TY  - JOUR
A1  - Soares, Gabriel
A1  - Yamazaki, Yosuke
A1  - Matzka, Jürgen
A1  - Pinheiro, Katia
A1  - Stolle, Claudia
A1  - Alken, Patrick
A1  - Yoshikawa, Akimasa
A1  - Uozumi, Teiji
A1  - Fujimoto, Akiko
A1  - Kulkarni, Atul
T1  - Longitudinal variability of the equatorial counter electrojet during the solar cycle 24
JF  - Studia geophysica et geodaetica
N2  - Ground and space-based geomagnetic data were used in the investigation of the longitudinal, seasonal and lunar phase dependence of the equatorial counter electrojet (CEJ) occurrence in the Peruvian, Brazilian, African, Indian and Philippine sectors during geomagnetically quiet days from the solar cycle 24 (2008 to 2018). We found that CEJ events occur more frequently during the morning (MCEJ) than in the afternoon (ACEJ). The highest MCEJ and ACEJ occurrence rates were observed for the Brazilian sector. Distinct seasonal dependence was found for each longitudinal sector under investigation. The lunar phase dependence was determined for the first time for the Philippine sector (longitude 125 degrees E), and it was shown to be less pronounced than in the Peruvian, Brazilian and African sectors. We demonstrate that differences in CEJ rates derived from ground-based and satellite data can arise from the longitudinal separation between low-latitude and equatorial stations that are used to determine the signal and its consequent time delay in their sunrise/sunset times at ionospheric heights.
KW  - geomagnetism
KW  - equatorial ionosphere
KW  - geomagnetic observatories
KW  - satellite data
Y1  - 2019
U6  - https://doi.org/10.1007/s11200-018-0286-0
SN  - 0039-3169
SN  - 1573-1626
VL  - 63
IS  - 2
SP  - 304
EP  - 319
PB  - Springer
CY  - New York
ER  - 
TY  - JOUR
A1  - Zhelayskaya, Irina S.
A1  - Vasile, Ruggero
A1  - Shprits, Yuri
A1  - Stolle, Claudia
A1  - Matzka, Jürgen
T1  - Systematic Analysis of Machine Learning and Feature Selection Techniques for Prediction of the Kp Index
JF  - Space Weather: The International Journal of Research and Applications
N2  - The Kp index is a measure of the midlatitude global geomagnetic activity and represents short-term magnetic variations driven by solar wind plasma and interplanetary magnetic field. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere and the radiation belts. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast Kp, based their inferences on the recent history of Kp and on solar wind measurements at L1. In this study, we systematically test how different machine learning techniques perform on the task of nowcasting and forecasting Kp for prediction horizons of up to 12 hr. Additionally, we investigate different methods of machine learning and information theory for selecting the optimal inputs to a predictive model. We illustrate how these methods can be applied to select the most important inputs to a predictive model of Kp and to significantly reduce input dimensionality. We compare our best performing models based on a reduced set of optimal inputs with the existing models of Kp, using different test intervals, and show how this selection can affect model performance.
KW  - Kp index
KW  - Predictive models
KW  - Feature selection
KW  - Machine learning
KW  - Validation
Y1  - 2019
U6  - https://doi.org/10.1029/2019SW002271
SN  - 1542-7390
VL  - 17
IS  - 10
SP  - 1461
EP  - 1486
PB  - American Geophysical Union
CY  - Washington
ER  -