A Comparison of the Location of the Mid‐Latitude Trough and Plasmapause Boundary

Abstract: We have compared the location of the mid‐latitude trough observed in two dimensional vertical total electron content (vTEC) maps with four plasmapause boundary models, Radiation Belt Storm Probes (RBSP) observations, and IMAGE extreme ultraviolet (EUV) observations all mapped to the ionosphere pierce point using the Tsyganenko (1996) magnetic field line model. For this study, we examine four events over North America: one just after the October 13, 2012 storm, one during the April 20, 2002 double storm, another during a large substorm on January 26, 2013, and one quiet event on May 19, 2001. We have found that in general, the equatorward edge of the mid‐latitude trough is within several degrees in geographic latitude of the mapped model plasmapause boundary location, the plasmapause boundary identified with IMAGE EUV, and the location identified by the RBSP spacecraft. When the mid‐latitude trough is mapped to the inner magnetosphere, the observed boundary agrees with the plasmapause boundary models within two Earth Radii at nearly all local times in the nightside and the observed mid‐latitude boundary is within the uncertainty of the observations at most local times in the nightside. Furthermore, during dynamic solar wind conditions of April 20, 2002, the mid‐latitude trough observed in the vTEC maps propagates equatorward as the plasmapause boundary identified with IMAGE EUV moves earthward. Our results indicate that the mid‐latitude trough observed within the vTEC maps represents an additional means of identifying the plasmapause boundary location, which could result in improved plasmapause boundary models.Plain Language Summary: The equatorward edge of the mid‐latitude trough as observed in TEC maps indicates the location of the plasmapause boundary. We compare the location of the mid‐latitude trough for four events over a range of geomagnetic conditions with plasmapause boundary identified by IMAGE extreme ultraviolet and Radiation Belt Storm Probes and the plasmapause location as indicated by four different models. We find a good agreement between some the methods even under dynamics conditions. The mid‐latitude trough can supply the location for the plasmapause boundary during periods when no spacecraft are available to identify the boundary.Key Points: We show for a range of geomagnetic conditions that the locations of the mid‐latitude trough observed in vTEC maps and IMAGE EUV measured plasmapause boundary mapped with a magnetic field line model to the ionosphere generally agree within the uncertainty. We show that common models of the position of the plasmapause boundary mapped with a magnetic field line model to the ionosphere generally agree with the location of the mid‐latitude trough.NASA THEMIS contractNASA HPDE contractNASA HiDEENASAThe Research Network for Geosciences in Berlin and Postda

Evolving ocean monitoring With GNSS-R: promises in surface wind speed and prospects for rain detection

After developing a wind speed retrieval algorithm, derived winds from measurements of UK TechDemoSat-1 (TDS-1), from May 2015 to July 2017, are compared to wind products of Advanced Scatterometer showing a reliable performance, especially during rain events. However, a rain signature in GNSS-R observations, a decrease in the value of the bistatic radar cross section at low winds, is demonstrated, which can potentially enable the technique to detect precipitation over oceans induced by low-to-moderate winds. This phenomenon is investigated and finally characterized as the rain splash effect altering the ocean surface roughness. To improve the quality of derived winds, a machine learning technique is implemented for the wind speed inversion as a geophysical model function. The trained feedforward neural network shows a significant improvement of 17% in the wind speed RMSE compared to the LS approach. In the end, one can conclude that space-borne ocean monitoring is evolving existing products with a potential for novel geophysical applications

A Combined Neural Network‐ and Physics‐Based Approach for Modeling Plasmasphere Dynamics

In recent years, feedforward neural networks (NNs) have been successfully applied to reconstruct global plasmasphere dynamics in the equatorial plane. These neural network‐based models capture the large‐scale dynamics of the plasmasphere, such as plume formation and erosion of the plasmasphere on the nightside. However, their performance depends strongly on the availability of training data. When the data coverage is limited or non‐existent, as occurs during geomagnetic storms, the performance of NNs significantly decreases, as networks inherently cannot learn from the limited number of examples. This limitation can be overcome by employing physics‐based modeling during strong geomagnetic storms. Physics‐based models show a stable performance during periods of disturbed geomagnetic activity if they are correctly initialized and configured. In this study, we illustrate how to combine the neural network‐ and physics‐based models of the plasmasphere in an optimal way by using data assimilation. The proposed approach utilizes advantages of both neural network‐ and physics‐based modeling and produces global plasma density reconstructions for both quiet and disturbed geomagnetic activity, including extreme geomagnetic storms. We validate the models quantitatively by comparing their output to the in‐situ density measurements from RBSP‐A for an 18‐month out‐of‐sample period from June 30, 2016 to January 01, 2018 and computing performance metrics. To validate the global density reconstructions qualitatively, we compare them to the IMAGE EUV images of the He+ particle distribution in the Earth's plasmasphere for a number of events in the past, including the Halloween storm in 2003.Key Points: We develop an approach to combine a neural network with a physics‐based model of the plasmasphere using data assimilation. The approach is extensively validated using in‐situ density measurements and observed plasmapause position derived from the Imager for Magnetopause‐to‐Aurora Global Exploration EUV. The developed model reproduces the plasmasphere dynamics during quiet, moderate, disturbed, and extreme geomagnetic events.Geo.XEU Horizon 2020Deutsche Forschungsgemeinschaft (DFG) http://dx.doi.org/10.13039/501100001659Helmholtz Association (亥姆霍兹联合会致力) http://dx.doi.org/10.13039/50110000931

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

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

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

Medium Energy Electron Flux in Earth's Outer Radiation Belt (MERLIN): A Machine Learning Model

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 programm

Assessing Machine Learning Techniques for Identifying Field Line Resonance Frequencies From Cross‐Phase Spectra

International audienceMonitoring the plasmasphere is an important task to achieve in the Space Weather context. A consolidated technique consists of remotely inferring the equatorial plasma mass density in the inner magnetosphere using Field Line Resonance (FLR) frequencies estimates. FLR frequencies can be obtained via cross-phase analysis of magnetic signals recorded from pairs of latitude separated stations. In the last years, machine learning (ML) has been successfully applied in Space Weather, but this is the first attempt to estimate FLR frequencies with these techniques. We survey several supervised ML algorithms for identifying FLR frequencies by using measurements of the European quasi-Meridional Magnetometer Array. Our algorithms take as input the 2-hour cross-phase spectra of magnetic signals and return the FLR frequency as output; we evaluate the algorithm performance on four different station pairs from L = 2.4 to L = 5.5. Results show that tree-based algorithms are robust and accurate models to achieve this goal. Their performance slightly decreases with increasing latitude and tend to deteriorate during nighttime. The estimation error does not seem to depend on the geomagnetic activity, although at high latitudes the error increases during highly disturbed geomagnetic conditions such as the main phase of a storm. Our approach may represent a prominent space weather tool included into an automatic monitoring system of the plasmasphere. This work represents only a preliminary step in this direction; the application of this technique on a more extensive data set and on more pairs of stations is straightforward and necessary to create more robust and accurate models