Ultralow frequency (ULF: 0.001–5 Hz) magnetic records have recently been used in the search for short‐term earthquake prediction methods. The separation of local and global effects in the magnetic records is the greatest challenge in this research area. Geomagnetic indices are often used to predict global ULF magnetic behavior where it is assumed that increases in a geomagnetic index correspond with an increase in ULF power. This paper examines the relationships between geomagnetic indices and ULF power, spectral polarization ratio, and the relationship between the spectral polarization ratio and solar wind parameters. The power in the ULF, Pc3‐5 bands (10–600 s), shows a linear correlation coefficient of 0.2 with the Kp magnetic activity index. The correlation varies with magnetic local time (MLT) and latitude. The correlation coefficient is inversely related to the integrated power in the ULF Pc3 band (10–45 s) over MLT and magnetic latitude. The ratio of spectral powers Z(ω)/G(ω) is discussed and shown to be a promising parameter in the search for earthquake precursor signals in ULF records.
Predicting the daily variability of Equatorial Plasma Bubbles (EPBs) is an ongoing scientific challenge. Various methods for predicting EPBs have been developed, however, the research community is yet to scrutinize the methods for evaluating and comparing these prediction models/techniques. In this study, 12 months of co-located GPS and UHF scintillation observations spanning South America, Atlantic/Western Africa, Southeast Asia, and Pacific sectors are used to evaluate the Generalized Rayleigh-Taylor (R-T) growth rates calculated from the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM). Various assessment metrics are explored, including the use of significance testing on skill scores for threshold selection. The sensitivity of these skill scores to data set type (i.e., GPS versus UHF) and data set size (30, 50, 60, and 90 days/events) is also investigated. It is shown that between 50 and 90 days is required to achieve a statistically significant skill score. Methods for conducting model-model comparisons are also explored, including the use of model "sufficiency." However, it is shown that the results of model-model comparisons must be carefully interpreted and can be heavily dependent on the data set used. It is also demonstrated that the observation data set must exhibit an appropriate level of daily EPB variability in order to assess the true strength of a given model/technique. Other limitations and considerations on assessment metrics and future challenges for EPB prediction studies are also discussed. ESF/EPB research has generally been motivated by the curious aspects of the equatorial ionosphere as a natural plasma physics laboratory. However, the now heavy reliance of global society on satellite communications and navigation signals, and the adverse impacts that EPBs have on such signals, has strongly motivated research aimed at the prediction of these space weather events in recent times (e.g.
This contribution is the first of a two-part investigation into an unseasonal post-sunset equatorial F-region irregularity (EFI) event over the Southeast Asian region on the evening of 28 July 2014. Ground-based GPS scintillation data, space-based GPS radio occultation (RO) data, and ionosonde data show the existence of EFIs shortly after sunset over a region spanning 30°in longitude and 40°in latitude, centered on the geomagnetic equator. This post-sunset EFI event was observed during a time of the year when post-sunset equatorial plasma bubbles (EPBs) are very infrequent in the Southeast Asian longitude sector. GPS RO data shows that the EFI event over Southeast Asia coincided with the suppression of peak-season EPBs in the African and Pacific longitude sectors. Ionosonde data shows the presence of a strong pre-reversal enhancement (PRE) in the upward plasma drift over Southeast Asia prior to the detection of EFIs. Further, it is reported that this PRE was significantly stronger than on any other day of July 2014. An analysis of the geophysical conditions during this event reveals that this enhanced PRE was not caused by disturbed geomagnetic activity. Therefore, it is hypothesized that forcing from lower altitudes, perhaps tidal/planetary waves, was the potential cause of this strong PRE, and the subsequent EPB/EFI activity, on this day over the Southeast Asian sector.
The space environment near Earth is constantly subjected to changes in the solar wind flow
An artificial neural network (ANN) method for the modeling of global topside ionospheric vertical scale height (VSH) using electron density profiles retrieved from Global Navigation Satellite Systems radio occultation (RO) data is proposed in this study. The data for this study are 80,124 VSHs derived from the events randomly selected from 9 years of Constellation Observing System for Meteorology, Ionosphere, and Climate RO measurements and 144,530 VSHs derived from the events randomly selected from 16 years of topside sounder measurements of both Aluoette-1/2 and ISIS-1/2 satellites during 1962-1978 are used for comparison. VSHs from the International Reference Ionosphere are also used for the comparison. Results showed that: (1) the median of the relative residuals of the new ANN regression approach/model (which was based on RO measurements) was 8.5% less than that of the traditional approach/model (which was based on the topside sounder data); (2) the median of the relative residuals of the ANN model when longitude was used as a variable was 1.1% less than the one without longitude; and substantial error in the polar region was shown to be mitigated by taking the variable longitude into consideration; (3) compared to International Reference Ionosphere, the accuracy of the new ANN model was improved by around 14%; (4) the new ANN model outperforms the traditional base vector-based least squares model by around 10% when incoherent scatter radar measurements are used as a reference; and (5) the characteristics of global VSHs generated from the new model during geomagnetic storms better agree with measurements than that of the base vector-based least squares.
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