Improving solar wind forecasting using Data Assimilation

<p>Accurate space weather forecasting requires 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 to reconstruct the conditions from 30 solar radii to Earth's orbital radius and over all longitudes and produce solar wind speed forecasts. In this study, we assimilate observations from the Solar Terrestrial Relations Observatory (STEREO) and the Advanced Composition Explorer (ACE). Analysis of two periods of time, one in solar minimum and one in solar maximum, reveals that assimilating observations from multiple spacecraft is preferable over observations from a single spacecraft. The age of the observations also has an impact on forecast error, whereby the mean absolute error (MAE) increases by up to 23% when the forecast lead time exceeds the time associated with the longitudinal separation between the observing spacecraft and the forecast location. It was also found that removing CMEs from the DA input observations acts to reduce the forecast MAE by up to 10% through removal of false streams in the forecast time series. This work adds further evidence to the usefulness of the L5 space weather monitoring mission, but also shows that a mission to L4 would aid in future solar wind DA forecasting capabilities. </p>

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).

To model the structure and dynamics of the heliosphere well enough for high-quality forecasting, it is essential to accurately estimate the global solar magnetic field used as inner boundary condition in solar wind models. However, our understanding of the photospheric magnetic field topology is inherently constrained by the limitation of systematically observing the Sun from only one vantage point, Earth. To address this challenge, we introduce global magnetic field maps that assimilate far-side active regions derived from helioseismology into solar wind modeling. Through a comparative analysis between the combined surface flux transport and helioseismic Far-side Active Region Model (FARM) and the base Surface Flux Transport Model without far-side active regions (SFTM), we assess the feasibility and efficacy of incorporating helioseismic far-side information in space weather forecasting. We are employing the Wang-Sheeley-Arge solar wind (WSA) model for statistical evaluation and leveraging the EUropean Heliospheric FOrecasting Information Asset (EUHFORIA), a three-dimensional heliospheric MHD model, to analyze a case study. Using the WSA model, we show that including far-side magnetic data improves solar wind forecasts for 2013 – 2014 by up to 50% in correlation and 3% in root mean square error and mean absolute error, especially near the Earth and Solar TErrestrial RElations Observatory – Ahead (STEREO-A). Additionally, our 3D modeling shows significant localized differences in heliospheric structure that can be attributed to the presence or absence of active regions in the magnetic maps used as input boundaries. This highlights the importance of including far-side information to more accurately model and predict space weather effects caused by solar wind, solar transients, and geomagnetic disturbances.

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 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.

We have assembled and tested, in real time, a space weather modeling system that starts at the Sun and extends to the Earth through a set of coupled, modular components. We describe recent efforts to improve the Hakamada‐Akasofu‐Fry (HAF) solar wind model that is presently used in our geomagnetic storm prediction system. We also present some results of these improvement efforts. In a related paper, Akasofu [2001] discusses the results of the first 2 decades using this system as a research tool and for space weather predictions. One key goal of our efforts is to provide quantitative forecasts of geoeffective solar wind conditions at the L1 satellite point and at Earth. Notably, we are addressing a key problem for space weather research: the prediction of the north‐south component (Bz) of the interplanetary magnetic field. This parameter is important for the transfer of energy from the solar wind to the terrestrial environment that results in space weather impacts upon society. We describe internal improvements, the incorporation of timely and accurate boundary conditions based upon solar observations, and the prediction of solar wind speed, density, magnetic field, and dynamic pressure. HAF model predictions of shock arrival time at the L1 satellite location are compared with the prediction skill of the two operational shock propagation models: the interplanetary shock propagation model (ISPM) and the shock‐time‐of‐arrival (STOA) model. We also show model simulations of shock propagation compared with interplanetary scintillation observations. Our modeling results provide a new appreciation of the importance of accurately characterizing event drivers and for the influences of the background heliospheric plasma on propagating interplanetary disturbances.

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.

<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>

Abstract. Data assimilation (DA) in geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction and is a crucial building block that has allowed dramatic improvements in weather forecasting over the past few decades. DA is commonly framed in a variational setting, where one solves an optimization problem within a Bayesian formulation using raw model forecasts as a prior and observations as likelihood. This leads to a DA objective function that needs to be minimized, where the decision variables are the initial conditions specified to the model. In traditional DA, the forward model is numerically and computationally expensive. Here we replace the forward model with a low-dimensional, data-driven, and differentiable emulator. Consequently, gradients of our DA objective function with respect to the decision variables are obtained rapidly via automatic differentiation. We demonstrate our approach by performing an emulator-assisted DA forecast of geopotential height. Our results indicate that emulator-assisted DA is faster than traditional equation-based DA forecasts by 4 orders of magnitude, allowing computations to be performed on a workstation rather than a dedicated high-performance computer. In addition, we describe accuracy benefits of emulator-assisted DA when compared to simply using the emulator for forecasting (i.e., without DA). Our overall formulation is denoted AIEADA (Artificial Intelligence Emulator-Assisted Data Assimilation).

Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction, and is a crucial building block that has allowed dramatic improvements in weather forecasting over the past few decades. DA is commonly framed in a variational setting, where one solves an optimization problem within a Bayesian formulation using raw model forecasts as a prior, and observations as likelihood. This leads to a DA objective function that needs to be minimized, where the decision variables are the initial conditions specified to the model. In traditional DA, the forward model is numerically and computationally expensive. Here we replace the forward model with a low-dimensional, data-driven, and differentiable emulator. Consequently, gradients of our DA objective function with respect to the decision variables are obtained rapidly via automatic differentiation. We demonstrate our approach by performing an emulator-assisted DA forecast of geopotential height. Our results indicate that emulator-assisted DA is faster than traditional equation-based DA forecasts by four orders of magnitude, allowing computations to be performed on a workstation rather than a dedicated high-performance computer. In addition, we describe accuracy benefits of emulator-assisted DA when compared to simply using the emulator for forecasting (i.e., without DA). Our overall formulation is denoted AIAEDA (Artificial Intelligence Emulator Assisted Data Assimilation).

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%.

Radiation Belt Storm Probes: A New Mission for Space Weather Forecasting

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.

Commission 49 covers research on the solar wind, shocks and particle acceleration, both transient and steady-state, e.g., corotating, structures within the heliosphere, and the termination shock and boundary of the heliosphere. During the last three years there was considerable progress made in studies of solar energetic particles, compositional and other signatures in the heliosphere, solar wind pickup ions, the termination shock, which was finally crossed by a spacecraft, and the boundary between the heliosphere and interstellar medium, and in solar wind modeling and space weather. These topics have been summarized here in five articles, each with extensive references that will guide the reader who wants further details. Observations from the following spacecraft have extensively used during this period: Ulysses, Cassini, Voyager 1 and 2, MESSENGER, ACE, Genesis, SOHO, Wind, and RHESSI.

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.