Search

The stratification phenomenon is investigated using the simultaneous in situ plasma density measurements obtained by the Swarm satellites orbiting at different altitudes above the F2 peak. For the first time, the continuous distribution morphology and the exact locations are obtained for the nighttime stratification, which show that the stratification events are centered at the EIA (equatorial ionization anomaly) trough and extend towards the two EIA crests, with the most significant part being located at the EIA trough. Another new discovery is the stratification in southern mid-latitudes; stratification events in this region are located on a local plasma peak sandwiched by two lower density strips covering all the longitudes. The formation mechanism of the stratification for the two latitudinal regions is discussed, but the stratification mechanism in southern mid-latitudes remains an unsolved problem. Highlights. This paper addresses the following: first application of in situ plasma densities for the direct analysis of the stratification in F2 layer, refined features of the exact location and continuous morphology for the stratification phenomenon, a new discovery of stratification covering all longitudes in southern mid-latitudes. [ABSTRACT FROM AUTHOR]

CSES (China Seismo-Electromagnetic Satellite) is a newly launched electric-magnetic satellite in China. A GNSS occultation receiver (GOR) is installed on the satellite to retrieve electron density related parameters. In order to validate the radio occultation (RO) data from the GOR on board CSES, a comparison between CSES RO and the co-located COSMIC RO data is conducted to check the consistency and reliability of the CSES RO data using measurements from 12 February 2018 to 31 March 2019. CSES RO peak values (Nm F2), peak heights (hm F2), and electron density profiles (EPDs) are compared with corresponding COSMIC measurements in this study. The results show that (1) Nm F2 between CSES and COSMIC is in extremely good agreement, with a correlation coefficient of 0.9898. The near-zero bias between the two sets is 0.005363×105 cm -3 with a RMSE of 0.3638×105 cm -3 , and the relative bias is 1.97 % with a relative RMSE of 16.17 %, which are in accordance with previous studies according to error propagation rules. (2) hm F2 between the two missions is also in very good agreement with a correlation coefficient of 0.9385; the mean difference between the two sets is 0.59 km with a RMSE of 12.28 km, which is within the error limits of previous studies. (3) Co-located EDPs between the two sets are generally in good agreement, but with a better agreement for data above 200 km than those below this altitude. Data at the peak height ranges show the best agreement, and then data above the peak regions; data below the peak regions, especially at the altitude of about the E layer, show relatively large fluctuations. It is concluded that CSES RO data are in good agreement with COSMIC measurements, and the CSES RO data are applicable for most ionosphere-related studies considering the wide acceptance and application of COSMIC RO measurements. However, particular attention should be paid to EDP data below peak regions in application as data at the bottom side of the profiles are less reliable than that at the peak and topside regions. [ABSTRACT FROM AUTHOR]

Many studies have revealed the stratification phenomenon of the topside ionospheric F2 layer using ground-based or satellite-based ionograms, which can show direct signs of this phenomenon. However, it is difficult to identify this phenomenon using the satellite-based in situ electron density data. Therefore, a statistical method, using the shuffle resampling skill, is adopted in this paper. For the first time, in situ electron density data, recorded by the same Langmuir probe aboard the DEMETER (Detection of Electro-Magnetic Emission Transmitted from Earthquake Regions) satellite at different altitudes, are analyzed, and a possible stratification phenomenon is identified using the proposed method. Our results show that the nighttime stratification, possibly a permanent phenomenon, can cover most longitudes near the geomagnetic equator, which is not found from the daytime data. The arch-like nighttime stratification decreases slowly on the summer hemisphere and thus extends a larger latitudinal distance from the geomagnetic equator. All results, obtained by the proposed method, indicate that the stratification phenomenon is more complex than what has previously been found. The proposed method is thus an effective one, which can also be used in similar studies of comparing fluctuated data. [ABSTRACT FROM AUTHOR]

The Search Coil Magnetometer(SCM) on China Seismo-Electromagnetism Satellite(CSES), on the basis of Faraday's law of electromagnetic induction, can obtain ULF/ELF/VLF information from variable magnetic field in the ionosphere. The information is used to study anomaly phenomena such as seismic activities, magnetic storm and unknown signals etc.This study is aimed at detecting abnormal phenomena from VLF power spectrum data in SCM ranging from 12.5hz to 25.6khz. VLF signals radiated by artificial VLF transmitters and magnetic storm are defined as abnormal phenomena. Constructing a detection model contributes to search abnormal phenomena in big data quickly and accurately.Firstly, in specific frequency, building training and testing samples, based on the acquired data from both the areas above artificial VLF transmitters (termed as positive samples) and other observed areas (termed as negative samples) respectively, needs to use fixed time window in each orbit to create two-dimensional images through Mel Frequency Cepstrum Coefficient (MFCC). Secondly, deep learning methods used as a detection model, such as CNN, is to detect abnormal phenomena from those samples. Finally, we can use this method to detect each time window in all of the VLF power spectrum data in order to fast find some interesting phenomena. In our experiments, we collect 440 samples including positive samples and negative samples. And we use 110 samples for each class as training data, and the others as testing data. The results show that testing precision of our method reaches 86%. If more data is added to CNN, our method can detect abnormal phenomena more efficiently. [ABSTRACT FROM AUTHOR]