric Imager Observations

AbstractWe present evidence that variability in the STEREO‐A Heliospheric Imager (HI) data is correlated with in situ solar wind speed estimates from WIND, STEREO‐A, and STEREO‐B. For 2008–2012, we compute the variability in HI differenced images in a plane‐of‐sky shell between 20 to 22.5 solar radii and, for a range of position angles, compare daily means of HI variability and in situ solar wind speed estimates. We show that the HI variability data and in situ solar wind speeds have similar temporal autocorrelation functions. Carrington rotation periodicities are well documented for in situ solar wind speeds, but, to our knowledge, this is the first time they have been presented in statistics computed from HI images. In situ solar wind speeds from STEREO‐A, STEREO‐B, and WIND are all are correlated with the HI variability, with a lag that varies in a manner consistent with the longitudinal separation of the in situ monitor and the HI instrument. Unlike many approaches to processing HI observations, our method requires no manual feature tracking; it is automated, is quick to compute, and does not suffer the subjective biases associated with manual classifications. These results suggest we could possibly estimate solar wind speeds in the low heliosphere directly from HI observations. This motivates further investigation, as this could be a significant asset to the space weather forecasting community; it might provide an independent observational constraint on heliospheric solar wind forecasts, through, for example, data assimilation. Finally, these results are another argument for the potential utility of including a HI on an operational space weather mission.

A Variational Approach to Data Assimilation in the Solar Wind

AbstractVariational data assimilation (DA) has enabled huge improvements in the skill of operational weather forecasting. In this study, we use a simple solar‐wind propagation model to develop the first solar‐wind variational DA scheme. This scheme enables solar‐wind observations far from the Sun, such as at 1 AU, to update and improve the inner‐boundary conditions of the solar wind model (at 30 solar radii). In this way, observational information can be used to improve estimates of the near‐Earth solar wind, even when the observations are not directly downstream of the Earth. Using controlled experiments with synthetic observations, we demonstrate this method's potential to improve solar wind forecasts, though the best results are achieved in conjunction with accurate initial estimates of the solar wind. The variational DA scheme is also applied to Solar‐Terrestrial Relations Observatory (STEREO) in situ observations using initial solar wind conditions supplied by a coronal model of the observed photospheric magnetic field. We consider the period October 2010 to October 2011, when the STEREO spacecraft were approximately 80∘ ahead/behind Earth in its orbit. For 12 of 13 Carrington Rotations, assimilation of STEREO data improves the near‐Earth solar wind estimate over the nonassimilated state, with a 18.4% reduction in the root mean square error. The largest gains are made by the DA during times when the steady‐state assumption of the coronal models breaks down. While applying this pure variational approach to complex solar‐wind models is technically challenging, we discuss hybrid DA approaches which are simpler to implement and may retain many of the advantages demonstrated here.

Flow-line Wandering in the Turbulent Solar Wind and Space Environment Forecasts

Abstract Space environment forecasts are based on ab initio modeling of the solar wind (SW) wherein solar magnetic fields and plasmas are propagated from an initial/boundary model source surface in the lower solar corona out to 1 au. Testing of these space environment forecasts relative to in situ measurements at 1 au typically shows uncertainties in the arrival times of fast SW streams of the order of a day, with broad distributions, means/medians of the order of half a day, and large variations between models but no definite winner. Here the effect of flow-line (FL) wandering due to the higher frequency velocity fluctuations within the turbulent SW on the arrival-time statistics of parcels of SW plasma transiting through the inner heliosphere out to 1 au is evaluated for a range of cutoff timescales in the velocity fluctuations. Used for this evaluation are in situ SW velocity measurements onboard Wind at 1 au, detailed spectral analysis of these measurements, WKB extrapolations to the inner heliosphere and simple application of a newly extended theoretical calculation of the mean SWFL cross-flow and “flow-aligned” displacements from the measured spectra. It is found that the velocity fluctuations near 1 au have little effect on the arrival times. The effect of the velocity fluctuations increases sunward, however, to a level sufficient to explain the large and broadly distributed “uncertainties” found in the testing of the forecasts.

Assessing the Quality of Models of the Ambient Solar Wind.

In this paper we present an assessment of the status of models of the global Solar Wind in the inner heliosphere. We limit our discussion to the class of models designed to provide solar wind forecasts, excluding those designed for the purpose of testing physical processes in idealized configurations. In addition, we limit our discussion to modeling of the 'ambient' wind in the absence of coronal mass ejections. In this assessment we cover use of the models both in forecast mode and as tools for scientific research. We present a brief history of the development of these models, discussing the range of physical approximations in use. We discuss the limitations of the data inputs available to these models and its impact on their quality. We also discuss current model development trends.

Cost‐Loss Analysis of Ensemble Solar Wind Forecasting: Space Weather Use of Terrestrial Weather Tools

AbstractThis commentary concerns recent work on solar wind forecasting by Owens and Riley (2017, https://doi.org/10.1002/2017SW001679). The approach taken makes effective use of tools commonly used in terrestrial weather—notably, via use of a simple model—generation of an “ensemble” forecast, and application of a “cost‐loss” analysis to the resulting probabilistic information, to explore the benefit of this forecast to users with different risk appetites. This commentary aims to highlight these useful techniques to the wider space weather audience and to briefly discuss the general context of application of terrestrial weather approaches to space weather.

PFSS-Based Solar Wind Forecast and the Radius of the Source-Surface

AbstractThe potential-field source-surface (PFSS) model of the solar corona is a widely used tool in the space weather research and operations. In particular, the PFSS model is used in solar wind forecast models which empirically associate solar wind properties with the numerically derived coronal magnetic field. In the PFSS model, the spherical surface where magnetic field lines are forced to open is typically placed at 2.5 solar radii. However, the results presented here suggest that setting this surface (the source-surface) to lower heights can provide a better agreement between observed and modelled coronal holes during the current solar cycle. Furthermore, the lower heights of the source-surface provide a better match between observed and forecasted solar wind speed.

High Speed Solar Wind Forecast Model from the Solar Surface to 1AU Using Global 3D MHD Simulation

AbstractEmanating from coronals holes (CHs), high speed streams (HSSs) cause recurrent geomagnetic disturbances in the Earth’s magnetosphere. For this reason being able to predict the occurrence and timing of the high speed solar wind is one of the more important issues in space weather forecasting. Currently, it is still difficult to estimate the effect of a CH in case that it extends from high latitudes to lower ones. To monitor the global solar wind condition we have therefore developed a three-dimensional MHD simulation code, the REProduce Plasma Universe (REPPU) code, that is driven by the solar magnetic field from the solar surface to 1AU. The connectivity of magnetic field lines from CHs to Earth’s orbit via HSSs has been investigated. Simulation results are presented and the usefulness of our model is discussed.

The Role of Empirical Space-Weather Models (in a World of Physics-Based Numerical Simulations)

AbstractAdvanced forecasting of space weather requires prediction of near-Earth solar-wind conditions on the basis of remote solar observations. This is typically achieved using numerical magnetohydrodynamic models initiated by photospheric magnetic field observations. The accuracy of such forecasts is being continually improved through better numerics, better determination of the boundary conditions and better representation of the underlying physical processes. Thus it is not unreasonable to conclude that simple, empirical solar-wind forecasts have been rendered obsolete. However, empirical models arguably have more to contribute now than ever before. In addition to providing quick, cheap, independent forecasts, simple empirical models aid in numerical model validation and verification, and add value to numerical model forecasts through parameterization, uncertainty estimation and ‘downscaling’ of sub-grid processes.