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Dynamical variations of radiation belt trapped electron fluxes are examined to better understand the variability of enhancements linked to substorm clusters. Analysis is undertaken using the Substorm Onsets and Phases from Indices of the Electrojet (SOPHIE) substorm cluster algorithm for event detection. Observations from Low Earth Orbit (LEO) are complemented by additional measurements from Medium Earth Orbit (MEO) to allow a major expansion in the energy range considered, from medium energy energetic electrons up to ultra-relativistic electrons. The number of substorms identified inside a cluster does not depend strongly on solar wind drivers or geomagnetic indices either before, during, or after the cluster start time. Clusters of substorms linked to moderate (100nT

Manuscripts based on machine-learning techniques have significantly increased in Space Weather over the past few years. We discuss which manuscripts are within the journal's scope and emphasize tha...

In this paper, we study electron radial diffusion coefficients derived from Pc4‐Pc5 ultralow frequency (ULF) wave power during the intense geomagnetic storm on 17–18 March 2015. During this storm the population of highly relativistic electrons was depleted within 2 hr of the storm commencement. This radial diffusion, depending upon the availability of source populations, can cause outward radial diffusion of particles and their loss to the magnetosheath, or inward transport and acceleration. Analysis of electromagnetic field measurements from Geostationary Operational Environment Satellite (GOES), Time History of Events and Macroscale Interactions during Substorms (THEMIS) satellite, and ground‐based magnetometers shows that the main phase storm‐specific radial diffusion coefficients do not correspond to statistical estimates. Specifically, during the main phase, the electric diffusion ( urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0001) is reduced, and the magnetic diffusion ( urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0002) is increased, compared to empirical models based on Kp. Contrary to prior results, the main phase magnetic radial diffusion cannot be neglected. The largest discrepancies, and periods of dominance of urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0003 over urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0004, occur during intervals of strongly southward IMF. However, during storm recovery, both magnetic and electric diffusion rates are consistent with empirical estimates. We further verify observationally, for the first time, an energy coherence for both urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0005 and urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0006 where diffusion coefficients do not depend on energy. We show that, at least for this storm, properly characterizing main phase radial diffusion, potentially associated with enhanced ULF wave magnetopause shadowing losses, cannot be done with standard empirical models. Modifications, associated especially with southward IMF, which enhance the effects of urn:x-wiley:jgra:media:jgra54863:jgra54863-math-0007 and introduce larger main phase outward transport losses, are needed.

This paper examines the rapid losses and acceleration of trapped relativistic and ultrarelativistic electron populations in the Van Allen radiation belt during the September 7-9, 2017, geomagnetic ...

For many years, it was believed that resonant interactions between electromagnetic ion cyclotron (EMIC) waves and radiation belt electrons were restricted to electron energies > 1 − 2 MeV. In recent years, however, a growing body of experimental evidence has shown that EMIC waves can cause the scattering loss of electrons down to sub‐MeV energies. Using measurements of trapped electron flux from the GPS satellite constellation, we investigate the ability of EMIC waves to cause significant depletions of radiation belt electron populations between 4 ≤ L* ≤ 5. For the first time, we present statistical evidence demonstrating global decreases in sub‐MeV trapped electron flux in response to EMIC wave activity. Although we find that electron losses extend down to sub‐MeV energies, we also show strong statistical support for the ability of EMIC waves to preferentially cause substantial depletions of ultra‐relativistic electrons in the radiation belts.

Both ground and space observations are used extensively in the modeling of space weather processes within the Earth's magnetosphere. In radiation belt physics modeling, one of the key phase‐space coordinates is L*, which indicates the location of the drift paths of energetic electrons. Global magnetic field models allow a subset of locations on the ground (mainly sub‐auroral) to be mapped along field lines to a location in space and transformed into L*, provided that the initial ground location maps to a closed drift path. This allows observations from ground, or low‐altitude space‐based platforms to be mapped into space in order to inform radiation belt modeling. Many data‐based magnetic field models exist; however these models can significantly disagree on mapped L* values for a single point on the ground, during both quiet times and storms. We present a state of the art probabilistic L* mapping tool, Pro‐L*, which produces probability distributions for L* corresponding to a given ground location. Pro‐L* has been calculated for a high resolution magnetic latitude by magnetic local time (MLT) grid in the Earth's northern hemisphere. We have developed the probabilistic model using 11 years of L* calculations for 7 widely used magnetic field models. Usage of the tool is highlighted for both event studies and statistical models, and we demonstrate a number of potential applications.

Understanding extreme space weather events in terms of the geospace response is a critical step towards protecting vulnerable technological infrastructure. This is particularly relevant for the eff...

We use long-term electron and proton in-situ measurements made by the CXD particle instruments, developed by Los Alamos National Laboratory and carried on board GPS satellites, to determine total ionizing dose (TID) values and daily/yearly dose rate (DR) values in medium Earth orbits (MEOs) caused by the natural space radiation environment. Here measurement-based TID and DR values on a simplified sample geometry--a small (with a radius of 0.1 mm) Silicon detector within an Aluminum shielding sphere with a thickness of 100 mil--are compared to those calculated from empirical radiation models. Results over the solar cycle 24 show that electron TID from measurements in GPS orbit is well above the values calculated from the median/mean fluences from AE8 and AE9 models, but close to model fluences at high percentiles. Also, it is confirmed that in MEOs proton contributions to TID are minor and mainly dominated by solar energetic protons. Several factors affecting those dose calculations are discussed and evaluated. Results from this study provide us another out-of-sample test on the reliability of existing empirical space radiation models, and also help estimate the margin factors on calculated dose values in MEOs that pass through the heart of the Earth's outer radiation belt., 10 pages, 11 figures, submitted to 2021 IEEE Aerospace Conference. It has been accepted for final revision. This is not the published version

Geomagnetic activity is often described using summary indices to summarize the likelihood of space weather impacts, as well as when parameterizing space weather models. The geomagnetic indexK(p)in particular, is widely used for these purposes. Current state-of-the-art forecast models provide deterministicK(p)predictions using a variety of methods - including empirically-derived functions, physics-based models, and neural networks - but do not provide uncertainty estimates associated with the forecast. This paper provides a sample methodology to generate a 3-hour-aheadK(p)prediction with uncertainty bounds and from this provide a probabilistic geomagnetic storm forecast. Specifically, we have used a two-layered architecture to separately predict storm (K-p >= 5(-)) and non-storm cases. As solar wind-driven models are limited in their ability to predict the onset of transient-driven activity we also introduce a model variant using solar X-ray flux to assess whether simple models including proxies for solar activity can improve the predictions of geomagnetic storm activity with lead times longer than the L1-to-Earth propagation time. By comparing the performance of these models we show that including operationally-available information about solar irradiance enhances the ability of predictive models to capture the onset of geomagnetic storms and that this can be achieved while also enabling probabilistic forecasts. Space Science and Applications group; Center for Space and Earth Science (CSES) at Los Alamos National Laboratory; US Department of EnergyUnited States Department of Energy (DOE); Laboratory Directed Research and Development (LDRD) program [20190262ER] SC thanks to the Space Science and Applications group and the Center for Space and Earth Science (CSES) at Los Alamos National Laboratory for organizing and supporting the Los Alamos Space Weather Summer School. Portions of this work by SKM were performed under the auspices of the US Department of Energy and were partially supported by the Laboratory Directed Research and Development (LDRD) program, award number 20190262ER. The authors wish to acknowledge the use of the OMNI solar wind data, available at https://omniweb.gsfc.nasa.gov/ow.html. The Kp index and GOES X-ray datasets were accessed through the GFZ-Potsdam website https://www.gfz-potsdam.de/en/kpindex/ and NOAA FTP server https://satdat.ngdc.noaa.gov/sem/goes/data/, respectively. The majority of analysis and visualization was completed with the help of free, open-source software tools, notably: Keras (Chollet, 2015); matplotlib (Hunter, 2007); IPython (Perez & Granger, 2007); pandas (McKinney, 2010); Spacepy (Morley et al., 2011); PyForecast-Tools (Morley, 2018); and others (see, e.g., Millman & Aivazis, 2011). The code developed during this work is available at https://github.com/shibaji7/Codebase_Kp_Prediction. The editor thanks two anonymous reviewers for their assistance in evaluating this paper.

The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120–600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis., Plain Language Summary: The radiation belts of the Earth, which are the zones of charged energetic particles trapped by the geomagnetic field, comprise complex and dynamic systems posing a significant threat to a variety of commercial and military satellites. While the inner belt is relatively stable, the outer belt is highly variable and depends substantially on solar activity; therefore, accurate and improved models of electron flux in the outer radiation belt are essential to understand the underlying physical processes. Although many models have been developed for the geostationary orbit and relativistic energies, prediction of electron flux in the 120–600 keV energy range still remains challenging. We present a data‐driven model of the medium energies (120–600 keV) differentialelectron flux in the outer radiation belt based on machine learning. We use 17 years of electron observations by Global Positioning System (GPS) satellites. We set up a 3D model for flux prediction in terms of L‐values, MLT, and magnetic latitude. The model gives reliable predictions of the radiation environment in the outer radiation belt and has wide space weather applications., Key Points: A machine learning model is created to predict electron flux at MEO for energies 120–600 keV. The model requires solar wind parameters and geomagnetic indices as input and does not use persistence. MERLIN model yields high accuracy and high correlation with observations (0.8)., Horizon 2020 – The EU Research and Innovation programme

Space plasma instruments often rely on ultrathin carbon foils for incident ion detection, time-of-flight (TOF) mass spectrometry, and ionization of energetic neutral atoms. Angular scattering and energy loss of ions or neutral atoms in the foil can degrade instrument performance, including sensitivity and mass resolution; thus, there is an ongoing effort to manufacture thinner foils. Using new 3-layer graphene foils manufactured at the Los Alamos National Laboratory, we demonstrate that these are the thinnest foils reported to date and discuss future testing required for application in space instrumentation. We characterize the angular scattering distribution for 3-30 keV protons through the foils, which is used as a proxy for the foil thickness. We show that these foils are ∼2.5-4.5 times thinner than the state-of-the-art carbon foils and ∼1.6 times thinner than other graphene foils described in the literature. We find that the inverse relationship between angular scattering and energy no longer holds, reaffirming that this may indicate a new domain of beam-foil interactions for ultrathin (few-layer) graphene foils.

We have developed a new procedure for combining lists of substorm onset times from multiple sources. We apply this procedure to observational data and to magnetohydrodynamic (MHD) model output from...

The Global Positioning System (GPS) is one of the most ubiquitously available services in the world used in military operations all the way down to locating a lost cat. However, GPS provides far more than just location and timing services; many of the satellites carry instruments to directly measure the space environment. On October 13th, 2016, the White House issued Executive Order #13744 ‘Coordinating Efforts to Prepare the Nation for Space Weather Events’ which stated that responsible agencies ‘shall make historical data from the GPS constellation and other U.S. Government satellites publically available.’ Los Alamos National Laboratory (LANL) has been designing, building, and operating energetic particle instruments in space for more than 50 years with two types of detectors on the current GPS constellation. As part of the 2016 Executive Order data collected by LANL sensors from 2001 through 2018 has been released and is archived by the National Oceanic and Atmospheric Administration. With more than 20 satellites operating LANL instruments during this period the dataset consists of more than 200 satellite-years of data that increases by roughly one satellite-year of data every two weeks and covers an entire solar cycle. The GPS constellation is in a medium-Earth orbit distributed across six orbital planes at a nominal altitude of ~20,200 km. Its circular orbit at an inclination of 55 degrees allows GPS to sample a wide range of magnetic latitude and McIlwain L. The data product consists of electron and proton measurements in energy ranges of 77 keV to $> \mathbf{5\ MeV}$ and 10 MeV to $> \mathbf{75\ MeV}$ respectively. These data are provided at a four minute cadence with information including differential omnidirectional fluxes, along with selected ephemeris data, count rates, as well as geomagnetic parameters computed from established magnetic field models. In comparison to the large number of GPS satellites, the average (even billion dollar) space mission will have one or maybe two satellites (with 5 being the outlier for the THEMIS mission). This exceptional data density allows the study of phenomena on rapid timescales and the simultaneous variations across magnetic local time. In depth study of how the magnetosphere reacts to solar storms and the access solar energetic particles have into the near-Earth environment is also possible. This will help in the verification and validation of transport codes as well as understanding what effect these solar events will have not only on our ground infrastructure, but our space assets as well. We will present an overview of the available data as well as some initial studies to exemplify its utility.

In this paper, we discuss the general applicability of the Backwards Liouville Test–Kinetic (BLTK) method to the modeling of time-dependent particle distributions in magnetospheric plasmas. We discuss a new particle tracing model, SHIELDS-PTM, and describe certain novel features of its implementation. Finally, we apply SHIELDS-PTM to highlight a powerful but oft-overlooked feature of the BLTK approach: the invariant trajectories are independent of the quantity being transported, so it is straightforward to optimize source models with undetermined parameters. We demonstrate this by using SHIELDS-PTM to determine the local-time distribution of particle fluxes around geosynchronous orbit using simple models for the electromagnetic fields and statistical models of geosynchronous electron fluxes. We find that optimized source distributions generally provide a far superior result than statistical models of the source region, improving the accuracy of modeled fluxes at some energies by more than an order of magnitude.

Accurate proton flux measurements of the near Earth environment are essential to the understanding of many phenomena which have a direct impact on our lives. Currently there is only a small set of satellites capable of performing these measurements which makes certain studies and analyses difficult. This paper details the capabilities of the Combined X-ray Dosimeter (CXD), flown on 21 satellites of the Global Positioning System (GPS) constellation, as it relates to proton measurements. We present a cross-calibration of the CXD with the Energetic Particle Sensor (EPS) onboard the Geostationary Operational Environmental Satellite (GOES) operated by the National Oceanic and Atmospheric Administration (NOAA). By utilizing Solar Energetic Particle Events (SEPEs) when both sets of satellites were operational we have orders of magnitude in flux and energy to compare against. Robust statistical analyses show that the CXD and GOES flux calculations are similar and that for proton energies > 30 MeV the CXD fluxes are on average within 20% of EPS. Although the CXD has a response to protons as low as 6 MeV the sensitivity at energies below 20 MeV is reduced and so flux comparisons of these are generally worse. Integral flux values > 10 MeV are typically within 40% of EPS. These calibrated CXD data sets will give researchers capabilities to study solar proton access to the inner magnetosphere down to L ~ 4 near the equatorial plane at high temporal cadence.

Quasi-periodic energetic particle injections have been observed at geosynchronous orbit on the dusk-side during a steady magnetospheric convection event. We examine high-resolution auroral imager data and ground magnetometer data associated with the first of these injections and conclude that it was not associated with classical sub-storm signatures. It is proposed that these injections are caused by the explosive nonlinear growth of a shear flow-ballooning instability in the region where sub-auroral polarization streams (SAPS) also occur. It is suggested that interchange will occur preferentially in the low-conductivity SAPS region since the magnetic Richardson number is lowest there and the "line-tying" effect will also be least stabilizing there. We propose that the observed particle injection signatures and auroral morphology constitute a new type of SAPS-associated explosive "onset-like" disturbance that can occur during intervals of strong convection.

Quantitative assessment of modeling and forecasting of continuous quantities uses a variety of approaches. We review existing literature describing metrics for forecast accuracy and bias, concentrating on those based on relative errors and percentage errors. Of these accuracy metrics, the mean absolute percentage error (MAPE) is one of the most common across many fields and has been widely applied in recent space science literature and we highlight the benefits and drawbacks of MAPE and proposed alternatives. We then introduce the log accuracy ratio and derive from it two metrics: the median symmetric accuracy and the symmetric signed percentage bias. Robust methods for estimating the spread of a multiplicative linear model using the log accuracy ratio are also presented. The developed metrics are shown to be easy to interpret, robust, and to mitigate the key drawbacks of their more widely used counterparts based on relative errors and percentage errors. Their use is illustrated with radiation belt electron flux modeling examples.