We analyzed the contribution of electromagnetic ion cyclotron (EMIC) wave driven electron loss to a flux dropout event in September 2017. The evolution of electron phase space density (PSD) through the dropout showed the formation of a radially peaked PSD profile as electrons were lost at high L*, resembling distributions created by magnetopause shadowing. By comparing 2D Fokker Planck simulations of pitch angle diffusion to the observed change in PSD, we found that the μ and K of electron loss aligned with maximum scattering rates at dropout onset. We conclude that, during this dropout event, EMIC waves produced substantial electron loss. Because pitch angle diffusion occurred on closed drift paths near the last closed drift shell, no radial PSD minimum was observed. Therefore, the radial PSD gradients resembled solely magnetopause shadowing loss, even though the local pitch angle scattering produced electron losses of several orders of magnitude of the PSD.

The ring current is an important component of the Earth’s near-space environment, as its variations are the direct driver of geomagnetic storms that can disrupt power grids, satellite communications, and navigation systems, thereby impacting a wide range of technological and human activities. Oxygen ions (O+) are one of the major components of the ring current and play a significant role in both the enhancement and depletion of the ring current during geomagnetic storms. Although a standard statistical study can provide average global distributions of ring current ions, it can’t offer insight into the short-term dynamic variations of the global distribution. Therefore, we employed the Artificial Neural Network (ANN) technique to construct a global ring current O+ ion model based on the Van Allen Probes observations. Through optimization of the combination of input geomagnetic indices and their respective time history lengths, the model can well reproduce the spatiotemporal variation of the oxygen ion flux distributions and demonstrates remarkable accuracy and minimal errors. Additionally, the model effectively reconstructs the temporal variation of ring current O+ ions for an out-of-sample dataset. Furthermore, the model provides a comprehensive and dynamic representation of global ring current O+ ion distribution. It accurately captures the dynamics of O+ ions during a geomagnetic storm with the oxygen ion fluxes enhancement and decay, and reveals distinct characteristics for different energy levels, such as injection from the plasma sheet, outflow from the ionosphere, and magnetic local time asymmetry.

We present an artificial neural network (ANN) model that reconstructs > 30 keV electron flux measurements near the geomagnetic equator from low-Earth-orbit (LEO) observations, exploiting the global coherent nature of the high-energy trapped electrons that constitute the radiation belts. To provide training data, we analyze magnetic conjunctions between one of National Oceanic and Atmospheric Administration’s (NOAA’s) Polar Orbiting Environmental Satellites (POES) and National Aeronautics and Space Administration’s (NASA’s) Van Allen Probes. These conjunctions occur when the satellites are connected along the same magnetic field line and allow for a direct comparison of satellites’ electron flux measurements for one integral energy channel, > 30 keV and over 64,000 such conjunctions have been identified. For each conjunction, we fit the equatorial pitch angle distribution (PAD) parameterized by the function JD = C·sinNα. The resulting conjunction dataset contains the POES electron flux measurements, L and MLT coordinates, geomagnetic activity AE index, and C and N coefficients from the PAD fit for each conjunction. We test combinations of input variables from the conjunction dataset and achieve the best model performance when we use all the input variables during training. We present our model’s prediction for the out-of-sample data that agrees well with observations, R2 > 0.80. We demonstrate the ability to nowcast and reconstruct equatorial electron flux measurements from LEO without the need for an in-situ equatorial satellite. The model can be expanded to include existing LEO data and has the potential to be used as a basis of future radiation-belt monitoring LEO constellations.

We performed a statistical study of electromagnetic ion cyclotron (EMIC) wave distributions and their coupling with energetic protons in the inner magnetosphere using the Arase satellite data from May 2017 to December 2020. We investigated the energetic proton pitch-angle distributions and partial thermal pressures associated with EMIC waves using inter-calibrated proton data in the energy range of 30 eV/q-187 keV/q. With a cold plasma approximation, we computed the proton minimum resonance energies using the observed EMIC wave frequency and plasma density values. We found that the EMIC waves had left-handed polarization near the magnetic equator close to the threshold of proton cyclotron instability, and propagated to higher latitudes along the field line with polarization reversal. H-EMIC waves showed two peak occurrence regions in the morning and noon sectors at L=7.5-9 outside the plasmasphere. The flux enhancements associated with morning side H-EMIC waves appeared at E<1 keV/q among all pitch angles, while H-EMIC waves in the noon sector exhibited flux enhancement in field-aligned directions at E=1-100 keV/q. He-EMIC waves showed a broad occurrence region from 12 to 20 magnetic local time at L=5.5-8.5 inside the plasmasphere with strong flux enhancements at all pitch-angle ranges at E=1-100 keV/q. The proton minimum resonance energy using the obtained central frequency was consistent with the observed flux enhancements at different peak occurrence regions. We conclude that the free energy sources of EMIC waves in different geomagnetic environments drive the two different types of EMIC waves, and they interact with energetic protons at different energy ranges.

Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power-grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long- and short-period characteristics?” is addressed. The study focuses on the long-term variability of measured GIC, and establishes the extent to which the variability relates to quiet-time geomagnetic activity. GIC quiet-day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of Thermosphere-Ionosphere- Electrodynamics General Circulation Model (TIE-GCM)-simulated neutral wind and height-integrated current density. The results show strong evidence that the middle-latitude nodes routinely respond to the tidal-driven Sq variation, with a local time and seasonal dependence on the the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC-based significance score computed in this study provides power utilities with a node-specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.

We use the framework of Physics-Informed Neural Network (PINN) to solve the inverse problem associated to the Fokker-Planck equation for radiation belts’ electron transport, using four years of Van Allen Probes data. Traditionally, reduced models have employed a diffusion equation based on the quasilinear approximation. We show that the dynamics of “killer electrons’ is described more accurately by a drift-diffusion equation, and that drift is as important as diffusion for nearly-equatorially trapped $\sim$1 MeV electrons in the inner part of the belt. Moreover, we present a recipe for gleaning physical insight from solving the ill-posed inverse problem of inferring model coefficients from data using PINNs. Furthermore, we derive a parameterization for the diffusion and drift coefficients as a function of $L$ only, which is both simpler and more accurate than earlier models. Finally, we use the PINN technique to develop an automatic event identification method that allows to identify times at which the radial transport assumption is inadequate to describe all the physics of interest.

We compute quasilinear diffusion rates due to pitch-angle scattering by various mechanisms in the Earth’s electron radiation belts. The calculated theoretical lifetimes are compared with observed decay rates and we find excellent qualitative agreement between the two. The overall structure of the observed lifetime profiles as a function of energy and is largely due to plasmaspheric hiss and Coulomb scattering. The results also reveal a local minimum in lifetimes in the inner zone at lower energy (~50 keV), attributed to enhanced scattering via ground-based VLF transmitters, and a reduction in lifetimes at higher and energy (>1 MeV), attributed to enhanced EMIC wave scattering. In addition, we find significant quantitative disagreement at <3.5, where the theoretical lifetimes are typically a factor of ~10 larger than the observed, pointing to an additional loss process that is missing from current models. We discuss potential factors that could contribute to this disagreement.

The growing scale of Earth and space science challenges dictate new modes of discovery–discovery that embraces cross-disciplinary interactions and links between communities, between data, between technologies. Nowhere is the challenge more pressing than in the field of Heliophysics where solar energy is generated, propagated through interplanetary space, interacts with the Earth’s space environment, and poses immediate threat to our technological infrastructure and human-natural systems (i.e., space weather). We will present a new project within the National Science Foundation Convergence Accelerator program that represents this new mode of discovery “The Convergence Hub for the Exploration of Space Science (CHESS).” Our approach is to semantically link Heliophysics data through a Knowedge Graph/Network (KG). The presentation and discussion will focus on: - What is a knowledge graph (KG)? - In what ways are KGs poised to transform Earth and space science? - The Convergence Hub for the Exploration of Space Science (CHESS) project and bridging to metadata and knowledge architecture efforts in Heliophysics We will highlight linkages to the NSF EarthCube program and ongoing efforts in the geoinformatics and data science communities across e.g., NSF, NOAA, and NASA.

The growing depth and breadth of available data that span the solar-terrestrial environment place us at a tipping point – the potential of these data is immense but realizing that potential requires a new representation. A new network-based approach to represent data collected by power utilities along with information from the solar-terrestrial connection is used. The progress is generated as part of a new project within the National Science Foundation Convergence Accelerator program: “The Convergence Hub for the Exploration of Space Science (CHESS).” Results are shared from current data provided through the Electric Power Research Institute (EPRI) SUNBURST project linked to magnetometer data from the Super Magnetometer Initiative. These data are transformed into a network with GIC measurements and magnetometers as the nodes in order to answer a long-standing question: “How much more likely are deviations from the average current when there is active space weather”? To answer this question, periods of active space weather are identified in the magnetometer data, and these are compared to times of DC transients in the GIC data. The probability of a these transients is found to be , on average, 1.6 times higher during periods of active space weather than during quiet times. The most indicative magnetometers of these DC transients are often not the closest to where the GICs are measured.

We present a comprehensive analysis of the processes that lead to quasilinear pitch-angle-scattering loss of electrons from the L < 4 region of the Earth’s inner magnetosphere during geomagnetically quiet times. We consider scattering via Coulomb collisions, hiss waves, lightning-generated whistler (LGW) waves, waves from ground-based very-low frequency (VLF) transmitters, and electromagnetic ion cyclotron (EMIC) waves. The amplitude, frequency, and wave normal angle spectra of these waves are parameterized with empirical wave models, which are then used to compute pitch-angle diffusion coefficients. From these coefficients, we estimate the decay timescales, or lifetimes, of 30 keV - 4 MeV electrons and compare the results with timescales obtained from in-situ observations. We demonstrate good quantitative agreement between the two over most of the L and energy range under investigation. Our analysis suggests that the electron decay timescales are very sensitive to the choice of plasmaspheric density model. At L < 2, where our theoretical lifetimes do not agree well with the observations, we show that including Coulomb energy drag (ionization energy loss) in our calculations significantly improves the quantitative agreement with the observed decay timescales. We also use an accurate model of the geomagnetic field to provide an estimate of the effect that the drift-loss cone has on the theoretically-calculated electron lifetimes, which are usually obtained using an axisymmetric dipole field.

We performed a comprehensive statistical study of electromagnetic ion cyclotron (EMIC) waves observed by the Van Allen Probes and Exploration of energization and Radiation in Geospace satellite (ERG/Arase). From 2017 to 2018, we identified and categorized EMIC wave events with respect to wavebands (H+ and He+ EMIC waves) and relative locations from the plasmasphere (inside and outside the plasmasphere). We found that H-band EMIC waves in the morning sector at L>8 are predominantly observed with a mixture of linear and right-handed polarity and higher wave normal angles during quiet geomagnetic conditions. Both H+ and He+ EMIC waves observed in the noon sector at L~4-6 have left-handed polarity and lower wave normal angles at |MLAT|< 20˚ during the recovery phase of a storm with moderate solar wind pressure. In the afternoon sector (12-18 MLT), He-band EMIC waves are dominantly observed with strongly enhanced wave power at L~6-8 during the storm main phase, while in the dusk sector (17-21 MLT) they have lower wave normal angles with linear polarity at L>8 during geomagnetic quiet conditions. Based on distinct characteristics at different EMIC wave occurrence regions, we suggest that EMIC waves in the magnetosphere can be generated by different free energy sources. Possible sources include the freshly injected particles from the plasma sheet, adiabatic heating by dayside magnetospheric compressions, suprathermal proton heating by magnetosonic waves, and off-equatorial sources.

Solar-Induced Chlorophyll Fluorescence (SIF) is a powerful proxy for gross primary productivity (GPP) in Boreal ecosystems. However, SIF and GPP are fundamentally different quantities that describe distinct, but related, physiological processes. Recent work has highlighted non-linearities between SIF and GPP at finer spatial (leaf- to canopy- level) and temporal (half-hourly) scales. Therefore, questions have arisen about when, where, and why SIF is a good proxy for GPP and what the potential sources for divergence between the two are. The goal of this study is to answer two specific questions: 1) At what temporal scale is SIF a good proxy for GPP and 2) What are the predominant physical and ecophysiological drivers of nonlinearity between SIF and GPP in boreal ecosystems? We collected tower-based measurements of SIF (and other common vegetation indices) with PhotoSpec (a custom spectrometer system) and eddy-covariance GPP data at a 30-minute resolution at the Southern Old Black Spruce Site (SOBS) in Saskatchewan, CA. We applied a combination of statistical and machine learning approaches to disentangle the influence of structural/illumination effects and ecophysiological variations on the SIF signal. Our results show that at a high temporal resolution (half-hourly), SIF and GPP are predominantly dependent on photosynthetically active radiation (PAR). Therefore, the non-linear light response of GPP drives non-linearity between SIF and GPP. Additionally, canopy structure and illumination effects become important to the SIF signal at high temporal resolutions. At the seasonal timescale, SIF and GPP exhibit co-varying responses to PAR, even when accounting for changes in canopy structure. We attribute changes in the light responses of SIF and GPP to sustained photoprotection over winter which co-varies with changes in temperature. Finally, we show that the relationship between SIF and GPP has a seasonal dependence caused by small differences between the light use efficiencies of fluorescence and photosynthesis. Accounting for this seasonally variable relationship will improve the use of SIF as a proxy for GPP.