A Machine Learning technique for ULF wave classification in Swarm magnetic field measurements

2021 ◽  
Author(s):  
Alexandra Antonopoulou ◽  
George Balasis ◽  
Constantinos Papadimitriou ◽  
Zoe Boutsi ◽  
Omiros Giannakis ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Machine Learning ◽  
Field Measurements ◽  
Data Availability ◽  
Machine Learning Technique ◽  
Ulf Wave ◽  
The Earth ◽  
Swarm Satellites ◽  
Magnetic Field Measurements ◽  
Learning Technique

<p>Ultra-low frequency (ULF) magnetospheric plasma waves play a key role in the dynamics of the Earth’s magnetosphere and, therefore, their importance in Space Weather studies is indisputable. Magnetic field measurements from recent multi-satellite missions are currently advancing our knowledge on the physics of ULF waves. In particular, Swarm satellites have contributed to the expansion of data availability in the topside ionosphere, stimulating much recent progress in this area. Coupled with the new successful developments in artificial intelligence, we are now able to use more robust approaches for automated ULF wave identification and classification. The goal of this effort is to use a machine learning technique to classify ULF wave events using magnetic field data from Swarm. We construct a Convolutional Neural Network that takes as input the wavelet power spectra of the Earth’s magnetic field variations per track, as measured by each one of the three Swarm satellites, aiming to classify ULF wave events in four categories: Pc3 wave events, background noise, false positives, and plasma instabilities. Our primary experiments show promising results, yielding successful identification of 90% accuracy. We are currently working on producing larger datasets, by analyzing Swarm data from mid-2014 onwards, when the final constellation was formed.</p>

A Deep Learning Technique for Automated Detection of ULF Waves in Swarm Time Series

2020 ◽  
Author(s):  
Alexandra Antonopoulou ◽  
Constantinos Papadimitriou ◽  
Georgios Balasis ◽  
Adamantia Zoe Boutsi ◽  
Konstantinos Koutroumbas ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Deep Learning ◽  
Building Blocks ◽  
Data Availability ◽  
Topside Ionosphere ◽  
Ulf Waves ◽  
Dense Layer ◽  
Ulf Wave ◽  
The Earth ◽  
Swarm Satellites

<p>Ultra-low frequency (ULF) magnetospheric plasma waves play a key role in the dynamics of the Earth’s magnetosphere and, therefore, their importance in Space Weather studies is indisputable. Magnetic field measurements from recent multi-satellite missions (e.g. Cluster, THEMIS, Van Allen Probes and Swarm) are currently advancing our knowledge on the physics of ULF waves. In particular, Swarm satellites, one of the most successful mission for the study of the near-Earth electromagnetic environment, have contributed to the expansion of data availability in the topside ionosphere, stimulating much recent progress in this area. Coupled with the new successful developments in artificial intelligence (AI), we are now able to use more robust approaches devoted to automated ULF wave event identification and classification. The goal of this effort is to use a deep learning method in order to classify ULF wave events using magnetic field data from Swarm. We construct a Convolutional Neural Network (CNN) that takes as input the wavelet spectra of the Earth’s magnetic field variations per track, as measured by each one of the three Swarm satellites, and whose building blocks consist of two convolution layers, two pooling layers and a fully connected (dense) layer, aiming to classify ULF wave events in four different categories: 1) Pc3 wave events (i.e., frequency range 20-100 MHz), 2) non-events, 3) false positives, and 4) plasma instabilities. Our primary experiments show promising results, yielding successful identification of more than 95% accuracy. We are currently working on producing larger training/test datasets, by analyzing Swarm data from the mid-2014 onwards, when the final constellation was formed, aiming to construct a dataset comprising of more than 50000 wavelet image inputs for our network.</p>

The effect of plasmaspheric material on magnetopause reconnection

2020 ◽  
Author(s):  
Stephen Fuselier ◽  
Stein Haaland ◽  
Paul Tenfjord ◽  
David Malaspina ◽  
James Burch ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Low Temperature ◽  
Magnetic Reconnection ◽  
Plasma Wave ◽  
Outer Part ◽  
Field Measurements ◽  
Ion Composition ◽  
The Earth ◽  
Magnetospheric Multiscale ◽  
Magnetic Field Measurements

<p>The Earth’s plasmasphere contains cold (~eV energy) dense (>100 cm<sup>-3</sup>) plasma of ionospheric origin. The primary ion constituents of the plasmasphere are H<sup>+ </sup>and He<sup>+</sup>, and a lower concentration of O<sup>+</sup>. The outer part of the plasmasphere, especially on the duskside of the Earth, drains away into the dayside outer magnetosphere when geomagnetic activity increases. Because of its high density and low temperature, this plasma has the potential to modify magnetic reconnection at the magnetopause. To investigate the effect of plasmaspheric material at the magnetopause, Magnetospheric Multiscale (MMS) data are surveyed to identify magnetopause crossings with the highest He<sup>+</sup>densities. Plasma wave, ion, and ion composition data are used to determine densities and mass densities of this plasmaspheric material and the magnetosheath plasma adjacent to the magnetopause. These measurements are combined with magnetic field measurements to determine how the highest density plasmaspheric material in the MMS era may affect reconnection at the magnetopause.</p>

scholarly journals Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites

2021 ◽  
Vol 13 (12) ◽  
pp. 2360
Author(s):  
Christoph Schirninger ◽  
Hans U. Eichelberger ◽  
Werner Magnes ◽  
Mohammed Y. Boudjada ◽  
Konrad Schwingenschuh ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Natural Hazards ◽  
Field Measurements ◽  
Volcanic Eruptions ◽  
Low Earth Orbit ◽  
Near Surface ◽  
The Earth ◽  
Magnetic Field Measurements ◽  
Spatio Temporal ◽  
Magnetic Field Variations

Processes and threats related to natural hazards play an important role in the evolution of the Earth and in human history. The purpose of this study is to investigate magnetic field variations measured at low Earth orbit (LEO) altitudes possibly associated with earthquakes, volcanic eruptions, and artificial outbursts. We focus on two missions with well equipped magnetometer packages, the China Seismo-Electromagnetic Satellite (CSES) and ESA’s three spacecraft Swarm fleet. After a natural hazards survey in the context of this satellites, and consideration of external magnetospheric and solar influences, together with spacecraft interferences, wavelet analysed spatio-temporal patterns in ionospheric magnetic field variations related to atmospheric waves are examined in detail. We provide assessment of the links between specific lithospheric or near surface sources and ionospheric magnetic field measurements. For some of the diverse events the achieved statistical results show a change in the pattern between pre- and post-event periods, we show there is an increase in the fluctuations for the higher frequency (smaller scales) components. Our results are relevant to studies which establish a link between space based magnetic field measurements and natural hazards.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

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