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Forecasting ground magnetic field perturbations has been a long-standing goal of the space weather community. The availability of ground magnetic field data and its potential to be used in geomagnetically induced current studies, such as risk assessment, have resulted in several forecasting efforts over the past few decades. One particular community effort was the Geospace Environment Modeling (GEM) challenge of ground magnetic field perturbations that evaluated the predictive capacity of several empirical and first principles models at both mid- and high-latitudes in order to choose an operative model. In this work, we use three different deep learning models-a feed-forward neural network, a long short-term memory recurrent network and a convolutional neural network-to forecast the horizontal component of the ground magnetic field rate of change ( dB H / dt ) over 6 different ground magnetometer stations and to compare as directly as possible with the original GEM challenge. We find that, in general, the models are able to perform at similar levels to those obtained in the original challenge, although the performance depends heavily on the particular storm being evaluated. We then discuss the limitations of such a comparison on the basis that the original challenge was not designed with machine learning algorithms in mind. 

During periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents (GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (| B N |) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000–2015 and is fine tuned for each station to maximize the effectiveness in predicting | B N |. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for | B N |. The neural network models show significant increases over the linear regression models for | B N | thresholds. We also calculate the Heidke skill scores for d| B N |/dt by deriving d| B N |/dt from | B N | predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict B N instead of | B N |, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times. 

Abstract. Terrestrial ecliptic dayside observations of the exospheric Lyman-α column intensity between 3–15 Earth radii (RE) by UVIS/HDAC (UVIS – ultraviolet imaging spectrograph; HDAC – hydrogen-deuterium absorptioncell) Lyman-α photometer at CASSINI have been analyzed to derive the neutral exospheric H-density profile at the Earth's ecliptic dayside in this radial range. The data were measured during CASSINI's swing-by maneuver at the Earth on 18 August 1999 and are published by Werner et al. (2004). In this study the dayside HDAC Lyman-α observations published by Werner et al. (2004) are compared to calculated Lyman-α intensities based on the 3D H-density model derived from TWINS (Two Wide-angle Imaging Neutral-atom Spectrometers) Lyman-α observations between 2008–2010 (Zoennchen et al., 2015). It was found that both Lyman-α profiles show a very similar radial dependence in particular between 3–8 RE. Between 3.0–5.5 RE impact distance Lyman-α observations of both TWINS and UVIS/HDAC exist at the ecliptic dayside. In this overlapping region the cross-calibration of the HDAC profile against the calculated TWINS profile was done, assuming that the exosphere there was similar for both due to comparable space weather conditions. As a result of the cross-calibration the conversion factor between counts per second and rayleigh, fc=3.285 counts s−1 R−1, is determined for these HDAC observations. Using this factor the radial H-density profile for the Earth's ecliptic dayside was derived from the UVIS/HDAC observations, which constrained the neutral H density there at 10 RE to a value of 35 cm−3. Furthermore, a faster radial H-density decrease was found at distances above 8 RE (≈r-3) compared to the lower distances of 3–7 RE (≈r-2.37). This increased loss of neutral H above 8 RE might indicate a higher rate of H ionization in the vicinity of the magnetopause at 9–11 RE (near subsolar point) and beyond, because of increasing charge exchange interactions of exospheric H atoms with solar wind ions outside the magnetosphere. 

We investigate the exospheric neutral density near the subsolar magnetopause, assumingXgse = 10 R_Eas its typical location, to support the upcoming Solar wind‐Magnetosphere‐Ionosphere Link Explorer that will visualize the Earth's magnetosheath and cusps in soft X‐rays. Neutral hydrogen density is a key parameter that controls soft X‐ray emission and can be inversely extracted from the soft X‐ray observations. We introduce a unique method to estimate the dayside neutral density by using X‐ray Multi‐Mirror Mission (XMM)‐Newton astrophysics X‐ray observations and Open Geospace Global Circulation Model (OpenGGCM) global magnetosphere‐ionosphere magnetohydrodynamics (MHD) model. On 4 May 2003 and 16 October 2001, the XMM‐Newton line of sight traversed the dayside of the Earth's magnetosheath and observed strong near‐Earth soft X‐ray emission. We simulate these two events using the OpenGGCM model. Although the model tends to produce a spatially thicker magnetosheath than measured, modeled magnetosheath plasma fluxes match well with the in situ observations of Cluster and Geotail. We calculate the neutral densities every thousand seconds by comparing the modeled count rates with the XMM‐Newton rates. The densities are averaged at39.9 ± 8.0 and57.6 ± 8.0 cm⁻³for the 4 May 2003 and 16 October 2001 events, respectively. Since our MHD tends to underestimate neutral densities due to its thicker magnetosheath, the true neutral density is likely to lie within the upper half of these error bars.

 

Entire fields of science, most notably in astrophysics, rely on line‐of‐sight observations. In planetary science and heliophysics, the techniques of soft X‐ray and energetic neutral atom imaging also produce line‐of‐sight measurements. An important question is whether the geometry of the surface, for example, the magnetopause, can be reconstructed using only line‐of‐sight observations from a single spacecraft. Under a broad range of conditions, the peak emission corresponds to the tangent to the boundary surface, such as the planetary surface or magnetopause, the so‐calledlimb brighteningphenomenon. Thus, line‐of‐sight observations frequently provide information concerning the tangent to the surfaces being observed. We present an algorithm to reconstruct the cross section of the magnetopause using line‐of‐sight soft X‐ray observations (and, in principle, energetic neutral atom observations). The algorithm successfully reconstructs the cross section of the magnetopause in the orbit plane. The three‐dimensional magnetopause structure can be recovered from observations by a spacecraft whose orbit precesses around the magnetosphere.