Artem Smirnov, Max Berrendorf, Yuri Shprits, Elena A. Kronberg, Hayley J. Allison, Nikita Aseev, Irina Zhelavskaya, Steven K. Morley, Geoffrey D. Reeves, Matthew R. Carver, Frederic Effenberger
- 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 theThe 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.…
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MetadatenAuthor details: | Artem SmirnovORCiDGND, Max BerrendorfORCiD, Yuri ShpritsORCiDGND, Elena A. KronbergORCiDGND, Hayley J. AllisonORCiD, Nikita AseevORCiDGND, Irina ZhelavskayaORCiDGND, Steven K. MorleyORCiD, Geoffrey D. ReevesORCiDGND, Matthew R. CarverORCiD, Frederic EffenbergerORCiDGND |
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DOI: | https://doi.org/10.1029/2020SW002532 |
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ISSN: | 1542-7390 |
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Title of parent work (English): | Space weather : the international journal of research and applications |
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Subtitle (English): | a Machine learning model |
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Publisher: | American geophysical union, AGU |
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Place of publishing: | Washington |
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Publication type: | Article |
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Language: | English |
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Date of first publication: | 2020/10/14 |
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Publication year: | 2020 |
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Release date: | 2023/01/25 |
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Tag: | electron flux; electrons; empirical modeling; machine learning; magnetosphere; radiation belts |
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Volume: | 18 |
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Issue: | 11 |
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Article number: | e2020SW002532 |
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Number of pages: | 20 |
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Funding institution: | transnational E-RARE grant `CCMCURE (DFG)European Commission [SFB958]; E-RARE [ERL 138397]; Canadian; Institutes for Health ResearchCanadian Institutes of Health Research; (CIHR) [PJT 153000]; the E-RARE grant `CCMCURE |
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Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Physik und Astronomie |
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DDC classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
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Peer review: | Referiert |
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Publishing method: | Open Access / Hybrid Open-Access |
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| DOAJ gelistet |
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License (German): | CC-BY - Namensnennung 4.0 International |
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