Space Weather Modeling Research Articles

Improved space weather observations and modeling for aviation radiation

In recent years there has been a growing interest from the aviation community for space weather radiation forecasts tailored to the needs of the aviation industry. In 2019 several space weather centers began issuing advisories for the International Civil Aviation Organization alerting users to enhancements in the radiation environment at aviation flight levels. Due to a lack of routine observations, radiation modeling is required to specify the dose rates experienced by flight crew and passengers. While mature models exist, support for key observational inputs and further modeling advancements are needed. Observational inputs required from the ground-based neutron monitor network must be financially supported for research studies and operations to ensure real-time data is available for forecast operations and actionable end user decision making. An improved understanding of the geomagnetic field is required to reduce dose rate uncertainties in regions close to the open/closed geomagnetic field boundary, important for flights such as those between the continental US and Europe which operate in this region. Airborne radiation measurements, which are crucial for model validation and improvement, are lacking, particularly during solar energetic particle events. New measurement campaigns must be carried out to ensure progress and in situ atmospheric radiation measurements made available for real-time situational awareness. Furthermore, solar energetic particle forecasting must be improved to move aviation radiation nowcasts to forecasts in order to meet customer requirements for longer lead times for planning and mitigation.

On the importance of investigating CME complexity evolution during interplanetary propagation

This perspective paper brings to light the need for comprehensive studies on the evolution of interplanetary coronal mass ejection (ICME) complexity during propagation. To date, few studies of ICME complexity exist. Here, we define ICME complexity and associated changes in complexity, describe recent works and their limitations, and outline key science questions that need to be tackled. Fundamental research on ICME complexity changes from the solar corona to 1 AU and beyond is critical to our physical understanding of the evolution and interaction of transients in the inner heliosphere. Furthermore, a comprehensive understanding of such changes is required to understand the space weather impact of ICMEs at different heliospheric locations and to improve on predictive space weather models.

Forecasting hourly [formula omitted] over Sonmiani using an artificial neural network algorithm

This article describes the efforts made to forecast daily hourly critical frequency foF2 of the ionosphere over a station at the crest of equatorial ionization anomaly (EIA) region by using artificial neural network (ANN) technique. The ANN based model is developed to forecast foF2 1-h in advance over Sonmiani (25.19°N, 66.74°E) by making use of ionosonde data of 2018–2020. The input parameters of the model include day of year (DOY), local time (Hour), 27- day running mean of solar radio flux (F10.7), running mean of 8 previous values of geomagnetic planetary Ap index (AP8) and 1-h prior value of ionosonde foF2 (foF2pObs). The model is comprising of one hidden layer with 27 neurons, tangent hyperbolic as activation function, adaptive movement estimation (Adam) as optimization algorithm and mean squared error (MSE) as cost function. The optimal configuration is obtained by training the model on 500 epochs using above-mentioned inputs and configuration. The model is then validated over unseen data of 2021. It is found that the developed ANN based model predicts 1-h ahead values of foF2 with an average RMSE of 1.0 MHz on comparing with ionosonde measurements. For the same unseen data set RMSE of 1.6 MHz is found between ionosonde measurements and IRI-Plas 2020 estimations. The developed space weather model may be further improved by incorporating long time series data in future to capture geomagnetically as well as atmospherically induced short-term variations.

The physics of solar spectral imaging observations in dm-cm wavelengths and the application on space weather

Recently, several new solar radio telescopes have been put into operation and provided spectral-imaging observations with much higher resolutions in decimeter (dm) and centimeter (cm) wavelengths. These telescopes include the Mingantu Spectral Radioheliograph (MUSER, at frequencies of 0.4–15 GHz), the Expanded Owens Valley Solar Array (EOVSA, at frequencies of 1–18 GHz), and the Siberian Radio Heliograph (SRH, at frequencies of 3–24 GHz). These observations offer unprecedented opportunities to study solar physics and space weather, especially to diagnose the coronal magnetic fields, reveal the basic nature of solar eruptions and the related non-thermal energy release, particle accelerations and propagation, and the related emission mechanisms. These results might be the important input to the space weather modeling for predicting the occurrence of disastrous powerful space weather events. In order to provide meaningful reference for other solar physicists and space weather researchers, this paper mainly focus on discussing the potential scientific problems of solar radio spectral-imaging observations in dm-cm wavelengths and its possible applications in the field of space weather. These results will provide a helpful reference for colleagues to make full use of the latest and future observation data obtained from the above solar radio telescopes.

Space weather observations, modeling, and alerts in support of human exploration of Mars

Space weather observations and modeling at Mars have begun but they must be significantly increased to support the future of Human Exploration on the Red Planet. A comprehensive space weather understanding of a planet without a global magnetosphere and a thin atmosphere is very different from our situation at Earth so there is substantial fundamental research remaining. It is expected that the development of suitable models will lead to a comprehensive operational Mars space weather alert (MSWA) system that would provide rapid dissemination of information to Earth controllers, astronauts in transit, and those in the exploration zone (EZ) on the surface by producing alerts that are delivered rapidly and are actionable. To illustrate the importance of such a system, we use a magnetohydrodynamic code to model an extreme Carrington-type coronal mass ejection (CME) event at Mars. The results show a significant induced surface field of nearly 3,000 nT on the dayside that could radically affect unprotected electrical systems that would dramatically impact human survival on Mars. Other associated problems include coronal mass ejection (CME) shock-driven acceleration of solar energetic particles producing large doses of ionizing radiation at the Martian surface. In summary, along with working more closely with international partners, the next Heliophysics Decadal Survey must include a new initiative to meet expected demands for space weather forecasting in support of humans living and working on the surface of Mars. It will require significant effort to coordinate NASA and the international community contributions.

Longitudinal Variations in Equatorial Ionospheric TEC from GPS, Global Ionosphere Map and International Reference Ionosphere-2016 during the Descending and Minimum Phases of Solar Cycle 24

Research on longitudinal discrepancies in local ionospheric variability, especially in equatorial and low-latitude regions, is a focal point of interest for the space weather modeling community. The ionosphere over these regions is influenced by complex electrodynamics, wind, and temperature dynamics that can seriously impact dynamic technological systems such as satellite tracking and positioning, satellite radio communication, and navigation control systems. Here, we researched the longitudinal variability in the ionospheric total electron content (TEC) by analyzing observed global positioning system (GPS)-derived TEC values along with those extracted from the most reliable global ionospheric maps (GIMs) and the International Reference Ionosphere (IRI-2016) model at selected stations in the vicinity of the magnetic equator along the American, African, and Asian longitude sectors. The period of study covered the descending (2016–2017) and deep solar minimum (2018–2019) years in the 24th solar cycle. Apart from the decreasing trend of the TEC from the descending to deep solar minimum period irrespective of season and longitude sector, the results showed a relatively higher magnitude of TEC in the African longitude than the other two longitude sectors. Despite evident overestimation and underestimations of TEC in both models, GIM predictions generally looked better in terms of observed variation patterns, especially in the African longitude. The study also highlights the seasonal and semiannual effects of longitudinal variations in TEC, manifesting in local time offsets and some peculiar anomalies, which seemed to be different from previously reported results, especially during the solar minimum years at the three longitude sectors. The insignificant effects of longitudinal variations on the equinoctial asymmetry are attributed to the diverse electron density distribution and ionospheric morphology at the three longitude sectors that will prompt further investigations in the future. The outcomes from this study may augment the past efforts of scientists to understand the seasonal effects of the longitudinal variations in TEC, thereby complementing the improvements of ionospheric representations in global ionosphere models and maps.

Plasma noise in TianQin time-delay interferometry

TianQin is a proposed geocentric space-based gravitational wave observatory mission, which requires time-delay interferometry (TDI) to cancel laser frequency noise. With high demands for precision, solar-wind plasma environment at $\sim 10^5$ km above the Earth may constitute a non-negligible noise source to laser interferometric measurements between satellites, as charged particles perturb the refractivity along light paths. In this paper, we first assess the plasma noises along single links from space-weather models and numerical orbits, and analyze the time and frequency domain characteristics. Particularly, to capture the plasma noise in the entire measurement band of $10^{-4} - 1$ Hz, we have performed additional space-weather magnetohydrodynamic simulations in finer spatial and temporal resolutions and utilized Kolmogorov spectra in high-frequency data generation. Then we evaluate the residual plasma noises of the first- and second-generation TDI combinations. Both analytical and numerical estimations have shown that under normal solar conditions the plasma noise after TDI is less than the secondary noise requirement. Moreover, TDI is shown to exhibit moderate suppression on the plasma noise below $\sim 10^{-2}$ Hz due to noise correlation between different arms, when compared with the secondary noise before and after TDI.

Measurements of metastable helium in Earth’s atmosphere by resonance lidar

Monitoring and predicting space weather activity is increasingly important given society’s growing reliance on space-based infrastructure but is hampered by a lack of observational data. Airglow at 1083 nm from metastable helium He(23S) in the thermosphere has long been a target for remote-sensing instruments seeking to fill that gap; however, passive measurements of He(23S) fluorescence are limited by low brightness, and interpretation of these observations is complicated by the > 500 km depth of the He(23S) layer. Here, we demonstrate a lidar instrument that is able to stimulate and detect He(23S) fluorescence, and we present measured profiles of He(23S) density. These measurements provide crucial validation to space weather models, support predictions of peak number density ( ~ 1 cm−3) and the dependence of density on altitude, solar zenith angle, and season, and extend by a factor of 4 the maximum probed altitude range by an atmospheric profiling lidar. These measurements open the door for the development of more sophisticated lidars: by applying well-established spectroscopic lidar techniques, one can measure the Doppler shift and broadening of the He(23S) line, thereby retrieving profiles of neutral wind speed and temperature, opening a window for studying space weather phenomena.

Comparing the Performance of a Solar Wind Model from the Sun to 1 au Using Real and Synthetic Magnetograms

The input of the solar wind models plays a significant role in accurate solar wind predictions at 1 au. This work introduces a synthetic magnetogram produced from a dynamo model as an input for magnetohydrodynamics simulations. We perform a quantitative study that compares the space weather modeling framework (SWMF) results for the observed and synthetic solar magnetogram input. For each case, we compare the results for extreme ultraviolet images and extract the simulation data along the Earth trajectory to compare with in situ observations. We initialize the SWMF using the real and synthetic magnetograms for a set of Carrington rotations within solar cycles 23 and 24. Our results help quantify the ability of dynamo models to be used as input to solar wind models and thus provide predictions for the solar wind at 1 au.

Comparison of Energetic Electron Fluxes Measured by GPS and THEMIS Spacecraft in the Inner Magnetosphere

AbstractKey elements of space weather models are energetic electron fluxes in the inner magnetosphere and the outer radiation belt. Flux depletion is driven by various loss processes: scattering into atmosphere, magnetopause shadowing. Flux enhancement is driven by various acceleration processes: local wave‐particle interactions, radial transport, plasma sheet injections. Many of these processes operate on ∼ hour timescales. Such mesoscale flux variations are not well traced by equatorial spacecraft with much longer orbits. Energetic electron detectors onboard the Global Positioning System (GPS) constellation provide a unique opportunity for probing such ∼ hour‐scale flux variations. Measurements from up to 23 identically instrumented GPS satellites cover a wide energy and L‐shell range with a subhour time resolution. However, their orbits are inclined and thus all measurements at L‐shell >4.3 are off‐equatorial. In this report, we present a comparison of equatorial THEMIS and nonequatorial GPS measurements of omnidirectional ≤600 keV electron fluxes. Such a comparison allows us to derive coefficients for using off‐equatorial GPS fluxes to infer the equatorial values. These coefficients depend on particle energy and L‐shell. We demonstrate a new data set derived from GPS measurements and discuss how it can be used to investigate mesoscale dynamics of energetic electron fluxes in the inner magnetosphere.

A comparative study on geoeffective and non-geoeffective corotating interaction regions

A long database of corotating interaction regions (CIRs) is used to investigate their geoeffectiveness. Among 290 CIR events identified during Solar Cycle 24 (2008–2019), 88 (30%) are found to be geoeffective in causing geomagnetic storms with the symmetric ring current index [SYM-H] peak ⩽-50nT, and 202 (70%) are non-geoeffective with the minimum SYM-H >-50nT. Based on a comparative study of the geoeffective and non-geoeffective CIRs, following results are obtained. Geoeffective and non-geoeffective CIRs have comparable duration (28±15hours, 26±14hours, respectively) and radial extent (0.33±0.17AU, 0.30±0.17AU, respectively), on average. While the mean solar-wind plasma speed during the geoeffective (503±70km s−1) and non-geoeffective (491±63km s−1) CIRs exhibits no statistical difference, the geoeffective CIRs have ≈25% higher peak plasma density, ≈45% higher ram pressure, ≈42% higher temperature, ≈39% higher interplanetary magnetic field (IMF) magnitude, ≈57% stronger IMF southward component, and ≈61% stronger reconnection electric field than the non-geoeffective events, on average. The auroral electrojet index [AE] and SYM-H are found to be, respectively, ≈58% and ≈192% stronger during the geoeffective CIRs than the non-geoeffective events. The typical characteristic solar-wind and geomagnetic activity parameters given in this article can be useful for space weather modeling and prediction purposes.

A 2D Kaleidoscope of Electron Heat Fluxes Driven by Auroral Electron Precipitation

AbstractElectron heat flux is an important value for ionospheric space weather modeling networks. Utilizing the 2D array of Time History of Events and Macroscale Interactions during Substorms all‐sky‐imager (ASI) observations, Gabrielse et al. (2021, https://doi.org/10.3389/fphy.2021.744298) described a new method that estimates the auroral scale sizes of intense precipitating electron energy fluxes and their mean energies during two substorms on 16 February 2010. These parameters in combination with SuperThermal Electron Transport code were used to develop a new methodology to calculate electron thermal fluxes from data inputs in 2D during one of the substorms at 09:40:00 UT across Canada and Alaska. To test the effect of various precipitation lifetimes on electron heat flux values, boxcar averages ranging from 0 to 900 s were applied to the ASI data. These data are then combined with the newly developed kinetic simulation to determine the thermal fluxes associated with the observed diffuse and discrete precipitation.

A New Model for Nowcasting the Aviation Radiation Environment With Comparisons to In Situ Measurements During GLEs

AbstractSignificant increases to the atmospheric radiation environment are recorded by a network of ground level neutron monitors as ground level enhancements (GLEs). These space weather phenomena pose a risk to aviation via single event effects in aircraft electronics and ionizing dose to passengers and crew. Under the UK Space Weather Instrumentation, Measurement, Modeling and Risk programme, we have developed a new model to provide nowcasts of the aviation radiation environment, including both the galactic cosmic ray (GCR) background and during GLE events. The Model for Atmospheric Ionising Radiation Effects (MAIRE+) uses multiple data sources to characterize primary GCR and GLE particle spectra and combines these with precalculated geomagnetic and atmospheric response matrices to predict particle fluxes from ground level to 20 km altitude across the entire globe. Two European neutron monitors (located at Oulu in Finland and Dourbes in Belgium) are used as the primary indicators of GLE intensity in order to maximize accuracy over UK airspace. Outputs from MAIRE+ for the historical GLEs in September and October 1989 are compared to recalibrated empirical data from a solid‐state detector that was carried on Concorde in that period. The model will be hosted in the UK and will provide additional capability to the Met Office Space Weather Operations Center (MOSWOC).

Application of Deep Learning to Solar and Space Weather Data

AbstractIn this review, we introduce our recent applications of deep learning to solar and space weather data. We have successfully applied novel deep learning methods to the following applications: (1) generation of solar farside/backside magnetograms and global field extrapolation based on them, (2) generation of solar UV/EUV images from other UV/EUV images and magnetograms, (3) denoising solar magnetograms using supervised learning, (4) generation of UV/EUV images and magnetograms from Galileo sunspot drawings, (5) improvement of global IRI TEC maps using IGS TEC ones, (6) one-day forecasting of global TEC maps through image translation, (7) generation of high-resolution magnetograms from Ca II K images, (8) super-resolution of solar magnetograms, (9) flare classification by CNN and visual explanation by attribution methods, and (10) forecasting GOES solar X-ray profiles. We present major results and discuss them. We also present future plans for integrated space weather models based on deep learning.

Kamodo: A functional API for space weather models and data

Kamodo: A functional API for space weather models and data

On the Considerations of Using Near Real Time Data for Space Weather Hazard Forecasting

AbstractSpace weather represents a severe threat to ground‐based infrastructure, satellites and communications. Accurately forecasting when such threats are likely (e.g., when we may see large induced currents) will help to mitigate the societal and financial costs. In recent years computational models have been created that can forecast hazardous intervals, however they generally use post‐processed “science” solar wind data from upstream of the Earth. In this work we investigate the quality and continuity of the data that are available in Near‐Real‐Time (NRT) from the Advanced Composition Explorer and Deep Space Climate Observatory (DSCOVR) spacecraft. In general, the data available in NRT corresponds well with post‐processed data, however there are three main areas of concern: greater short‐term variability in the NRT data, occasional anomalous values and frequent data gaps. Some space weather models are able to compensate for these issues if they are also present in the data used to fit (or train) the model, while others will require extra checks to be implemented in order to produce high quality forecasts. We find that the DSCOVR NRT data are generally more continuous, though they have been available for small fraction of a solar cycle and therefore DSCOVR has experienced a limited range of solar wind conditions. We find that short gaps are the most common, and are most frequently found in the plasma data. To maximize forecast availability we suggest the implementation of limited interpolation if possible, for example, for gaps of 5 min or less, which could increase the fraction of valid input data considerably.

Constraining Global Coronal Models with Multiple Independent Observables

Global coronal models seek to produce an accurate physical representation of the Sun’s atmosphere that can be used, for example, to drive space-weather models. Assessing their accuracy is a complex task, and there are multiple observational pathways to provide constraints and tune model parameters. Here, we combine several such independent constraints, defining a model-agnostic framework for standardized comparison. We require models to predict the distribution of coronal holes at the photosphere, and neutral line topology at the model’s outer boundary. We compare these predictions to extreme-ultraviolet (EUV) observations of coronal hole locations, white-light Carrington maps of the streamer belt, and the magnetic sector structure measured in situ by Parker Solar Probe and 1 au spacecraft. We study these metrics for potential field source surface (PFSS) models as a function of source surface height and magnetogram choice, as well as comparing to the more physical Wang–Sheeley–Arge (WSA) and the Magnetohydrodynamic Algorithm outside a Sphere (MAS) models. We find that simultaneous optimization of PFSS models to all three metrics is not currently possible, implying a trade-off between the quality of representation of coronal holes and streamer belt topology. WSA and MAS results show the additional physics that they include address this by flattening the streamer belt while maintaining coronal hole sizes, with MAS also improving coronal hole representation relative to WSA. We conclude that this framework is highly useful for inter- and intra-model comparisons. Integral to the framework is the standardization of observables required of each model, evaluating different model aspects.

Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application

Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth’s upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predict thermospheric density while providing robust and reliable uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance capabilities for models trained on both local and global datasets. We show that the NLPD loss provides similar results for both techniques but the direct probability distribution prediction method has a much lower computational cost. For the global model regressed on the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, we achieve errors of approximately 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAllenging Minisatellite Payload (CHAMP) density dataset, models developed using both techniques provide test error on the order of 13%. The CHAMP models—on validation and test data—are within 2% of perfect calibration for the twenty prediction intervals tested. We show that this model can also be used to obtain global density predictions with uncertainties at a given epoch.

Selection of the Main Control Parameters for the Dst Index Prediction Model Based on a Layer-wise Relevance Propagation Method

The prediction of the Dst index is an important subject in space weather. It has significant progress with the prevalent applications of neural networks. The selection of input parameters is critical for the prediction model of the Dst index or other space-weather models. In this study, we perform a layer-wise relevance propagation (LRP) method to select the main parameters for the prediction of the Dst index and understand the physical interpretability of neural networks for the first time. Taking an hourly Dst index and 10 types of solar wind parameters as the inputs, we utilize a long short-term memory network to predict the Dst index and present the LRP method to analyze the dependence of the Dst index on these parameters. LRP defines the relevance score for each input, and a higher relevance score indicates that the corresponding input parameter contributes more to the output. The results show that Dst, E y , B z , and V are the main control parameters for Dst index prediction. In order to verify the LRP method, we design two more supplementary experiments for further confirmation. These results confirm that the LRP method can reduce the initial dimension of neural network input at the cost of minimum information loss and contribute to the understanding of physical processes in space weather.

On the Challenges of Measuring Energetic Particles in the Inner Belt: A Geant4 Simulation of an Energetic Particle Detector Instrument, REPTile‐2

AbstractFollowing the retirement of the community supported Van Allen Probes mission, the quest for high‐quality energetic particle measurements in the radiation belts is likely to be taken on by smaller spacecraft like CubeSats in the foreseeable future. Here we introduce the Relativistic Electron Proton Telescope integrated little experiment‐2 (REPTile‐2), a miniaturized (∼1.5 U) solid‐state charged particle telescope that aims to undertake this challenging task. It incorporates detailed pulse‐height analysis to enable 60 electron channels and 60 proton channels and includes anticoincidence detectors to minimize unwanted background contamination. This paper presents a description of the REPTile‐2 design and emphasizes the importance of extensive Geant4‐based analysis to inform the design of a new energetic particle detector and characterize the instrument response. Our analysis shows that REPTile‐2 can measure ∼0.3–∼4 MeV electrons and ∼6.7–35 MeV protons with energy resolution (∆E/E) of 7%–38% for electrons and 1.5%–5% for protons. Results from a Sr‐90/Y‐90 radioactive source test have verified the instrument performance and the validity of the Geant4 simulations. These energetic particle measurements will enable a new scientific understanding of the inner radiation belt, where unwanted contamination from the unforgiving penetration of highly energetic protons (tens of MeV to GeV) is common, and provide detailed quantification of the inner belt electrons and protons in the low‐Earth orbit that is crucial for space weather modeling.