Extreme Ultraviolet Data Research Articles

Effect of ultraviolet radiation on total electron content in global positioning system observations

Climate is one of the basic elements for understanding natural phenomena and the development of civilizations throughout history. Climate change determines most of the modifications of human nature and culture, as human must adapt to changing conditions which are sometimes an important element that can enhance or threaten its existence. This study aims to determine the characteristics of changes in the total electron content (TEC) and determine the radiation has influence to TEC through observing the global positioning system (GPS) in the ionosphere. In addition, the TEC is a measure of ionospheric parameters that affect the radiation that occurs. This study uses secondary data obtained from the Cibinong Spatial Global Station in 2008 – 2012. This research was conducted as a comparison of the TEC and extreme ultraviolet (EUV). The method used is a simple statistical method which seeks the maximum and minimum the TEC and EUV data, as well as the correlation method to relate the coefficient of determination TEC and EUV. As the results the maximum TEC value is 55.895 TECU which occurred in 2012 at 7 unit time (UT) and the minimum TEC value is 1.955 TECU which occurred in 2010 at 22 UT. The correlation ratio between the TEC and EUV is directly proportional, where the higher the value of the TEC, the higher the EUV value. In closing, TEC and EUV are influence by solar activity.

Signatures of dynamic fibrils at the coronal base: Observations from Solar Orbiter/EUI

The solar chromosphere hosts a wide variety of transients, including dynamic fibrils (DFs) that are characterised as elongated, jet-like features seen in active regions, often through Hα diagnostics. So far, these features have been difficult to identify in coronal images, primarily due to their small size and the lower spatial resolution of the current extreme-ultraviolet (EUV) imagers. Here we present the first unambiguous signatures of DFs in coronal EUV data using high-resolution images from the Extreme Ultraviolet Imager (EUI) on board Solar Orbiter. Using the data acquired with the 174 Å High Resolution Imager (HRIEUV) of EUI, we find many bright dot-like features (with a size of 0.3−0.5 Mm) that move up and down (often repeatedly) in the core of an active region. In a space-time map, these features produce parabolic tracks akin to the chromospheric observations of DFs. Properties such as their speeds (14 km s−1), lifetime (332 s), deceleration (82 m s−2), and lengths (1293 km) are also reminiscent of the chromospheric DFs. The EUI data strongly suggest that these EUV bright dots are basically the hot tips (of the cooler chromospheric DFs) that could not be identified unambiguously before because of a lack of spatial resolution.

Pixel-to-pixel Translation of Solar Extreme-ultraviolet Images for DEMs by Fully Connected Networks

In this study, we suggest a pixel-to-pixel image translation method among similar types of filtergrams such as solar extreme-ultraviolet (EUV) images. For this, we consider a deep-learning model based on a fully connected network in which all pixels of solar EUV images are independent of one another. We use six-EUV-channel data from the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), of which three channels (17.1, 19.3, and 21.1 nm) are used as the input data and the remaining three channels (9.4, 13.1, and 33.5 nm) as the target data. We apply our model to representative solar structures (coronal loops inside of the solar disk and above the limb, coronal bright point, and coronal hole) in SDO/AIA data and then determine differential emission measures (DEMs). Our results from this study are as follows. First, our model generates three EUV channels (9.4, 13.1, and 33.5 nm) with average correlation coefficient values of 0.78, 0.89, and 0.85, respectively. Second, our model generates the solar EUV data with no boundary effects and clearer identification of small structures when compared to a convolutional neural network–based deep-learning model. Third, the estimated DEMs from AI-generated data by our model are consistent with those using only SDO/AIA channel data. Fourth, for a region in the coronal hole, the estimated DEMs from AI-generated data by our model are more consistent with those from the 50 frames stacked SDO/AIA data than those from the single-frame SDO/AIA data.

Solar flare catalog based on SDO/AIA EUV images: Composition and correlation with GOES/XRS X-ray flare magnitudes

Supervised Machine Learning (ML) models for solar flare prediction rely on accurate labels for a given input data set, commonly obtained from the GOES/XRS X-ray flare catalog. With increasing interest in utilizing ultraviolet (UV) and extreme ultraviolet (EUV) image data as input to these models, we seek to understand if flaring activity can be defined and quantified using EUV data alone. This would allow us to move away from the GOES single pixel measurement definition of flares and use the same data we use for flare prediction for label creation. In this work, we present a Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA)-based flare catalog covering flare of GOES X-ray magnitudes C, M and X from 2010 to 2017. We use active region (AR) cutouts of full disk AIA images to match the corresponding SDO/Helioseismic and Magnetic Imager (HMI) SHARPS (Space weather HMI Active Region Patches) that have been extensively used in ML flare prediction studies, thus allowing for labeling of AR number as well as flare magnitude and timing. Flare start, peak, and end times are defined using a peak-finding algorithm on AIA time series data obtained by summing the intensity across the AIA cutouts. An extremely randomized trees (ERT) regression model is used to map SDO/AIA flare magnitudes to GOES X-ray magnitude, achieving a low-variance regression. We find an accurate overlap on 85% of M/X flares between our resulting AIA catalog and the GOES flare catalog. However, we also discover a number of large flares unrecorded or mislabeled in the GOES catalog.

Comparison of the Coronal Green-line Intensities with the EUV Measurements from SDO/AIA

The intensity of the green line (Fe xiv 5303 Å) is the strongest in the visible spectrum of the solar corona, and this line has been used as long-term powerful diagnostic tools for studying the coronal configurations and hot plasma dynamics. However, it remains unclear and an open question whether there exists close relationship between the green line intensities and the coronal extreme ultraviolet (EUV) line emissions for various coronal structures. In this paper, we use the green-line data by the Lijiang YOGIS Lyot coronagraph and the EUV data from the Solar Dynamics Observatory/Atmospheric Imaging Assembly instruments in order to perform direct comparisons and analyses, based on two algorithms developed to extract particular features in the low corona. It is found that, among the correlation coefficients obtained between the intensities of 5303 Å and the EUV lines, the coefficients between the green line and the 211 Å wavelength for different coronal structures and different limb locations always keep the highest values (ranging from 0.89 to 0.99), which has not been reported before. This result can be helpful and promising to link together the various physical processes involved at different heights in the corona by precisely tracking the bright loops or other features observed in 5303 Å above the limb down to the correct surface locations revealed by the 211 Å data. Furthermore, the ground-based observations of the coronal green line and the space-based EUV observations at 211 Å can advantageously complement each other when there is a need.

The first widespread solar energetic particle event of solar cycle 25 on 2020 November 29

Context. On 2020 November 29, an eruptive event occurred in an active region located behind the eastern solar limb as seen from Earth. The event consisted of an M4.4 class flare, a coronal mass ejection, an extreme ultraviolet (EUV) wave, and a white-light (WL) shock wave. The eruption gave rise to the first widespread solar energetic particle (SEP) event of solar cycle 25, which was observed at four widely separated heliospheric locations (∼230°). Aims. Our aim is to better understand the source of this widespread SEP event, examine the role of the coronal shock wave in the wide distribution of SEPs, and investigate the shock wave properties at the field lines magnetically connected to the spacecraft. Methods. Using EUV and WL data, we reconstructed the global three-dimensional structure of the shock in the corona and computed its kinematics. We determined the magnetic field configurations in the corona and interplanetary space, inferred the magnetic connectivity of the spacecraft with the shock surface, and derived the evolution of the shock parameters at the connecting field lines. Results. Remote sensing observations show formation of the coronal shock wave occurring early during the eruption, and its rapid propagation to distant locations. The results of the shock wave modelling show multiple regions where a strong shock has formed and efficient particle acceleration is expected to take place. The pressure/shock wave is magnetically connected to all spacecraft locations before or during the estimated SEP release times. The release of the observed near-relativistic electrons occurs predominantly close to the time when the pressure/shock wave connects to the magnetic field lines or when the shock wave becomes supercritical, whereas the proton release is significantly delayed with respect to the time when the shock wave becomes supercritical, with the only exception being the proton release at the Parker Solar Probe. Conclusions. Our results suggest that the shock wave plays an important role in the spread of SEPs. Supercritical shock regions are connected to most of the spacecraft. The particle increase at Earth, which is barely connected to the wave, also suggests that the cross-field transport cannot be ignored. The release of energetic electrons seems to occur close to the time when the shock wave connects to, or becomes supercritical at, the field lines connecting to the spacecraft. Energetic protons are released with a time-delay relative to the time when the pressure/shock wave connects to the spacecraft locations. We attribute this delay to the time that it takes for the shock wave to accelerate protons efficiently.

Acceleration of Solar Energetic Particles through CME-driven Shock and Streamer Interaction

Abstract On 2013 June 21, a solar prominence eruption was observed, accompanied by an M2.9 class flare, a fast coronal mass ejection, and a type II radio burst. The concomitant emission of solar energetic particles (SEPs) produced a significant proton flux increase, in the energy range 4–100 MeV, measured by the Low and High Energy Telescopes on board the Solar TErrestrial RElations Observatory (STEREO)-B spacecraft. Only small enhancements, at lower energies, were observed at the STEREO-A and Geostationary Operational Environmental Satellite (GOES) spacecraft. This work investigates the relationship between the expanding front, coronal streamers, and the SEP fluxes observed at different locations. Extreme-ultraviolet data, acquired by the Atmospheric Imaging Assembly (AIA) instrument on board the Solar Dynamics Observatory (SDO), were used to study the expanding front and its interaction with streamer structures in the low corona. The 3D shape of the expanding front was reconstructed and extrapolated at different times by using SDO/AIA, STEREO/Sun Earth Connection Coronal and Heliospheric Investigation, and Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph observations with a spheroidal model. By adopting a potential field source surface approximation and estimating the magnetic connection of the Parker spiral, below and above 2.5 R ⊙, we found that during the early expansion of the eruption, the front had a strong magnetic connection with STEREO-B (between the nose and flank of the eruption front) while having a weak connection with STEREO-A and GOES. The obtained results provide evidence, for the first time, that the interaction between an expanding front and streamer structures can be responsible for the acceleration of high-energy SEPs up to at least 100 MeV, as it favors particle trapping and hence increases the shock acceleration efficiency.

Solar Prominence Bubble and Plumes Caused By an Eruptive Magnetic Flux Rope

Abstract Prominence bubbles and plumes often form near the lower prominence–corona boundary. They are believed to play an important role in mass supply and evolution of solar prominences. However, how they form is still an open question. In this Letter we present a unique high-resolution Hα observation of a quiescent prominence by the New Vacuum Solar Telescope. Two noteworthy bubble–plume events are studied in detail. The two events are almost identical, except that an erupting mini filament appeared below the prominence–bubble interface in the second event, unlike the first one or any of the reported bubble observations. Analysis of the Hα and extreme-ultraviolet data indicates that the rising magnetic flux rope (MFR) in the mini filament is the cause of bubble expansion and that the interaction between the prominence and MFR results in plume formation. These observations provided clear evidence that emerging MFR may be a common trigger of bubbles and suggested a new mechanism of plumes in addition to Rayleigh–Taylor instability and reconnection.

Mapping Solar X-Ray Images from SDO/AIA EUV Images by Deep Learning

Abstract The full-Sun corona is now imaged every 12 s in extreme ultraviolet (EUV) passbands by Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA), whereas it is only observed several times a day at X-ray wavelengths by Hinode/X-Ray Telescope (XRT). In this paper, we apply a deep-learning method, i.e., the convolution neural network (CNN), to establish data-driven models to generate full-Sun X-ray images in XRT filters from AIA EUV images. The CNN models are trained using a number of data pairs of AIA six-passband (171, 193, 211, 335, 131, and 94 Å) images and the corresponding XRT images in three filters: “Al_mesh,” “Ti_poly,” and “Be_thin.” It is found that the CNN models predict X-ray images in good consistency with the corresponding well-observed XRT data. In addition, the purely data-driven CNN models are better than the conventional analysis method of the coronal differential emission measure (DEM) in predicting XRT-like observations from AIA data. Therefore, under conditions where AIA provides coronal EUV data well, the CNN models can be applied to fill the gap in limited full-Sun coronal X-ray observations and improve pool-observed XRT data. It is also found that DEM inversions using AIA data and our deep-learning-predicted X-ray data jointly are better than those using AIA data alone. This work indicates that deep-learning methods provide the opportunity to study the Sun based on virtual solar observation in future.

A Fast, Simple, Robust Algorithm for Coronal Temperature Reconstruction

Abstract We describe a new algorithm for reconstruction of differential emission measures (DEMs) in the solar corona. Although a number of such algorithms currently exist, they can have difficulty converging for some cases and can be complex, slow, or idiosyncratic in their output (i.e., their inversions can have features that are a result of the inversion code and instrument response, not of the solar source); we will document some of these issues in this paper. The new algorithm described here significantly reduces these drawbacks and is particularly notable for its simplicity; it is reproduced here, in full, on a single page. After we describe the algorithm, we compare its performance and fidelity with some prevalent methods. Although presented here for extreme-ultraviolet data, the algorithm is robust and extensible to any other wavelengths (e.g., X-rays) where the DEM treatment is valid.

Sun–Heliosphere Observation-based Ionization Rates Model

Abstract The solar wind (SW) and the extreme ultraviolet (EUV) radiation modulate fluxes of interstellar and heliospheric particles inside the heliosphere both in time and in space. Understanding this modulation is necessary to correctly interpret measurements of particles of interstellar origin inside the heliosphere. We present a revision of heliospheric ionization rates and provide the Sun–Heliosphere Observation-based Ionization Rates model based on the currently available data. We calculate the total ionization rates using revised SW and solar EUV data. We study the in-ecliptic variation of the SW parameters, the latitudinal structure of the SW speed and density, and the reconstruction of the photoionization rates. The revision most affects the SW out of the ecliptic plane during solar maximum and the estimation of the photoionization rates, the latter due to a change of the reference data. The revised polar SW is slower and denser during the solar maximum of solar cycle (SC) 24. The current estimated total ionization rates are higher than the previous ones for H, O, and Ne, and lower for He. The changes for the in-ecliptic total ionization rates are less than 10% for H and He, up to 20% for O, and up to 35% for Ne. Additionally, the changes are not constant in time and vary as a function of time and latitude.

The Application of the Filtered Backprojection Algorithm to Solar Rotational Tomography

Abstract Solar rotational tomography (SRT) is an important method to reconstruct the physical parameters of the three-dimensional solar corona. Here we propose an approach to apply the filtered backprojection (FBP) algorithm to the SRT. The FBP algorithm is generally not suitable for SRT due to the several issues with solar extreme ultraviolet (EUV) observations—in particular, a problem caused by missing data because of the unobserved back side of corona hidden behind the Sun. We developed a method to generate a modified sinogram that resolves the blocking problem. The modified sinogram is generated by combining the EUV data at two opposite sites observed by the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory (SDO). We generated the modified sinogram for about one month in 2019 February and reconstructed the three-dimensional corona under the static state assumption. In order to obtain the physical parameters of the corona, we employed a differential emission measure inversion method. We tested the performance of the FBP algorithm with the modified sinogram by comparing the reconstructed data with the observed EUV image, electron density models, previous studies of electron temperature, and an observed coronagraph image. The results illustrate that the FBP algorithm reasonably reconstructs the bright regions and the coronal holes and can reproduce their physical parameters. The main advantage of the FBP algorithm is that it is easy to understand and computationally efficient. Thus, it enables us to easily probe the inhomogeneous coronal electron density and temperature distribution of the solar corona.

Synthesising solar radio images from Atmospheric Imaging Assembly extreme-ultraviolet data

During non-flaring times, the radio flux of the Sun at wavelengths of a few centimeters to several tens of centimeters mostly originates from thermal bremsstrahlung emission, very similar to extremeultraviolet (EUV) radiation. Owing to such a proximity, it is feasible to investigate the relationship between the EUV emission and radio emission in a quantitative way. In this paper, we reconstruct the radio images of the Sun through the differential emission measure obtained from multi-wavelength EUV images of the Atmospheric Imaging Assembly on board Solar Dynamics Observatory (SDO). Through comparing the synthetic radio images at 6GHz with those observed by the Siberian Radioheliograph, we find that the predicted radio flux is qualitatively consistent with the observed value, confirming thermal origin of the coronal radio emission during non-flaring times. The results further show that the predicted radio flux is closer to the observations in the case that includes the contribution of plasma with temperatures above 3MK than in the case of only involving low temperature plasma, as was usually done in the pre-SDO era. We also discuss applications of the method and uncertainties of the results.

Direct Estimates of the Solar Coronal Magnetic Field Using Contemporaneous Extreme-ultraviolet, Radio, and White-light Observations

Abstract We report a solar coronal split-band type II radio burst that was observed on 2016 March 16 with the Gauribidanur Radio Spectro-Polarimeter in the frequency range ≈90–50 MHz, and the Gauribidanur RadioheliograPH at two discrete frequencies, viz. 80 and 53.3 MHz. Observations around the same epoch in extreme ultraviolet (EUV) and white light show that the above burst was associated with a flux-rope structure and a coronal mass ejection (CME), respectively. The combined height–time plot generated using EUV, radio, and white-light data suggests that the different observed features (i.e., the flux rope, type II burst, and the CME) are all closely associated. We constructed an empirical model for the coronal electron density distribution (N e (r), where r is the heliocentric distance) from the above set of observations themselves and used it to estimate the coronal magnetic field strength (B) over the range of r values in which the respective events were observed. The B values are consistent with each other. They vary as B(r) = 2.61 × r −2.21 G in the range r ≈ 1.1–2.2R ⊙. As far as we know, similar direct estimates of B in the near-Sun corona without assuming a model for N e (r), and by combining cotemporal set of observations in two different regions (radio and white-light) of the electromagnetic spectrum, have rarely been reported. Further, the present work is a novel attempt where the characteristics of a propagating EUV flux-rope structure, considered to be the signature of a CME close to the Sun, have been used to estimate B(r) in the corresponding distance range.

The Mid-Term Forecast Method of F10.7Based on Extreme Ultraviolet Images

The solar radio flux at 10.7cm (F10.7) is a direct monitor and an important indicator of solar variability, and F10.7is commonly used in empirical atmospheric models, ionosphere models, etc. The source regions of F10.7are mainly in the corona above the active regions, and the extreme ultraviolet (EUV) images reflect the coronal thermal structure. In this paper, an index is defined asPSRbased on the intensity values of solar EUV images to represent the coronal contribution to F10.7. The Spearman correlation coefficient between the observed values of F10.7andPSRis 0.85 in 304 Å EUV images. Based on the high correlation, an empirical model is constructed. Combining the EUV data of SDO/AIA and the twin STEREO/EUVI, solar full-disk EUV images can be generated, and the future 27-day values ofPSRcan be calculated. Then, a realistic estimation of F10.7from 1 to 27 days in advance can be provided by the empirical model. Compared to the predictive values of F10.7by the 54th-order autoregressive models in 2012-2013, the error drop-rate of our model is 12.54%, and our method has significant advantages in the upcoming 3 to 27 days’ forecast.

Solar farside magnetograms from deep learning analysis of STEREO/EUVI data

Solar magnetograms are important for studying solar activity and predicting space weather disturbances1. Farside magnetograms can be constructed from local helioseismology without any farside data2-4, but their quality is lower than that of typical frontside magnetograms. Here we generate farside solar magnetograms from STEREO/Extreme UltraViolet Imager (EUVI) 304-A images using a deep learning model based on conditional generative adversarial networks (cGANs). We train the model using pairs of Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) 304-A images and SDO/Helioseismic and Magnetic Imager (HMI) magnetograms taken from 2011 to 2017 except for September and October each year. We evaluate the model by comparing pairs of SDO/HMI magnetograms and cGAN-generated magnetograms in September and October. Our method successfully generates frontside solar magnetograms from SDO/AIA 304-A images and these are similar to those of the SDO/HMI, with Hale-patterned active regions being well replicated. Thus we can monitor the temporal evolution of magnetic fields from the farside to the frontside of the Sun using SDO/HMI and farside magnetograms generated by our model when farside extreme-ultraviolet data are available. This study presents an application of image-to-image translation based on cGANs to scientific data. Farside solar magnetograms are generated from STEREO images using deep learning, with Hale-patterned active regions being well reproduced. These images can be used to monitor the temporal evolution of magnetic fields from the farside to the frontside.

Comprehensive Analysis of the Formation of a Shock Wave Associated with a Coronal Mass Ejection

Abstract On 2014 November 1, a solar prominence eruption associated with a C2.7 class flare and a type II radio burst resulted in a fast partial halo coronal mass ejection (CME). Images acquired in the extreme ultraviolet (EUV) by the Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA) and PROBA2/SWAP and in white light (WL) by Solar and Heliospheric Observatory/Large Angle and Spectrometric Coronagraph show the expansion of a bright compression front ahead of the CME. In this work, we present a detailed investigation of the CME-driven shock associated with this event following the early evolution of the compression front observed near the Sun up to the extended corona. Our aim is to shed light on the long-debated issue concerning the location and timing of shock formation in the corona. Through differential emission measure analysis, we derived, for the first time, the compression ratio across the expanding EUV front observed by AIA at different temperature ranges: higher compression ratios corresponded to higher plasma temperature ranges, as expected. Moreover, comparison between up- and downstream temperatures and those expected via adiabatic compression shows that no additional heating mechanisms occurred in the early front expansion phase, implying that the shock formed beyond the AIA field of view. Finally, the analysis of the associated type II radio burst, in combination with the inferred coronal density distribution, allowed us to identify a well-defined region located northward of the CME source region as the site for shock formation and to outline its kinematics in accordance with the evolution of the expanding front as obtained from the EUV and WL data.

Predicting well-connected SEP events from observations of solar EUVs and energetic protons

This study shows a quantitative assessment of the use of Extreme Ultraviolet (EUV) observations in the prediction of Solar Energetic Proton (SEP) events. The UMASEP scheme (Space Weather, 9, S07003, 2011; 13, 2015, 807–819) forecasts the occurrence and the intensity of the first hours of SEP events. In order to predict well-connected events, this scheme correlates Solar Soft X-rays (SXR) with differential proton fluxes of the GOES satellites. In this study, we explore the use of the EUV time history from GOES-EUVS and SDO-AIA instruments in the UMASEP scheme. This study presents the results of the prediction of the occurrence of well-connected >10 MeV SEP events, for the period from May 2010 to December 2017, in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and the average and median of the warning times. The UMASEP/EUV-based models were calibrated using GOES and SDO data from May 2010 to October 2014, and validated using out-of-sample SDO data from November 2014 to December 2017. The best results were obtained by those models that used EUV data in the range 50–340 Å. We conclude that the UMASEP/EUV-based models yield similar or better POD results, and similar or worse FAR results, than those of the current real-time UMASEP/SXR-based model. The reason for the higher POD of the UMASEP/EUV-based models in the range 50–340 Å, was due to the high percentage of successful predictions of well-connected SEP events associated with <C4 flares and behind-the-limb flares, which amounted to 25% of all the well-connected events during the period May 2010 to December 2017. By using all the available data (2010–2017), this study also concluded that the simultaneous use of SXRs and EUVs in 94 Å in the UMASEP-10 tool for predicting all >10 MeV SEP events, improves the overall performance, obtaining a POD of 92.9% (39/42) compared with 81% (34/42) of the current tool, and a slightly worse FAR of 31.6% (18/57) compared with 29.2% (14/58) of the current tool.

Continuous Doppler sounding of the ionosphere during solar flares

Solar flares cause a rapid increase in ionization in the ionosphere owing to significant enhancement of ionizing solar radiation in the X-ray and extreme ultraviolet (EUV) spectral ranges. The change of electron densities in the ionosphere influences the propagation of radio waves. The ionospheric response to solar flares is investigated for three selected examples recorded during the maximum and decreasing phase of the solar cycle 24 with time resolution of several seconds by continuous Doppler sounding systems installed in the Czech Republic (50N, 14E), Taiwan (24N, 121E) and Northern Argentina (27S, 65W). The reflection heights of sounding signals are derived from nearby ionospheric sounders. The measured Doppler shifts are compared with EUV and X-ray data from the GOES-15 satellite. It is shown that the largest Doppler shifts are observed at times when the time derivatives of EUV fluxes are maximal, while the Doppler shifts are around zero at times when the EUV fluxes reach maxima. This means that loss processes balance the ionization when the EUV fluxes maximize. The attenuation of Doppler signal caused by enhanced electron density in the D and E layer was well correlated with the cosmic noise absorption measured by riometer. For large ionizing fluxes, the attenuation leads to very low signal-to-noise ratio, loss of the received signal, and inability to process both Doppler shift spectrograms and ionograms.

Phase analysis of solar activity indices using wavelet techniques

AbstractA precise knowledge of solar extreme ultraviolet (EUV) irradiance is of great importance for better understanding of Earth′s ionosphere and thermosphere. The search for an ideal solar EUV proxy is vital since the ionospheric and thermospheric models are based on the solar proxies of EUV radiation. In this study, the phase asynchrony analysis of solar EUV data with other solar activity indices during solar cycle 23 is done. The cross-wavelet transform (XWT) technique is used to reveal the phase difference between the two time series of solar indices. Analysis reveals that the phase relationship between the indices is both time and frequency dependent. The solar indices F10.7 and Mg II core-to-wing index are found to be more synchronous with solar EUV data for low frequency components.