Correlation analysis between the occurrence of ionospheric scintillation at the magnetic equator and at the southern peak of the Equatorial Ionization Anomaly

Abstract

Ionospheric scintillation refers to amplitude and phase fluctuations in radio signals due to electron density irregularities associated to structures named ionospheric plasma bubbles. The phenomenon is more pronounced around the magnetic equator where, after sunset, plasma bubbles of varying sizes and density depletions are generated by plasma instability mechanisms. The bubble depletions are aligned along Earth's magnetic field lines, and they develop vertically upward over the magnetic equator so that their extremities extend in latitude to north and south of the dip equator. Over Brazil, developing bubbles can extend to the southern peak of the Equatorial Ionization Anomaly, where high levels of ionospheric scintillation are common. Scintillation may seriously affect satellite navigation systems, such as the Global Navigation Satellite Systems. However, its effects may be mitigated by using a predictive model derived from a collection of extended databases on scintillation and its associated variables. This work proposes the use of a classification and regression decision tree to perform a study on the correlation between the occurrence of scintillation at the magnetic equator and that at the southern peak of the equatorial anomaly. Due to limited size of the original database, a novel resampling heuristic was applied to generate new training instances from the original ones in order to improve the accuracy of the decision tree. The correlation analysis presented in this work may serve as a starting point for the eventual development of a predictive model suitable for operational use.

Prediction of the level of ionospheric scintillation at equatorial latitudes in Brazil using a neural network

“…This imbalance of the number of tuples of each class could deteriorate the performance of the neural network since the class with the largest number of training tuples would introduce a bias in the training of the network. This problem was circumvented by applying a resampling procedure [ Lima et al ., ] to the tuples of the class S in the training set in order to match the number of tuples of class NS in the training set. The resampling procedure can be briefly described in three steps: All tuples of class S (including the original and resampled tuples) have each value of their predictive attributes discretized in 16 subbands (for each attribute) using an iterative averaging scheme to define the upper and lower bounds of each subband [ Lima and Stephany , ].…”

“…It is planned to achieve this goal by dividing the modeling task in steps of increasing complexity, as outlined below: Step (A) Correlation study: the first step consisted in analyzing the correlation between the observed attributes related to the IS occurrence earlier at the ME and its later occurrence at the EIA. This correlation analysis was done in Lima et al [] and de Paula et al [], which allowed the choice of a set of predictive attributes at the ME for the IS occurrence at the EIA. Step (B) Prediction of the IS level at the ME: this step was developed in the current work and consists in developing a model to predict the level of IS at the ME, a few hours before it occurs, using relevant data over the same station. However, only such level is predicted, not the starting and ending times.…”

Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian semiarid region, for use in Dynamic Global Vegetation Models

“…The calculation of the maximum rate of electron transport (J max ) was carried out by solving the equation that describes the points of A j . The stomatal limitation was quantified by the method Farquhar and Sharkey (1982), described in details by Long and Bernacchi (2003).…”

“…This method has other advantages such as the easiness to interpret the results; low sensitivity to outliers, ability to handle high dimensionality data (i.e. data with many attributes), and also to identify the more relevant parameters; requires little data preparation, whereas other techniques often need the normalization of data; as well as fast processing time and high accuracy (Sutton, 2005;Lima et al, 2014).…”

Investigation onFlayer height rise and equatorial spreadFonset time: Signature of standing large-scale wave

“…ESF not only remains to be a matter of scientific quest but also poses as a technological challenge in the field of satellite‐based navigation and communication [ Shume et al ., ; Bagiya et al ., ; Oladipo et al ., ]. Several investigations in the recent times have highlighted the day‐to‐day variability in the ionospheric irregularities [e.g., Whalen , ] and its impact on the satellite‐based navigation through L band scintillation [e.g., de Lima et al ., ; Zhang et al ., ; Sreeja et al ., , ; Aquino and Sreeja , ]. Recently, Kelly et al .…”