Scattering of Ultra-relativistic Electrons in the Van Allen Radiation Belts Accounting for Hot Plasma Effects.

Electron flux in the Earth's outer radiation belt is highly variable due to a delicate balance between competing acceleration and loss processes. It has been long recognized that Electromagnetic Ion Cyclotron (EMIC) waves may play a crucial role in the loss of radiation belt electrons. Previous theoretical studies proposed that EMIC waves may account for the loss of the relativistic electron population. However, recent observations showed that while EMIC waves are responsible for the significant loss of ultra-relativistic electrons, the relativistic electron population is almost unaffected. In this study, we provide a theoretical explanation for this discrepancy between previous theoretical studies and recent observations. We demonstrate that EMIC waves mainly contribute to the loss of ultra-relativistic electrons. This study significantly improves the current understanding of the electron dynamics in the Earth's radiation belt and also can help us understand the radiation environments of the exoplanets and outer planets

Wave-induced loss of ultra-relativistic electrons in the Van Allen radiation belts.

The dipole configuration of the Earth's magnetic field allows for the trapping of highly energetic particles, which form the radiation belts. Although significant advances have been made in understanding the acceleration mechanisms in the radiation belts, the loss processes remain poorly understood. Unique observations on 17 January 2013 provide detailed information throughout the belts on the energy spectrum and pitch angle (angle between the velocity of a particle and the magnetic field) distribution of electrons up to ultra-relativistic energies. Here we show that although relativistic electrons are enhanced, ultra-relativistic electrons become depleted and distributions of particles show very clear telltale signatures of electromagnetic ion cyclotron wave-induced loss. Comparisons between observations and modelling of the evolution of the electron flux and pitch angle show that electromagnetic ion cyclotron waves provide the dominant loss mechanism at ultra-relativistic energies and produce a profound dropout of the ultra-relativistic radiation belt fluxes

Discussions on stakeholder requirements for space weather related models

Participants of the 2017 European Space Weather Week in Ostend, Belgium, discussed the stakeholder requirements for space weather related models. It was emphasized that stakeholders show an increased interest in space weather related models. Participants of the meeting discussed particular prediction indicators that can provide first order estimates of the impact of space weather on engineering systems

Model Evaluation Guidelines for Geomagnetic Index Predictions

Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.Plain Language SummaryOne aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment.Key PointsWe review existing practices for assessing geomagnetic index prediction models and recommend a “standard set” of metricsAlong with fit performance metrics that use all data‐model pairs in their formulas, event detection performance metrics are recommendedOther aspects of metrics assessment best practices, limitations, uncertainties, and geomagnetic index caveats are also discussedPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/1/swe20790_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/2/swe20790.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/3/swe20790-sup-0001-2018SW002067-SI.pd

Neural Decision Tree: A New Tool for Building Forecast Models for Plasmasphere Dynamics

Abstract The Neural Decision Tree (NDT) is a hybrid supervised machine‐learning algorithm that combines the self‐limiting property of a decision tree (Classification and Regression Tree [CART]) algorithm with the artificial neural network. We demonstrate the use of NDT for a regression problem of building a prediction model for the plasmasphere electron density with solar and geomagnetic measurements as inputs. Our work replicates the work by Zhelavskaya et al. reported in their 2017 article (I. S. Zhelavskaya et al., 2017, https://doi.org/10.1002/2017JA024406) to show that NDT makes available sophisticated network layout for building a predictive model, thus taking advantage of deep‐learning potential of the neural network. We also demonstrate that with the ability to automatically select an appropriate network layout, as well as, effective initialization, the NDT algorithm allows research scientists in space weather to focus more of their attention on physically and statistically relevant aspects of using machine‐learning techniques. In fact, our example highlights the fact that the basic assumptions of standard supervise machine‐learning problems are often unsatisfied in real‐world space weather applications. Greater attention to these fundamental issues may create significantly different solutions to space weather forecast problems

The Effect of Plasma Boundaries on the Dynamic Evolution of Relativistic Radiation Belt Electrons

Understanding the dynamic evolution of relativistic electrons in the Earth's radiation belts during both storm and nonstorm times is a challenging task. The U.S. National Science Foundation's Geospace Environment Modeling (GEM) focus group “Quantitative Assessment of Radiation Belt Modeling” has selected two storm time and two nonstorm time events that occurred during the second year of the Van Allen Probes mission for in-depth study. Here, we perform simulations for these GEM challenge events using the 3D Versatile Electron Radiation Belt code. We set up the outer L* boundary using data from the Geostationary Operational Environmental Satellites and validate the simulation results against satellite observations from both the Geostationary Operational Environmental Satellites and Van Allen Probe missions for 0.9-MeV electrons. Our results show that the position of the plasmapause plays a significant role in the dynamic evolution of relativistic electrons. The magnetopause shadowing effect is included by using last closed drift shell, and it is shown to significantly contribute to the dropouts of relativistic electrons at high L*. We perform simulations using four different empirical radial diffusion coefficient models for the GEM challenge events, and the results show that these simulations reproduce the general dynamic evolution of relativistic radiation belt electrons. However, in the events shown here, simulations using the radial diffusion coefficients from Brautigam and Albert (2000) produce the best agreement with satellite observations

Nowcasting and Predicting the Kp Index Using Historical Values and Real-Time Observations

Current algorithms for the real-time prediction of the Kp index use a combination of models empirically driven by solar wind measurements at the L1 Lagrange point and historical values of the index. In this study, we explore the limitations of this approach, examining the forecast for short and long lead times using measurements at L1 and Kp time series as input to artificial neural networks. We explore the relative efficiency of the solar wind-based predictions, predictions based on recurrence, and predictions based on persistence. Our modeling results show that for short-term forecasts of approximately half a day, the addition of the historical values of Kp to the measured solar wind values provides a barely noticeable improvement. For a longer-term forecast of more than 2 days, predictions can be made using recurrence only, while solar wind measurements provide very little improvement for a forecast with long horizon times. We also examine predictions for disturbed and quiet geomagnetic activity conditions. Our results show that the paucity of historical measurements of the solar wind for high Kp results in a lower accuracy of predictions during disturbed conditions. Rebalancing of input data can help tailor the predictions for more disturbed conditions

Intercalibration of the Plasma Density Measurements in Earth's Topside Ionosphere

Over the last 20 years, a large number of instruments have provided plasma density measurements in Earth's topside ionosphere. To utilize all of the collected observations for empirical modeling, it is necessary to ensure that they do not exhibit systematic differences and are adjusted to the same reference frame. In this study, we compare satellite plasma density observations from Gravity Recovery and Climate Experiment (GRACE), Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), CHAllenging Minisatellite Payload (CHAMP), Swarm, and Communications/Navigation Outage Forecasting System (C/NOFS) missions. Electron densities retrieved from GRACE K‐Band Ranging (KBR) system, previously shown to be in excellent agreement with incoherent scatter radar (ISR) measurements, are used as a reference. We find that COSMIC radio occultation (RO) densities are highly consistent with GRACE‐KBR observations showing a mean relative difference of <2%, and therefore no calibration factors between them are necessary. We utilize the outstanding three‐dimensional coverage of the topside ionosphere by the COSMIC mission to perform conjunction analysis with in situ density observations from CHAMP, C/NOFS, and Swarm missions. CHAMP measurements are lower than COSMIC by ∼11%. Swarm densities are generally lower at daytime and higher at nighttime compared to COSMIC. C/NOFS ion densities agree well with COSMIC, with a relative bias of ∼7%. The resulting cross‐calibration factors, derived from the probability distribution functions, help to eliminate the systematic leveling differences between the data sets, and allow using these data jointly in a large number of ionospheric applications.Key Points: A systematic comparison of the plasma density data from CHAMP, C/NOFS, GRACE, COSMIC, and Swarm missions is performed. Electron densities retrieved from COSMIC‐RO agree well with GRACE‐KBR observations showing a relative difference of less than 2%. Intercalibration factors, allowing to eliminate the systematic offsets between the considered data sets, are presented.Helmholtz Pilot Projects Information & Data Science II, MAchine learning based Plasma density model projectNational Center for Atmospheric Research http://dx.doi.org/10.13039/100005323Air Force Office of Scientific Research http://dx.doi.org/10.13039/100000181National Science Foundation http://dx.doi.org/10.13039/10000000

Intercalibration of the Plasma Density Measurements in Earth's Topside Ionosphere

Over the last 20 years, a large number of instruments have provided plasma density measurements in Earth's topside ionosphere. To utilize all of the collected observations for empirical modeling, it is necessary to ensure that they do not exhibit systematic differences and are adjusted to the same reference frame. In this study, we compare satellite plasma density observations from Gravity Recovery and Climate Experiment (GRACE), Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC), CHAllenging Minisatellite Payload (CHAMP), Swarm, and Communications/Navigation Outage Forecasting System (C/NOFS) missions. Electron densities retrieved from GRACE K-Band Ranging (KBR) system, previously shown to be in excellent agreement with incoherent scatter radar (ISR) measurements, are used as a reference. We find that COSMIC radio occultation (RO) densities are highly consistent with GRACE-KBR observations showing a mean relative difference of < E 2%, and therefore no calibration factors between them are necessary. We utilize the outstanding three-dimensional coverage of the topside ionosphere by the COSMIC mission to perform conjunction analysis with in situ density observations from CHAMP, C/NOFS, and Swarm missions. CHAMP measurements are lower than COSMIC by E ∼11%. Swarm densities are generally lower at daytime and higher at nighttime compared to COSMIC. C/NOFS ion densities agree well with COSMIC, with a relative bias of E ∼7%. The resulting cross-calibration factors, derived from the probability distribution functions, help to eliminate the systematic leveling differences between the data sets, and allow using these data jointly in a large number of ionospheric applications