Solar EUV Research Articles

An Empirical Orthogonal Function Study of the Ionospheric TEC Predicted Using the TIEGCM Model over the South Atlantic Anomaly in 2002 and 2008

In this study, the variability of the ionospheric total electron content (TEC) in the South Atlantic Anomaly (SAA) in the solar maximum of 2002 and the solar minimum of 2008 were compared by using an empirical orthogonal function (EOF) analysis. The ionospheric TEC data were simulated using the National Center for Atmospheric Research Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM). The first three EOFs accounted for 94.8% and 93.86% of the variability in the data in 2002 and 2008, respectively. The results showed that the TEC variations of the first three EOFs were generally consistent in 2002 and 2008. The first mode showed the equatorial anomaly caused by plasma drift and the east–west asymmetry possibly caused by the change in geomagnetic declination and zonal wind; EOF2 exhibited the zonal variation influenced by the solar EUV radiation and the semiannual variation possibly controlled by the [O/N2], solar zenith angle, and atmospheric circulation. EOF3 suggested an equatorial anomaly and winter anomaly influenced by the [O/N2] variation. However, the values and amplitude variations in the TEC were significantly greater in the solar maximum than that in the solar minimum, and the spring–autumn asymmetry of the TEC was more obvious in the solar minimum. In addition, we used the EOF method to extract the annual variation characteristics of the time coefficients and carried out a correlation analysis. The results showed that the annual variation in the TEC in 2002 was mainly affected by the solar EUV radiation, which was strongly correlated with F10.7 (r = 0.7348). In contrast, the TEC was mainly influenced by the geomagnetic activity in 2008 and had a strong correlation with Dst (r = −0.7898).

Models for magnetospheric mass density and average ion mass including radial dependence

Analytical models for magnetospheric mass density, ρm, and average ion mass, M, were created from a database of ρm and electron density, ne, values from six spacecraft missions by making use of the Eureqa nonlinear genetic regression algorithm. All values of ρm were determined from Alfvén frequencies, and the values of ne were determined from plasma wave or spacecraft potential data. Models of varying complexity are listed. The most complex models appearing in this paper are capable of modeling ρm within a factor of 1.81, and M within a factor of 1.34 if ne is used as an input parameter, or within a factor of 1.45 if ne is not used. The most important parameters for modeling ρm are L, the solar EUV index F10.7, magnetic local time, MLT, the geomagnetic activity index Kp, and the solar wind dynamic pressure, Pdyn. The very simplest model for M depends on Kp. In more complex models for M including ne, the most important parameters are ne with L, F10.7, and Pdyn or Kp. In more complex models for M not including ne, the most important parameters are Kp, MLT, F10.7, L, and the auroral electrojet index, AE. Explanations for most of the dependencies are given. We also demonstrate the danger of calculating spatial dependence without taking account of different conditions sampled in different regions. Here we avoid that problem by using multivariant models.

An Algorithm to Extract the Boundary and Center of EUV Solar Image Based on Sobel Operator and FLICM

An algorithm to extract the disk boundary and center of EUV solar image using the Sobel operator, Fuzzy Local Information C-Means Clustering algorithm (FLICM), and the least square circle fitting method is proposed in this paper. The Sobel operator can determine the solar disk boundary preliminarily, and then the image is processed further using the FLICM algorithm. After the background is removed based on the clustered image and the boundary points can be highlighted, these points are fitted using the least square circle fitting method as the final boundary circle. The solar data used in this paper was from the observation of the Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO/AIA) instrument. The 2523 19.3 nm solar images covering solar minimum, moderate solar activity, and more active suns were calculated using the proposed algorithm to analyze the accuracy statistically. The statistical comparison results demonstrate that the method is accurate and effective. This method can support the processing of solar EUV images and serve the operational system of a space weather forecast.

Exploring the Limits of Synthetic Creation of Solar EUV Images via Image-to-image Translation

The Solar Dynamics Observatory (SDO), a NASA multispectral decade-long mission that has been daily producing terabytes of observational data from the Sun, has been recently used as a use case to demonstrate the potential of machine-learning methodologies and to pave the way for future deep space mission planning. In particular, the idea of using image-to-image translation to virtually produce extreme ultraviolet channels has been proposed in several recent studies, as a way to both enhance missions with fewer available channels and to alleviate the challenges due to the low downlink rate in deep space. This paper investigates the potential and the limitations of such a deep learning approach by focusing on the permutation of four channels and an encoder–decoder based architecture, with particular attention to how morphological traits and brightness of the solar surface affect the neural network predictions. In this work we want to answer the question: can synthetic images of the solar corona produced via image-to-image translation be used for scientific studies of the Sun? The analysis highlights that the neural network produces high-quality images over 3 orders of magnitude in count rate (pixel intensity) and can generally reproduce the covariance across channels within a 1% error. However, the model performance drastically diminishes in correspondence to extremely high energetic events like flares, and we argue that the reason is related to the rareness of such events posing a challenge to model training.

Forecasting the Geomagnetic Activity Several Days in Advance Using Neural Networks Driven by Solar EUV Imaging

AbstractMany models of the near‐Earth's space environment (radiation belts, ionosphere, upper atmosphere, etc.) are driven by geomagnetic indices, representing the state of disturbance of the Earth's magnetosphere. Over the past decade, machine learning‐based methods for forecasting geomagnetic indices from near‐Earth solar wind parameters have become popular in the space weather community. These methods often prove to be very accurate and skilled. However, these approaches have the notable drawback of being effective in an operational context only for limited forecasting horizons (often up to a couple of hours ahead at best). In order to increase this prediction horizon, we introduce SERENADE, a novel deep learning‐based proof‐of‐concept model using images delivered by the Atmospheric Imaging Assembly instrument onboard the Solar Dynamics Observatory spacecraft to directly provide probabilistic forecasts of the daily maximum of the geomagnetic index Kp up to a few days ahead. We show in particular that SERENADE is able to capture information on the geomagnetic dynamics from solar imaging alone. In addition, despite it being a prototypical model, our model is more accurate in most situations than three empirical baseline models. However, the model still shows some strong limitations inherent to its structure and the used data set, which could be the focus of future works. This opens the way to a better mid‐to‐long term data‐driven magnetospheric modeling within space weather and geophysical pipelines.

Gleissberg Cycle Dependence of Inner Zone Proton Flux

AbstractInner zone proton flux from 1980 to mid‐2021 is examined using NOAA POES satellite data, indicating a long‐term increase corresponding to a one hundred year minimum in solar activity consistent with the Centennial Gleissberg Cycle. Variation of inner belt protons is correlated with decreasing F10.7 maxima over the 40‐year period, serving as proxy for solar EUV input to Earth's atmosphere. Extending an earlier study (Qin et al., 2014; https://doi.org/10.1002/2014JA020300) of >70 MeV protons from 1980 – 2021 using the South Atlantic Anomaly (SAA) peak flux, and at fixed L = 1.3, a comparison is made between the >35, >70 and >140 MeV energy channels on POES. All three energies show an increase in proton flux over the period 1998 – 2021 using a single spacecraft. The observed flux increase is correlated with decreasing F10.7 over the longer 40‐year time interval, as with the ∼11‐year solar cycle. A phase lag during Solar Cycle 24 (January 2010 – June 2021) between the F10.7 minimum and proton flux maximum was determined to be ∼500 days, the same at all energies studied. A model calculation of the inner zone proton flux is found to generally confirm the long‐term trend examined both in absolute magnitude and phase lag. It is concluded that this long‐term trend is a manifestation of the concurrent Gleissberg cycle minimum and accompanying decrease in solar EUV. Reduced EUV at solar maximum (F10.7 proxy) reduces proton loss to the atmosphere following solar maximum, thus explaining the long‐term flux increase observed.

Non-thermal escape of the martian CO[formula omitted] atmosphere over time: Constrained by Ar isotopes

The ion escape of Mars’ CO2 atmosphere caused by its dissociation products C and O atoms is simulated from present time to ∼4.1 billion years ago (Ga) by numerical models of the upper atmosphere and its interaction with the solar wind. The planetward-scattered pick-up ions are used for sputtering estimates of exospheric particles including 36Ar and 38Ar isotopes. Total ion escape, sputtering and photochemical escape rates are compared. For solar EUV fluxes ≥3 times that of today’s Sun (earlier than ∼2.6 Ga) ion escape becomes the dominant atmospheric non-thermal loss process until thermal escape takes over during the pre-Noachian eon (earlier than ∼4.0–4.1 Ga). If we extrapolate the total escape of CO2-related dissociation products back in time until ∼4.1 Ga we obtain a maximum theoretical equivalent to CO2 partial pressure of more than ∼0.4 bar through non-thermal escape during quiet solar wind conditions. However, surface–atmosphere interaction and/or extreme solar events such as frequent CMEs could have increased this value even further. By including the surface as a sink, up to 0.9bar, or even up to 1.8bar in case of hidden carbonate reservoirs, could have been present at 4.1Ga The fractionation of 36Ar/38Ar isotopes through sputtering and volcanic outgassing from its initial chondritic value of 5.3, as measured in the 4.1 billion years old Mars meteorite ALH 84001, until the present day, however, can be reproduced for assumed CO2 partial pressures of ∼0.01–0.4bar without, and ∼0.4–1.8bar including surface sinks, and depending on the cessation time of the Martian dynamo (assumed between 3.6 and 4.0 Ga) — if atmospheric sputtering of Ar started afterwards.

Influence of the solar wind high-speed streams on the thermospheric neutral density during the declining phase of solar cycle 23

Thermospheric neutral density derived from the GRACE A satellite and normalized at altitude of 490 km (ρ) are used to investigate the ρ behavior during the period of recurrent geomagnetic activity (25 January - 5 May, 2005) caused by solar wind high-speed streams (HSSs). The north and south hemispheric ρ values divided in three latitudinal bins are analyzed separately at dayside and nightside. The variations, not caused by solar/geomagnetic activity, are investigated accounting for the relationship between solar flux index, illumination, latitude and DOY for particular magnetic local times (MLTs). Analysis has shown that: ρ increases/decreases from high to low latitudes at dayside/nightside; ρ increases from high to low latitudes at dayside caused by solar EUV radiation is most prominent around MLT = 14 and decrease rapidly with departure from this MLT; the difference between north and south hemispheric ρ values are not the same prior and after the equinox; quiet time ρ differs among latitudinal bins; ρ contains a clear imprint of solar/geomagnetic activity. The cross-correlation analysis reveals a high degree of correlation between ρ and different employed ρ indicators. Time lags based on all ρ indicators suggest that ρ disturbance: occurs about 4 h earlier at high than at lower latitudes and propagates presumably without losses toward equatorial region both at dayside and nightside; propagates faster between polar and mid latitudes at dayside and between mid and low latitudes at nightside. This study provides the most probable characteristics of the ρ variations during the periods in which magnetospheric-thermospheric system is affected primarily by HSSs.

The Height‐Dependent Delayed Ionospheric Response to Solar EUV

AbstractBased on the analysis of electron density Ne profiles (Grahamstown ionosonde), a case study of the height‐dependent ionospheric response to two 27‐day solar rotation periods in 2019 is performed. A well‐defined sinusoidal response is observed for the period from 27 April 2019 to 24 May 2019 and reproduced with a Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model simulation. The occurring differences between model and observations as well as the driving physical and chemical processes are discussed based on the height‐dependent variations of Ne and major species. Further simulations with an artificial noise free sinusoidal solar flux input show that the Ne delay is defined by contributions due to accumulation of O+ at the Ne peak (positive delay) and continuous loss of in the lower ionosphere (negative delay). The neutral parts' 27‐day signatures show stronger phase shifts. The time‐dependent and height‐dependent impact of the processes responsible for the delayed ionospheric response can therefore be described by a joint analysis of the neutral and ionized parts. The return to the initial ionospheric state (and thus the loss of the accumulated O+) is driven by an increase of downward transport in the second half of the 27‐day solar rotation period. For this reason, the neutral vertical winds (upwards and downwards) and their different height‐dependent 27‐day signatures are discussed. Finally, the importance of a wavelength‐dependent analysis, statistical methods (superposed epoch analysis), and coupling with the middle atmosphere is discussed to outline steps for future analysis.

Auroral Ionosphere Model with PC Index as an Input

Auroral Ionosphere Model (AIM-E) is designed to calculate chemical content in the high-latitude E region ionosphere and takes into account both the solar EUV radiation and the electron precipitation of magnetospheric origin. The latter is extremely important for auroral ionosphere chemistry especially in disturbed conditions. In order to maximize the AIM-E timing accuracy when simulating highly variable periods in the course of geomagnetic storms and substorms, we suggest to parameterize the OVATION-Prime empirical precipitation model with the ground-based Polar Cap (PC) index. This gives an advantage to: (1) perform ionospheric simulation with actual input, since PC index reflects the geoeffective solar wind conditions; (2) promptly assess the current geomagnetic situation, since PC index is available in real-time with 1 min resolution. The simulation results of AIM-E with OVATION-Prime (PC) demonstrate a good agreement with the ground-based incoherent scatter radar data (EISCAT UHF, Tromso) and with the vertical sounding data in the Arctic zone during events of intense particle precipitation. The model reproduces well the electron content calculated in vertical column (90–140 km) and critical frequency of sporadic E layer (fOEs) formed by precipitating electrons. The AIM-E (PC) model can be applied to monitor the sporadic E layer in real-time and in the entire high-latitude ionosphere, including the auroral and subauroral zones, which is important for predicting the conditions of radio wave propagation.

Comments on “A new method to subtract dayglow for auroral observation of SSUSI in LBH ranges based on the improved AURIC” by Wang et al. (2021)

A paper A new method to subtract dayglow for auroral observation of SSUSI in LBH ranges based on the improved AURIC reports a new method to estimate the dayglow intensities in DMSP/SSUSI LBH bands using an improved AURIC model. It is claimed that the new method offers a better alternative than the SSUSI operational algorithm which uses a data based table. The paper showed a few examples and compared them with SSSUI operational results. The comparison indicated that the new method didn't offer any improvement and provided net auroral images with strong residual dayglow. On the other hand, the auroral oval can be easily recognized in the SSUSI data using the operational algorithm, despite some weak residual background which is expected due to count errors in the data. There are likely a few reasons why the method led to poor results: (1) dayglow contribution in SSUSI data covers solar zenith angles (SZA) beyond 90° and the AURIC model is limited to SZA ≤90°, (2) In addition to SZA, SSUSI radiances also depend on look angle (along and cross track pixels). Such a look-angle effect was apparently not reported in the paper. (3) The localized peaks in the plots (radiance versus SZA) were likely due to changes in solar EUV flux, SZA as well as noises caused Southern Atlantic Anomaly, MeV particles at sub-auroral latitude and glint in the Ap dependent data bins. The examples in the paper indicate that the new algorithm is not appropriate to estimate net SSUSI dayglow intensity.

Thermospheric density enhancement and limb O 130.4 nm radiance increase during geomagnetic storms

We explore a connection between thermospheric density enhancement and increase in thermospheric O 130.4 nm radiance. We observe TIMED/GUVI enhancements in the limb 130.4 nm radiances at ∼400 and ∼520 km on the dayside during four intense geomagnetic storms in 2003 and 2004. The enhancements were well correlated with Dst and CHAMP total neutral density at 400 km which represents O density as O is the dominant species at those altitudes. At the 400 and 520 km altitudes, O 130.4 nm emissions are mostly created by two comparable sources: solar resonance scatter and photoelectron impact excitation. The coincident disk 130.4 nm radiances, mostly due to emissions below 200 km (peaked around 130–140 km), were not clearly correlated with the limb radiances. Because the limb 130.4 nm radiances depend on O density, solar EUV and 130.4 nm fluxes, variations in the limb 130.4 nm radiance respond mostly to changes in O density when the solar EUV and 130.4 nm fluxes are stable. This explains the good correlation (correlation coefficients up to 0.98) between the limb 130.4 nm radiance and CHAMP neutral density. Once a quantitative relationship is established between GUVI limb 130.4 nm radiance and neutral density under both quiet and disturbed conditions and at different altitude levels through empirical or radiative transfer modeling, the limb 130.4 nm radiances can be used to retrieve O density profiles in the upper thermosphere.

Predicting the Daily 10.7-cm Solar Radio Flux Using the Long Short-Term Memory Method

As an important index of solar activity, the 10.7-cm solar radio flux (F10.7) can indicate changes in the solar EUV radiation, which plays an important role in the relationship between the Sun and the Earth. Therefore, it is valuable to study and forecast F10.7. In this study, the long short-term memory (LSTM) method in machine learning is used to predict the daily value of F10.7. The F10.7 series from 1947 to 2019 are used. Among them, the data during 1947–1995 are adopted as the training dataset, and the data during 1996–2019 (solar cycles 23 and 24) are adopted as the test dataset. The fourfold cross validation method is used to group the training set for multiple validations. We find that the root mean square error (RMSE) of the prediction results is only 6.20~6.35 sfu, and the correlation coefficient (R) is as high as 0.9883~0.9889. The overall prediction accuracy of the LSTM method is equivalent to those of the widely used autoregressive (AR) and backpropagation neural network (BP) models. Especially for 2-day and 3-day forecasts, the LSTM model is slightly better. All this demonstrates the potentiality of the LSTM method in the real-time forecasting of F10.7 in future.

A Fast Bow Shock Location Predictor-Estimator From 2D and 3D Analytical Models: Application to Mars and the MAVEN Mission.

We present fast algorithms to automatically estimate the statistical position of the bow shock from spacecraft data, using existing analytical two‐dimensional (2D) and three‐dimensional (3D) models of the shock surface. We derive expressions of the standoff distances in 2D and 3D and of the normal to the bow shock at any given point on it. Two simple bow shock detection algorithms are constructed, one solely based on a geometrical predictor from existing models, the other using this predicted position to further refine it with the help of magnetometer data, an instrument flown on many planetary missions. Both empirical techniques are applicable to any planetary environment with a defined shock structure. Applied to the Martian environment and the NASA/MAVEN mission, the predicted shock position is on average within 0.15 planetary radius R p of the bow shock crossing. Using the predictor‐corrector algorithm, this estimate is further refined to within a few minutes of the true crossing (≈0.05R p). Between 2014 and 2021, we detect 14,929 clear bow shock crossings, predominantly quasi‐perpendicular. Thanks to 2D conic and 3D quadratic fits, we investigate the variability of the shock surface with respect to Mars Years (MY), solar longitude (Ls), and solar EUV flux levels. Although asymmetry in Y and Z Mars Solar Orbital coordinates is on average small, we show that for MY32 and MY35, Ls = [135°−225°] and high solar flux, it can become particularly noticeable, and is superimposed to the usual North‐South asymmetry due in part to the presence of crustal magnetic fields.

Three Solar Irradiance Proxies for Aperture Photoelectron Detections in Top‐Hat ESAs Coated With Ebonol‐C

AbstractDecades after the introduction of top‐hat electrostatic analyzers (ESAs) to experimental space plasma physics, photoelectron noise in apertures of low‐energy electron instruments continues to be an issue in instrument performance. The photoelectrons at each portion of measured phase space distribution can be mapped through laboratory and in‐flight testing, and have been for many missions. In flight, the photoelectrons are a response to dynamic solar EUV spectral intensities which vary over the course of a solar cycle and even within a solar rotation. This paper outlines three solar irradiance proxies that can be used to scale a map of photo‐electron locations in phase space in order to account for dynamic solar activity. These proxies apply to top‐hat ESAs coated with Ebonol‐C (the most common coating) used by missions such as MMS, Solar Orbiter, the Van Allen Probes, and Juno. These proxies are discovered by searching possible wavelengths for correlation with an independent algorithm for MMS/FPI (Magnetospheric Multiscale Mission/Fast Plasma Instrument) which automatically determines the scaling factor. The three wavelengths that serve as viable proxies are found to be: 17.5, 30.5, and 37.5 nm.

Linking the Sun to the Heliosphere Using Composition Data and Modelling

Our understanding of the formation and evolution of the corona and the heliosphere is linked to our capability of properly interpreting the data from remote sensing and in-situ observations. In this respect, being able to correctly connect in-situ observations with their source regions on the Sun is the key for solving this problem. In this work we aim at testing a diagnostics method for this connectivity. This paper makes use of a coronal jet observed on 2010 August 2nd in active region 11092 as a test for our connectivity method. This combines solar EUV and in-situ data together with magnetic field extrapolation, large scale MHD modeling and FIP (First Ionization Potential) bias modeling to provide a global picture from the source region of the jet to its possible signatures at 1AU. Our data analysis reveals the presence of outflow areas near the jet which are within open magnetic flux regions and which present FIP bias consistent with the FIP model results. In our picture, one of these open areas is the candidate jet source. Using a back-mapping technique we identified the arrival time of this solar plasma at the ACE spacecraft. The in-situ data show signatures of changes in the plasma and magnetic field parameters, with FIP bias consistent with the possible passage of the jet material. Our results highlight the importance of remote sensing and in-situ coordinated observations as a key to solve the connectivity problem. We discuss our results in view of the recent Solar Orbiter launch which is currently providing such unique data.

Plasma densities, flow, and solar EUV flux at comet 67P

Context.During its two-year mission at comet 67P, Rosetta nearly continuously monitored the inner coma plasma environment for gas production rates varying over three orders of magnitude, at distances to the nucleus ranging from a few to a few hundred kilometres. To achieve the best possible measurements, cross-calibration of the plasma instruments is needed.Aims.Our goal is to provide a consistent plasma density dataset for the full mission, while in the process providing a statistical characterisation of the plasma in the inner coma and its evolution.Methods.We constructed physical models for two different methods to cross-calibrate the spacecraft potential and the ion current as measured by the Rosetta Langmuir probes (LAP) to the electron density as measured by the Mutual Impedance Probe (MIP). We also described the methods used to estimate spacecraft potential, and validated the results with the Ion Composition Analyser (ICA).Results.We retrieve a continuous plasma density dataset for the entire cometary mission with a much improved dynamical range compared to any plasma instrument alone and, at times, improve the temporal resolution from 0.24−0.74 Hz to 57.8 Hz. The physical model also yields, at a three-hour time resolution, ion flow speeds and a proxy for the solar EUV flux from the photoemission from the Langmuir probes.Conclusions.We report on two independent mission-wide estimates of the ion flow speed that are consistent with the bulk H2O+ion velocities as measured by the ICA. We find the ion flow to consistently be much faster than the neutral gas over the entire mission, lending further evidence that the ions are collisionally decoupled from the neutrals in the coma. Measurements of ion speeds from Rosetta are therefore not consistent with the assumptions made in previously published plasma density models of the comet 67P’s ionosphere at the start and end of the mission. Also, the measured EUV flux is perfectly consistent with independently derived values previously published from LAP and lends support for the conclusions drawn regarding an attenuation of solar EUV from a distant nanograin dust population, when the comet activity was high. The new density dataset is consistent with the existing MIP density dataset, but it facilitates plasma analysis on much shorter timescales, and it also covers long time periods where densities were too low to be measured by MIP.

Segmentation of Coronal Features to Understand the Solar EUV and UV Irradiance Variability III. Inclusion and Analysis of Bright Points

The study of solar irradiance variability is of great importance in heliophysics, Earth’s climate, and space weather applications. These studies require careful identifying, tracking and monitoring of features in the solar photosphere, chromosphere, and corona. Do coronal bright points contribute to the solar irradiance or its variability as input to the Earth atmosphere? We studied the variability of solar irradiance for a period of 10 years (May 2010 – June 2020) using the Large Yield Radiometer (LYRA), the Sun Watcher using APS and image Processing (SWAP) on board PROBA2, and the Atmospheric Imaging Assembly (AIA), and applied a linear model between the segmented features identified in the EUV images and the solar irradiance measured by LYRA. Based on EUV images from AIA, a spatial possibilistic clustering algorithm (SPoCA) is applied to identify coronal holes (CHs), and a morphological feature detection algorithm is applied to identify active regions (ARs), coronal bright points (BPs), and the quiet Sun (QS). The resulting segmentation maps were then applied on SWAP images, images of all AIA wavelengths, and parameters such as the intensity, fractional area, and contribution of ARs/CHs/BPs/QS features were computed and compared with LYRA irradiance measurements as a proxy for ultraviolet irradiation incident to the Earth atmosphere. We modeled the relation between the solar disk features (ARs, CHs, BPs, and QS) applied to EUV images against the solar irradiance as measured by LYRA and the F10.7 radio flux. A straightforward linear model was used and corresponding coefficients computed using a Bayesian method, indicating a strong influence of active regions to the EUV irradiance as measured at Earth’s atmosphere. It is concluded that the long- and short-term fluctuations of the active regions drive the EUV signal as measured at Earth’s atmosphere. A significant contribution from the bright points to the LYRA irradiance could not be found.

Thermospheric parameters’ long-term variations over the period including the 24/25 solar cycle minimum. Whether the CO2 increase effects are seen?

The CO2 concentration has been increasing for more than five decades reaching ~29 % at present with respect to the pre-industrial era. The largest CO2 cooling effects in the thermosphere are predicted for solar minimum conditions. A comparison of solar minima in 1954/1964 to the recent one in 2019 was used to check at the quantitative level the theoretical predictions and the validity of the CO2 cooling hypothesis. June monthly median noontime ionospheric observations at Moscow, Rome, and Slough/Chilton were used to infer neutral gas density ρ, exospheric temperature Tex, height of the F2-layer maximum hmF2, and total solar EUV flux for the (1954–2020) period. Solar and geomagnetic activity was shown to explain ~99 % of the whole variability in the retrieved neutral gas density and Tex during the (1958–2020) period resulting in statistically insignificant residual linear trends. A comparison of 1954/1964 to 2019 solar minima does not confirm the theoretically predicted decrease of ~21 % in ρ, ~15 K in Tex, and ~7 km in hmF2 related to a 29 % increase of the CO2 abundance. The main conclusion: despite continuous CO2 increase in the Earth's atmosphere long-term variations of thermospheric parameters are controlled by solar and geomagnetic activity.

Obtaining space-based SDO/AIA solar UV and EUV images from ground-based Hα observations by deep learning

In this work, we explore the mappings from solar images taken in Hα (6563 Å) by the Global Oscillation Network Group (GONG) on the ground to those observed in eight different wavelengths (94, 131, 171, 193, 211, 304, 335 and 1600 Å) by SDO/AIA in space. Eight mappings are built by training the conditional Generative Adversarial Networks (cGANs) on datasets with 500 paired images, which are [Hα, AIA94], [Hα, AIA131], [Hα, AIA171], [Hα, AIA193], [Hα, AIA211], [Hα, AIA304], [Hα, AIA335] and [Hα, AIA1600]. We evaluate the eight trained cGANs models on validation and test datasets with 154-pair images and 327-pair images, respectively. The model generated fake AIA images match the corresponding observed AIA images well on large-scale structures such as large active regions and prominences. But the small-scale flare loops and filament threads are difficult to reconstruct. Four quantitative comparisons are carried out on the validation and test datasets to score the mappings. We find that the model-generated images in 304 and 1600 Å match the corresponding observed images best. This exploration suggests that the cGANs are promising methods for mappings between ground-based Hα and space-based EUV/UV images, while some improvements are necessary.