Space Weather Indices Research Articles

Modeling of Nitric Oxide Infrared radiative flux in lower thermosphere: A machine learning perspective

Nitric Oxide (NO) significantly impacts energy distribution and chemical processes in the mesosphere and lower thermosphere (MLT). During geomagnetic storms, a substantial influx of energy in the thermosphere leads to an increase in NO infrared emissions. Accurately predicting the radiative flux of Nitric Oxide is crucial for understanding the thermospheric energy budget, particularly during extreme space weather events. With advancements in computational techniques, machine learning (ML) has become a highly effective tool for space weather forecasting. This effort becomes even more worthwhile considering the availability of two decades of continuous NO infrared emissions measurement by TIMED/SABER along with several other key thermospheric variables. We present the scheme of development of an ML-based predictive model for Nitric Oxide Infrared Radiative Flux (NOIRF). Various ML algorithms have been tested for better predictive ability, and an optimized model (NOEMLM) has been developed for the study of NOIRF. This model is able to extract the underlying relationships between the input features and effectively predict the NOIRF. The NOEMLM predictions have very good agreements with SABER observation during quiet time as well as geomagnetic storms. In comparison with the existing TIEGCM model, NOEMLM has very good performance, especially during extreme space weather conditions. The results of this study suggest that utilizing geomagnetic and space weather indices with ML/AI can serve as superior parameters for studying the upper atmosphere, as compared to focusing on specific species having complex chemical processes and associated uncertainties in constituents. ML techniques can effectively carry out the analysis with greater ease than traditional chemical studies.

A Multi-Input Convolutional Neural Networks Model for Earthquake Precursor Detection Based on Ionospheric Total Electron Content

Earthquakes occur all around the world, causing varying degrees of damage and destruction. Earthquakes are by their very nature a sudden phenomenon and predicting them with a precise time range is difficult. Some phenomena may be indicators of physical conditions favorable for large earthquakes (e.g., the ionospheric Total Electron Content (TEC)). The TEC is an important parameter used to detect pre-earthquake changes by measuring ionospheric disturbances and space weather indices, such as the global geomagnetic index (Kp), the storm duration distribution (Dst), the sunspot number (R), the geomagnetic storm index (Ap-index), the solar wind speed (Vsw), and the solar activity index (F10.7), have also been used to detect pre-earthquake ionospheric changes. In this study, the feasibility of the 6th-day earthquake prediction by the deep neural network technique using the previous five consecutive days is investigated. For this purpose, a two-staged approach is developed. In the first stage, various preprocessing steps, namely TEC signal improvement and time-frequency representation-based TEC image construction, are performed. In the second stage, a multi-input convolutional neural network (CNN) model is designed and trained in an end-to-end fashion. This multi-input CNN model has a total of six inputs, and five of the inputs are designed as 2D and the sixth is a 1D vector. The 2D inputs to the multi-input CNN model are TEC images and the vector input is concatenated space weather indices. The network branches with the 2D inputs contain convolution, batch normalization, and Rectified Linear Unit (ReLU) activation layers, and the branch with the 1D input contains a ReLU activation layer. The ReLU activation outputs of all the branches are flattened and then concatenated. And the classification is performed via fully connected, softmax, and classification layers, respectively. In the experimental work, earthquakes with a magnitude of Mw5.0 and above that occurred in Turkey between 2012 and 2019 are used as the dataset. The TEC data were recorded by the Turkey National Permanent GNSS Network-Active (TNPGN-Active) Global Navigation Satellite System (GNSS) stations. The TEC data five days before the earthquake were marked as “precursor days” and the TEC data five days after the earthquake were marked as “normal days”. In total, 75% of the dataset is used to train the proposed method and 25% of the dataset is used for testing. The classification accuracy, sensitivity, specificity, and F1-score values are obtained for performance evaluations. The results are promising, and an 89.31% classification accuracy is obtained.

Uncertainty Quantification for Machine Learning‐Based Ionosphere and Space Weather Forecasting: Ensemble, Bayesian Neural Network, and Quantile Gradient Boosting

AbstractMachine learning (ML) has been increasingly applied to space weather and ionosphere problems in recent years, with the goal of improving modeling and forecasting capabilities through a data‐driven modeling approach of nonlinear relationships. However, little work has been done to quantify the uncertainty of the results, lacking an indication of how confident and reliable the results of an ML system are. In this paper, we implement and analyze several uncertainty quantification approaches for an ML‐based model to forecast Vertical Total Electron Content (VTEC) 1‐day ahead and corresponding uncertainties with 95% confidence intervals (CI): (a) Super‐Ensemble of ML‐based VTEC models (SE), (b) Gradient Tree Boosting with quantile loss function (Quantile Gradient Boosting, QGB), (c) Bayesian neural network (BNN), and (d) BNN including data uncertainty (BNN + D). Techniques that consider only model parameter uncertainties (a and c) predict narrow CI and over‐optimistic results, whereas accounting for both model parameter and data uncertainties with the BNN + D approach leads to a wider CI and the most realistic uncertainties quantification of VTEC forecast. However, the BNN + D approach suffers from a high computational burden, while the QGB approach is the most computationally efficient solution with slightly less realistic uncertainties. The QGB CI are determined to a large extent from space weather indices, as revealed by the feature analysis. They exhibit variations related to daytime/nightime, solar irradiance, geomagnetic activity, and post‐sunset low‐latitude ionosphere enhancement.

Database development for intellectual system for research of space weather parameters

An analysis of the helio- and geo-activity subject area has been carried out, which become a ground for the main essences of space weather indices, their attributes and connections between them were determined. An ER-diagram was constructed and a logical scheme of the database of the intelligent system for the research of space weather parameters was developed. The scientific novelty of the obtained results relies on the development of a database model of an intelligent system for the research of space weather parameters. The practical significance of the obtained results lies in the possibilities of filling the database on manifestations of solar activity, their processing, analysis and establishment of connections between indicators of helio- and geoactivity.

Possible connection between solar activity and local seismicity

Recently, much attention has been paid to the relationship between solar and seismic activities toward earthquake (EQ) prediction. Some researchers believe in the existence of a connection between them; however, others completely refuse the existence of such a connection. In this study, the correlation between solar disturbances and occurrence of EQs during two consecutive solar cycles (SCs) 23 & 24 from 1996 to 2019 was investigated to explore such a relationship. The study was performed on both global and local scales. On a global scale, we studied the temporal variations of EQs number and the corresponding solar activity, represented by sunspot number. On the other hand, we selected several seismic zones characterized with high seismic activities and shallow depth EQs. For each zone we examined the day-to-day variations in the number of EQs and explored the space weather “chain of action” from the Sun to Earth in order to examine whether these events have an influence to increase the number of EQs or not. Results showed that, for the whole global seismicity, no clear correlation is found between EQs occurrence and solar activity, while for small active seismic zones, the connection between them is significant. An increase in the number of shallow EQs in the studied seismic zones is observed in association with the variations of examined space weather indices. Thus, the current study suggests a possible connection between solar activity and localized seismic activities. However, the solar-magnetosphere-lithosphere coupling and interaction during solar events need further study and investigation for a better understanding of their EQ triggering effects.

Observations of Ionospheric Clutter at Near Equatorial High Frequency Radar Stations

The temporal variation of received clutter and noise at a pair of oceanographic high frequency radars (HFR) operating near the geomagnetic equator in the Republic of Palau is investigated. Oceanographic HFRs process range-gated Doppler spectra from groundwave signals that are backscattered from the ocean’s surface to derive maps of ocean currents. The range performance of the radars exhibited a regular diurnal signal which is determined to be a result of both ionospheric clutter and noise. The increased Clutter plus Noise Floor (C+NF) decreases the Signal to Clutter plus Noise Ratio (SCNR) which, in turn, reduces the range and quality of ocean surface current measurement. Determining the nature and origin of this degradation is critical to QA/QC of existing HFR deployments as well as performance predictions of future installations. Nighttime impacts are most severe and negatively affect ocean surface current measurements as low SCNR is found to extend across the Doppler spectra at all ranges, challenging the ability of HFR to map the ocean surface current. Daytime degradation is less severe and presents itself in a way consistent with independent observations of ionospheric clutter, specifically the diurnal temporal pattern and range where the C+NF features occur. A timeseries analysis of SCNR and C+NF is pursued to understand this relationship using received range-dependent Doppler spectra and C+NF features using image segmentation techniques. Clutter plus noise features are classified into daytime, nighttime, and no-noise feature types. The diurnal structure and variability of these features are examined, and the occurrences of each feature type are calculated. The occurrences are compared with space weather indices including a measure of geomagnetic activity, namely the EE (Equatorial Electro Jet) index (determined from magnetometers measuring the earth’s magnetic field), as well as solar impacts using the F10.7 solar radio clutter index to assess the relationship of ionospheric conditions with HFR ocean surface current measurement.

Newcomb–Benford Law as a generic flag for changes in the derivation of long-term solar terrestrial physics timeseries

Abstract The Newcomb–Benford Law (NBL) prescribes the probability distribution of the first digit of variables which explore a broad range under conditions including aggregation. Long-term space weather relevant observations and indices necessarily incorporate changes in the contributing number and types of observing instrumentation over time and we find that this can be detected solely by comparison with the NBL. It detects when upstream solar wind magnetic field high resolution OMNI interplanetary magnetic field incorporated new data from the WIND and Advanced Composition Explorer spacecraft after 1995. NBL comparison can detect underlying changes in the geomagnetic auroral electrojet index (activity-dependent background subtraction) and the SuperMAG electrojet index (different station types) that select individual stations showing the largest deflection, but not where station data are averaged, as in the SuperMAG ring-current index. As composite indices become more widespread across the geosciences, the NBL may provide a generic, data processing-independent flag indicating changes in the constituent raw data, calibration, or sampling method.

Auroral Oval Boundary Dynamics on the Nature of Geomagnetic Storm

During emergency events, we could significantly depend on the stable operation of radio communication, navigation, and radars. The ionosphere, especially its auroral regions, significantly influences radio systems, which is why scientists and engineers create systems to monitor these regions. Using data from the global GNSS network, we analyzed the 10 strongest magnetic storms of solar cycle 24: five coronal mass ejection-driven (CME-driven) and five high-speed stream-driven (HSS-driven) storms. The analysis was based on the calculation of the standard deviation of the total electron content (TEC) derivative (rate of TEC index, ROTI). Under all the storms, the ROTI featured similar dynamics: the average ROTI reaches the highest values during the main phase, and the higher the intensity is, the more intense and equatorward the average ROTI registered. The highest cross-correlations are observed with a lag of 1 h, between the IMF z-component Bz and the magnetic latitude where the highest ROTI values appear. The auroral electrojet (SME index) shows the highest impact on the ROTI dynamics. An increase in the space weather indices (in absolute value) is accompanied by a decrease in the latitude where the maximal ROTI occurs. We found that the peculiarities of a storm affect the ROTI dynamics: all the CME-driven storms feature a high cross-correlation (>0.75) between the IMF z-component Bz and the magnetic latitude where the highest ROTI appears, while the HSS-driven storms feature a lower cross-correlation (<0.75) between them. The difference in duration of similar (by maximal values of geomagnetic indices) HSS- and CME-driven storms could produce differences in the highest ROTI values. Correlations show that compared to HSS-driven storms, CME-driven ones more directly impact the ROTI values and locations of regions with a high ROTI.

The Possible Effect of Space Weather Factors on Various Physiological Systems of the Human Organism

A systematic review of heliobiological studies of the last 25 years devoted to the study of the potential influence of space weather factors on human health and well-being was carried out. We proposed three criteria (coordinates), according to which the work on solar–biospheric relations was systematized: the time scale of data sampling (years, days, hours, minutes); the level of organization of the biological system under study (population, group, individual, body system); and the degree of system response (norm, adaptation, failure of adaptation (illness), disaster (death)). This systematic review demonstrates that three parameters mentioned above are closely related in the existing heliobiological studies: the larger the selected time scale, the higher the level of estimated biological system organization and the stronger the potential response degree is. The long-term studies are devoted to the possible influence of solar activity on population disasters, i.e., significant increases in morbidity and mortality. On a daily scale, a probable effect of geomagnetic storms and other space weather events on short-term local outbreaks of morbidity is shown as well as on cases of deterioration in people functional state. On an intraday scale, in the regular functioning mode, the heart and brain rhythms of healthy people turn to be synchronized with geomagnetic field variations in some frequency ranges, which apparently is the necessary organism’s existence element. The applicability of different space weather indices at different data sampling rates, the need to take into account the contribution of meteorological factors, and the prospects for an individual approach in heliobiology are discussed. The modern important results of experiments on modeling the action of magnetic storms in laboratory conditions and the substantiation of possible theoreical mechanisms are described. These results provide an experimental and theoretical basis for studies of possible connections of space weather and human health.

Data‐Driven Modeling of Atomic Oxygen Airglow over a Period of Three Solar Cycles

AbstractThe Earth's upper atmosphere is a dynamic environment that is continuously affected by space weather from above and atmospheric processes from below. An effective way to observe this interface region is the monitoring of airglow. Since the 1950s, airglow emissions have been systematically measured by ground‐based photometers in specific wavelength bands during the nighttime. The availability of the calibrated data from over 30 years of photometric airglow measurements at Abastumani in Georgia (41.75° N, 42.82° E), at wavelengths of 557.7 and 630.0 nm, enable us to investigate if a data‐driven model based on advanced machine learning techniques can be successfully employed for modeling airglow intensities. A regression task was performed using the time series of space weather indices and thermosphere‐ionosphere parameters. We have found that the developed data‐driven model has good consistency with the commonly used GLOW airglow model and also captures airglow variations caused by cycles of solar activity and changes of the seasons. This enables us to visualize the green and red airglow variations over a period of three solar cycles with a one‐hour time resolution.

The relevance of local magnetic records when using extreme space weather events as benchmarks

Space weather indices introduced for scientific purposes are commonly used to quantify operational nowcast of the geospace state during extreme space weather events. Some indices, such as the Disturbance storm time (Dst) index, have been applied to situations for which they are not originally intended. This raises a question about suitability as a space weather benchmark. In analysing historical records for different magnetometers at low- and mid-latitude, we find periods with longitudinal asymmetry in magnetic response that suggest important signals from individual magnetometers are being averaged out of the Dst record. This asymmetry develops as a double spike in the H-component: one negative in the observatories in the day sector and one positive in the observatories in the night sector. These spikes develop in short-time (about 2 h) and pose a potential hazardous effect for users affected by space weather. The results from historical events have been reinforced with the systematic study of magnetic records during extreme events (Dst ≤ −200 nT and AL ≤ −2000 nT) in the period 1998–2017 from six magnetic observatories at about 40° magnetic latitude. Moreover, we show that the largest asymmetries take place during the early main phase and are recorded in narrow local time sectors. An important outcome of these results is that space weather benchmarks should be based on local records instead of the commonly used global indices. This action improves two important aspects of space weather: the assessment of historical extreme events and that of the needs of users.

Ionospheric anomalies associated with the <i>M</i><sub>w</sub> 7.3 Iran–Iraq border earthquake and a moderate magnetic storm

Abstract. The analysis of the unexpected ionospheric phases before large earthquakes is one of the cutting-edge issues in earthquake prediction studies. In this study, the total electron content (TEC) data from seven International GNSS Service (IGS) stations and the global ionosphere maps (GIMs) were used. Short-time Fourier transform (STFT) and a running median process were applied to the TEC time series to detect abnormalities before the Mw 7.3 Iran–Iraq border earthquake on 12 November 2017. The analyses showed positive anomalies 8–9 d before the earthquake and some positive and negative anomalies 1–6 d before the earthquake. These anomalies were cross-checked using the Kp, Dst, F10.7, Bz component of the interplanetary magnetic field (IMF Bz), electric field (Ey), and plasma speed (VSW) space weather indices. The results showed that the anomalies 1–6 d before the earthquake were caused by a moderate magnetic storm. Moreover, the positive anomalies 8–9 d before the earthquake were likely related to the Iran–Iraq border earthquake due to quiet space weather, local dispersion, and the proximity to the epicenter.

The Space Weather Atmosphere Models and Indices (SWAMI) project: Overview and first results

Space weather driven atmospheric density variations affect low Earth orbit (LEO) satellites during all phases of their operational lifetime. Rocket launches, re-entry events and space debris are also similarly affected. A better understanding of space weather processes and their impact on atmospheric density is thus critical for satellite operations as well as for safety issues. The Horizon 2020 project Space Weather Atmosphere Model and Indices (SWAMI) project, which started in January 2018, aims to enhance this understanding by:Developing improved neutral atmosphere and thermosphere models, and combining these models to produce a new whole atmosphere model.Developing new geomagnetic activity indices with higher time cadence to enable better representation of thermospheric variability in the models, and improving the forecast of these indices.The project stands out by providing an integrated approach to the satellite neutral environment, in which the main space weather drivers are addressed together with model improvement. The outcomes of SWAMI will provide a pathway to improved space weather services as the project will not only address the science issues, but also the transition of models into operational services.The project aims to develop a unique new whole atmosphere model, by extending and blending the Unified Model (UM), which is the Met Office weather and climate model, and the Drag Temperature Model (DTM), which is a semi-empirical model which covers the 120–1500 km altitude range. A user-focused operational tool for satellite applications shall be developed based on this. In addition, improved geomagnetic indices shall be developed and shall be used in the UM and DTM for enhanced nowcast and forecast capability.In this paper, we report on progress with SWAMI to date. The UM has been extended from its original upper boundary of 85 km to run stably and accurately with a 135 km lid. Developments to the UM radiation scheme to enable accurate performance in the mesosphere and lower thermosphere are described. These include addition of non-local thermodynamic equilibrium effects and extension to include the far ultraviolet and extreme ultraviolet. DTM has been re-developed using a more accurate neutral density observation database than has been used in the past. In addition, we describe an algorithm to develop a new version of DTM driven by geomagnetic indices with a 60 minute cadence (denoted Hp60) rather than 3-hourlyKpindices (and corresponding ap indices). The development of the Hp60 index, and the Hp30 and Hp90 indices, which are similar to Hp60 but with 30 minute and 90 minute cadences, respectively, is described, as is the development and testing of neural network and other machine learning methods applied to the forecast of geomagnetic indices.

Development of New Capabilities Using Machine Learning for Space Weather Prediction

With the development of space exploration and space environment measurements, the numerous observations of solar, solar wind, and near Earth space environment have been obtained in last 20 years. The accumulation of multiple data makes it possible to better use machine learning technique, which has achieved unforeseen results in industrial applications in last decades, for developing new approaches and models in space weather investigation and prediction. In this paper, the efforts on the forecasting methods for space weather indices, events, and parameters using machine learning are briefly introduced based on the study works in recent years. These investigations indicate that machine learning, especially deep learning technique can be used in automatic characteristic identification, solar eruption prediction, space weather forecasting for solar and geomagnetic indices, and modeling of space environment parameters.

Correlation between Ionospheric Spatial Decorrelation and Space Weather Intensity for Safety-Critical Differential GNSS Systems.

An ionospheric spatial decorrelation is one of the most dominant error factors that affects the availability of safety-critical differential global navigation satellite systems (DGNSS). This is because systems apply significant conservatism on the error source when ensuring navigation safety due to its unpredictable error characteristic. This paper investigates a correlation between GNSS-derived ionospheric spatial decorrelation and space weather intensity. The understanding of the correlation has significant advantages when modeling residual ionospheric errors without being overly pessimistic by exploiting external sources of space weather information. An ionospheric spatial decorrelation is quantified with a parameter of spatial gradient, which is an ionosphere total electron content (TEC) difference per unit distance of ionospheric pierce point (IPP). We used all pairs of stations from dense GNSS networks in the conterminous United States (CONUS) that provide an IPP separation distance of less than 100 km to obtain spatial gradient measurements under both ionospherically quiet and active conditions. Since the correlation results would be applied to safety-critical navigation applications, special attention was paid by taking into consideration all non-Gaussian tails of a spatial gradient distribution when determining spatial gradient statistics. The statistics were compared with space weather indices which are disturbance storm time (Dst) index and interplanetary magnetic field (IMF) Bz index. As a result, the ionospheric spatial decorrelation showed a significant positive correlation with both indices, especially under active ionospheric conditions. Under quiet conditions, it showed positive correlation slightly weaker than those under active conditions, and the IMF Bz showed preceding response to the spatial gradient statistics revealing the potential applicability for predicting the spatial decorrelation conditions.

On the detection of anomalous seismo-ionospheric behavior in the presence of space weather stimuli for large earthquakes

Anomalous behavior of ionospheric total electron content (TEC) prior to earthquake has been observed in many studies. Evidence of such seismo-ionospheric coupling effects suggests that it is plausible to rely on TEC signatures for early earthquake warning. However, the detection of pre-earthquake TEC anomalies (PETA) has not been adopted in practice due to two pertinent issues. Firstly, the effects of space weather activity can affect TEC levels and cause anomalous behavior in the TEC. Usually arbitrary thresholds are set for space weather indices to eliminate TEC anomaly due to space weather effects. Secondly, the choice regarding moving time-window length used to characterise background variation of TEC within the statistical envelope approach has an effect on detection of PETA. While the rule-of-thumb in selecting the moving window length is to have a time window capable of capturing background variability and short-term fluctuations, the length of the time window used in the literature varies with little justification. In this study, a critical examination is conducted on the statistical envelope approach and in particular, to eliminate the effect of space weather activity without the use of arbitrary space indices to detect PETA. A two-part PETA identification procedure is proposed, consisting of wavelet analyses isolating non-earthquake TEC contributions, followed by the statistical envelope method using a moving window length standardized based on observed periodicities and statistical implications is suggested. The approach is tested on a database of 30 large earthquakes (M ≥ 7.0). The proposed procedure shows that PETA can be detected prior to earthquakes at higher confidence levels without the need to separately check for space weather activity. More importantly, the procedure was able to detect PETA for studies where it was previously reported that PETA could not be detected or detected convincingly.

Real-Time Ionospheric Threat Adaptation Using a Space Weather Prediction for GNSS-Based Aircraft Landing Systems

The use of ground-based augmentation systems (GBASs) is increasing in the national airspace system and also in many nations to support aircraft precision approaches and landing. An anomalous ionospheric event if undetected can cause a potential threat to users of single-frequency-based global navigation satellite system augmentation systems. Current GBAS utilize the pre-defined “worst case” ionospheric threat model in their computation of user position errors to consider all possible ionospheric conditions. This could lead to an excessive availability penalty by adding conservatism on the resulting error bounds. This paper proposes a methodology of real-time ionospheric threat adaptation that adjusts the ionospheric threat model in real time instead of always using the same threat model. This is done by using predicted values of space weather indices for determining the corresponding threat model based on an established relationship between space weather indices and ionospheric threats. Since space weather prediction itself is not reliable due to prediction errors, an uncertainty model was derived from 17 years of historical data. When applied to Category I GBAS in the Conterminous United States, this method lowered the upper bound of the current threat model about 95% of the time during the 17 years (from 1995 to 2011) using the bounded prediction value of the disturbance-storm time index.

Operational Space Weather Services in National Space Science Center of Chinese Academy of Sciences

Operational Space Weather Services in National Space Science Center of Chinese Academy of Sciences

Time of Conception and Birth-Relation of “Big Killers” Patients and Longevity

In recent years some interrelationships between time of birth and longevity were published. Concomitant publications appeared demonstrating links between Space Weather and the timing of medical events; in part of both these studies it was shown that Space Weather indices are stronger connected with the time of conception as with the birth time. The aim of these study was to consider birth and conception month of patients suffering from a number of “big killer” pathologies, affecting human longevity and comparing with published data on centenarians—100 - 112 years old persons in the USA. Patients and Methods: We included monthly births distribution of our four papers including patients with Acute Myocardial Infarction undergoing Percutaneous Coronary Intervention (PCI)—n-3765, and admitted in another tertiary medical facility for AMI—n-22,047, and, also patients suffering Rapid—n-1239 and Sudden Cardiac Death—n-327 and, also, patients with oncologic malignancies—n-44587. At all in this group 71,965 patients were included. Their birth and conception months were compared with analogical data of 1574 people of 100 - 112 years old using data of L.A. and N.S. Gavrilov for USA centenarians. Results: The birth months of the cardiovascular and oncology patients were maximal in January, March and April (above 10% at each of these months); their conception was maximal in April, June and July. The monthly distribution of conception and birth of the studied population is presented in Tables 1-3. The maximal birth months of the centenarians were November, September, and October; the maximal conception months were December, January, and February. Conclusion: People suffering Myocardial Infarction, Sudden Cardiac Death and Oncologic Malignancies are in higher numbers born in the first four months of the year and conception in April, June and July. The maximal birth months of the very old people were November, September and October and conception were December, January and February.

Environmental surrounding and perception of speech

A longitudinal experiment with 26 respondents was conducted to study emotional and color images of 12 men names and 6 vowels. The collected data was studied in association with environmental conditions: the time of the day, local and space weather indices. Most of the studied indices showed significant and valid changes related to the time of the day at p≤0.05÷0.001. Multiple correlations were revealed for the studied indices and environmental parameters at p≤0.05÷0.001.