Calibration of Swarm Plasma Densities Overestimation Using Neural Networks
Abstract Recent studies have shown that the measurements of Langmuir Probes (LPs) onboard ESA's Swarm mission overestimate ion densities on the nightside by up to 50%. The overestimation is due to the assumption of oxygen‐only plasma for ion density calculations, which is often violated at mid‐...
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Wiley
2024-08-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2024SW003925 |
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| author | Artem Smirnov Yuri Shprits Hermann Lühr Alessio Pignalberi Chao Xiong |
| author_facet | Artem Smirnov Yuri Shprits Hermann Lühr Alessio Pignalberi Chao Xiong |
| author_sort | Artem Smirnov |
| collection | DOAJ |
| description | Abstract Recent studies have shown that the measurements of Langmuir Probes (LPs) onboard ESA's Swarm mission overestimate ion densities on the nightside by up to 50%. The overestimation is due to the assumption of oxygen‐only plasma for ion density calculations, which is often violated at mid‐latitudes on the nightside. In this study, we present a calibration model that resolves the nighttime overestimation by Swarm LPs. Using observations by Swarm FacePlate (FP) as a reference, we develop a neural network (NN) model that adjusts LP data to the FP measurements. The model incorporates dependence on solar and geomagnetic conditions, parameterized by the P10.7 and Hp30 indices, location, day of the year and local time. Our model reveals a distinct double‐crest pattern in nighttime density overestimation by LPs, centered at ∼30° quasi‐dipole latitude in both hemispheres. This overestimation intensifies during low solar activity and shows strong seasonal dependence. During solstices, the crests are more pronounced in the local winter hemispheres, while during equinoxes the crests are weaker and exhibit hemispheric symmetry. This morphology aligns with the presence of light ions diffusing downward from the plasmasphere. Validating the LP data in conjunctions with Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) observations showed a much stronger agreement after applying the developed correction: for Swarm B, nighttime correlation with COSMIC increased from 0.74 to 0.93. The NN‐calibrated LP data set has numerous applications in ionospheric research, and the developed model can provide useful insights into the ion composition in the topside ionosphere. |
| format | Article |
| id | doaj-art-29adb849f4814e23ba980f0f3674199b |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-29adb849f4814e23ba980f0f3674199b2025-01-14T16:27:32ZengWileySpace Weather1542-73902024-08-01228n/an/a10.1029/2024SW003925Calibration of Swarm Plasma Densities Overestimation Using Neural NetworksArtem Smirnov0Yuri Shprits1Hermann Lühr2Alessio Pignalberi3Chao Xiong4Helmholtz Centre Potsdam ‐ GFZ German Research Centre for Geosciences Potsdam GermanyHelmholtz Centre Potsdam ‐ GFZ German Research Centre for Geosciences Potsdam GermanyHelmholtz Centre Potsdam ‐ GFZ German Research Centre for Geosciences Potsdam GermanyNational Institute of Geophysics and Volcanology (INGV) Rome ItalyDepartment of Space Physics College of Electronic Information Wuhan University Wuhan ChinaAbstract Recent studies have shown that the measurements of Langmuir Probes (LPs) onboard ESA's Swarm mission overestimate ion densities on the nightside by up to 50%. The overestimation is due to the assumption of oxygen‐only plasma for ion density calculations, which is often violated at mid‐latitudes on the nightside. In this study, we present a calibration model that resolves the nighttime overestimation by Swarm LPs. Using observations by Swarm FacePlate (FP) as a reference, we develop a neural network (NN) model that adjusts LP data to the FP measurements. The model incorporates dependence on solar and geomagnetic conditions, parameterized by the P10.7 and Hp30 indices, location, day of the year and local time. Our model reveals a distinct double‐crest pattern in nighttime density overestimation by LPs, centered at ∼30° quasi‐dipole latitude in both hemispheres. This overestimation intensifies during low solar activity and shows strong seasonal dependence. During solstices, the crests are more pronounced in the local winter hemispheres, while during equinoxes the crests are weaker and exhibit hemispheric symmetry. This morphology aligns with the presence of light ions diffusing downward from the plasmasphere. Validating the LP data in conjunctions with Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) observations showed a much stronger agreement after applying the developed correction: for Swarm B, nighttime correlation with COSMIC increased from 0.74 to 0.93. The NN‐calibrated LP data set has numerous applications in ionospheric research, and the developed model can provide useful insights into the ion composition in the topside ionosphere.https://doi.org/10.1029/2024SW003925topside ionosphereneural networkscalibrationswarmLangmuir probesplasma density |
| spellingShingle | Artem Smirnov Yuri Shprits Hermann Lühr Alessio Pignalberi Chao Xiong Calibration of Swarm Plasma Densities Overestimation Using Neural Networks Space Weather topside ionosphere neural networks calibration swarm Langmuir probes plasma density |
| title | Calibration of Swarm Plasma Densities Overestimation Using Neural Networks |
| title_full | Calibration of Swarm Plasma Densities Overestimation Using Neural Networks |
| title_fullStr | Calibration of Swarm Plasma Densities Overestimation Using Neural Networks |
| title_full_unstemmed | Calibration of Swarm Plasma Densities Overestimation Using Neural Networks |
| title_short | Calibration of Swarm Plasma Densities Overestimation Using Neural Networks |
| title_sort | calibration of swarm plasma densities overestimation using neural networks |
| topic | topside ionosphere neural networks calibration swarm Langmuir probes plasma density |
| url | https://doi.org/10.1029/2024SW003925 |
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