Sunspot Activity Research Articles

Unraveling the variability and geoclimatic drivers of daily precipitation concentration over a Western Himalayan Province of India.

A sound understanding of daily precipitation concentration provides critical insights about extreme precipitation and associated hazards such as rainstorms, flash floods, cloud bursts, and soil erosion. Therefore, this study examines the spatiotemporal variability and geoclimatic drivers of daily precipitation concentration using the concentration index (CI), precipitation concentration degree (PCD), Q Index (QI), and Theil index (TI) over a Western Himalayan Province, namely Himachal Pradesh, India. For this, the daily precipitation data pertaining to 111 grids (0.25° × 0.25° resolution), encompassing entire province, have been downloaded from India Meteorological Department (IMD), Pune, for the period 1951 to 2020. The trends in concentration indices have been detected using Mann-Kendall (MK) and Sen's slope methods, whereas their relationship with geoclimatic drivers have been assessed using Pearson's correlation coefficient (physiographic) and random forest regression model (climatic) at annual and monsoon season time scales. The results have revealed a high correlation between CI-TI (> 0.95), followed by QI-TI (> 0.84), QI-PCD (> 0.80), TI-PCD (> 0.78), QI-CI (> 0.65), and CI-PCD (> 0.60), representing nearly similar precipitation concentration distribution. Among the concentration indices, QI has shown maximum sensitivity to long-term changes followed by TI, CI, and PCD at both time scales. Similarly, physiographic factors have maximum effect on QI, showing its greatest sensitivity to latitude, longitude and elevation. While considering the impact of climatic drivers, Atlantic Meridian Oscillation (AMO) and Sunspot activity have exerted maximum influence on annual variability of CI, PCD, QI, and TI, whereas monsoon variability of these indices has been modulated by Pacific Decadal Oscillation (PDO), especially over northern parts of Himachal Pradesh. These findings will assist soil and water resource managers, disaster executives, agricultural stakeholders, and urban planners in understanding the unexplored aspects of daily precipitation concentration and making informed decisions over the mountainous regions of western Himalaya.

Prediction of Sunspot Activity Based on Differential Evolution Algorithm and BP Neural Network Model

In this research report, the prediction method of sunspot activity is deeply discussed, and a variety of statistical and data science models are used to make comprehensive prediction. Research focuses include the start time and duration of the solar maximum in the solar cycle, as well as the prediction of the number and area of sunspots. By capturing the periodicity of solar activity, Pearson correlation coefficient is used to analyze the relationship between the maximum solar activity and the number of sunspots, and the adaptive multivariate nonlinear regression-BP neural network model and particle swarm optimization BP neural network are combined to make high-precision prediction. The research results provide a scientific basis for the prediction of space weather, ionospheric state and communication system reliability.

Climate Response and Radial Growth Dynamics of Pedunculate Oak (Quercus robur L.) Plus Trees and Their Half-Sib Progeny in Periods of Severe Droughts in the Forest-Steppe Zone of Eastern Europe.

The dendrochronological parameters of 97 pedunculate oak (Quercus robur L.) trees including 20 plus trees (142-year-old on average) and four half-sib families for four of them were analyzed considering also specifically years of the most severe droughts that were identified using average monthly air temperature and precipitation data. The tree-ring width (TRW) was mostly affected by air temperature that had the largest cross-dating indices (CDI), up to 78% maximum. However, the 32-year Brückner-Egeson-Lockyer cycle (a climatic cycle of approximately 30-40 years that correlates with sunspot activity) was more reflected in the TRW dynamics in plus trees than precipitation and air temperature. A high-frequency of abnormal TRW was clearly observed during drought periods and in the following 2-3 years. Tree radial-growth reduction due to drought stress varied significantly between families. The resistance to drought based on TRW was higher in the maternal plus oak trees than in progeny. Drought resulted in reduced growth during the subsequent year(s); hence, the minimum growth occurred after the actual climate event. Autumn-winter precipitation and weather conditions were of the greatest importance at the onset of active vegetation in April and May. The influence of air temperature on oak growth was the largest in March (r = 0.39, p < 0.05). The strongest positive correlation between precipitation and growth (with r up to 0.38) was observed in May 2023. Plus trees had a high adaptive potential due to the stability of radial growth during drought with high resistance (Rt = 1.29) and resilience (Rs = 1.09) indexes. The offspring of families 1 (Rt = 0.89, Rs = 0.89) and 2 (Rt = 1.04, Rs = 0.87) had similar resistance and resilience, but the recovery indices (Rc) for offspring in families 1, 2 and 3 exceeded the recovery values for plus trees. For offspring in families 3 and 4, the index values were lower. The revealed responses of wood growth of plus trees to climatic parameters estimated as resistance (Rt), resilience (Rs) and recovery (Rc) indexes and similar responses in their progeny can be used in breeding pedunculate oak for wood growth productivity and drought resistance.

A correlation between sunspot observations and solar Ca ii H&amp;K activity proxies

ABSTRACT The magnetic phenomena on the solar surface have been the subject of several investigations over the last 400 yr. An early indicator of solar magnetic activity was sunspot counting. Currently, the main sunspot indicators are the international sunspot number, the sunspot group number, the total sunspot area, and the photometric sunspot index. Several improvements in observational techniques have allowed magnetic activity to be measured using solar/stellar spectra. Standard spectroscopic activity indicators are the $S_{\rm MW}$ index, based on the Ca ii H&amp;K emission lines, and the chromospheric component $R^{\prime }_{\rm HK}$ index. In this context, we present a correlation between sunspot observations and solar Ca ii H&amp;K activity proxies. We present our comparisons between the spectroscopic chromospheric activity proxies ($S_{\rm MW}$ and $R^{\prime }_{\rm HK}$) and the sunspot indicators over the last decades, using solar measurements (spectroscopic and spot proxy) performed on the same day. In general, our results indicate a linear fit between the chromospheric proxies and sunspot indicators. In addition, using the long-term sunspot group number records, we estimate an average spectroscopic proxy along the solar Maunder minimum (MM) phase, corresponding to $\mathinner {\langle {S_{\rm MW}}\rangle } = 0.167 \pm 0.013$ and $\mathinner {\log \langle {R^{\prime }_{\rm HK}}\rangle } = -4.913 \pm 0.363$. The estimated variability is $\sigma _{S_{\rm MW}} = 1.137 \times 10^{-7}$ and $\sigma _{\log R^{\prime }_{\rm HK}} = 2.704 \times 10^{-6}$. Our linear regression analysis, applied annually, suggests that the variability level of the chromospheric activity in the MM phase is significantly lower than in the normal period of activity and that this could be the result of linear regression on annually averaged data, combined with minimal sunspot activity during the solar MM phase. Further observations of MM analogues will be needed to test this hypothesis.

Statistical Study of the Occurrence of Coronal Holes in the Solar Corona During Solar Cycle 24

In this article we look at: (a) the occurrence of coronal holes as a function of the different phases of solar cycle 24, (b) the variability of the annual number of coronal holes from 2009 to 2019, (c) the distribution of polar and non-polar coronal holes, irrespective of the phase but also as a function of the different phases of the cycle. From the phase minimum to the descending phase of solar cycle 24, the occurrences of coronal holes observed on the solar disc are 38%, 24%, 9% and 29% respectively. The annual number of coronal holes observed on the solar disc does not always follow the expected evolution of the two components of the solar magnetic field and the annual number of sunspots. Regardless of the phase of the solar cycle, the majority of coronal holes, around 65%, are located in the polar regions. During the early and late phases of the solar cycle, polar coronal holes predominate, whereas during the ascending phase and phase maximum, non-polar coronal holes predominate. The number of polar coronal holes is highest during the minimum phase of the solar cycle and lowest during the maximum phase. During the phases of the solar cycle, coronal hole activity appears to be in phase opposition with sunspot activity. There is a fairly good correlation between variations in the number of polar coronal holes and the expected evolution of the poloidal component of the solar magnetic field.

Fifty years of studying the GCR intensity during inversion of the heliospheric magnetic fields. II. HMF inversion on the inner heliospheric boundary

Phenomena in the outer layer of the solar atmosphere, the heliosphere, including the supersonic solar wind, the heliospheric magnetic field (HMF) carried by it, and cosmic rays propagating in the heliosphere are important for many processes occurring in this layer. For some of these processes such as geomagnetic activity or propagation of cosmic rays, not only the strength, but also the direction of the field is significant. Nonetheless, if in this regard the situation during periods of low sunspot activity is quite clear — the heliosphere is divided into two hemispheres with opposite polarity (toward the Sun/away from the Sun), — during periods of high sunspot activity when the HMF inversion occurs, there is no simple model of this phenomenon. The paper is a sequel to the study of the HMF inversion phenomenon and associated effects in the intensity of galactic cosmic rays (GCR). Previously, general ideas about the 22-year cyclicity in the characteristics of the Sun, heliosphere, and cosmic rays have been formulated, and the effects observed in the GCR intensity, which we associate with the HMF inversion, have been discussed in detail. This paper deals with a model of HMF inversion, associated only with the evolution of the magnetic field in the layer between the photosphere and the base of the heliosphere due to changes in the distribution of photospheric fields from one solar rotation to the next one, and shows that this is not enough to explain the main effects in the GCR intensity. In this layer, the magnetic field is the main energy factor. A more complete model of HMF inversion, including the transformation of its characteristics due to the interaction of different-speed solar wind streams in the heliosphere itself, where the solar wind is the main energy factor, will be discussed in the next paper.

Long-term geomagnetic activities and stratospheric winter temperature

In this research, impact of long term solar forcing on stratospheric winter temperature is checked. The 11- year sunspot activity and geomagnetic indices (AE, Kp, Dst) are used as an indicator for solar forcing, as geomagnetic activity indices show good correlation with solar variability. To understand the impact of solar forcing through high latitude, stratospheric winter (November to March) time North Polar Region (60N–90N) temperature anomalies are considered. The findings showed that temperature changes in the stratosphere are significantly correlated with solar activity, as evidenced by a significant positive correlation between the 11-year moving mean of stratospheric (10 hPa) temperature anomalies and sunspot number. Approximately from 1970 to 2000, the North Polar Region saw positive anomalous stratospheric winter temperatures. During the same time, the geomagnetic activity also showed a substantial increase. The year-to-year correlation between stratospheric pole temperature and geomagnetic activity is significant (about 0.5). The Empirical Mode Decomposition analysis reveals a highly significant correlation (around 0.9) between the long-term component of stratospheric winter temperature (IMF-4) and the long-term component of geomagnetic activity (IMF-3 and IMF-4). One of the reasons for the increase in lower stratospheric temperature is an increase in ozone concentration during the same period when geomagnetic activity is higher. Empirical orthogonal function (EOF) and correlation analysis of stratospheric winter temperature with large-scale circulation patterns are also carried out. The spatial correlation is checked for stratospheric winter temperature at North Pole and lower atmospheric levels (250 hPa and 850 hPa) followed by pre-monsoon and monsoon season. This study includes statistical analysis, however, also highlights the necessity of in-depth dynamical analysis to improve our understanding of how solar activity impacts Earth's atmospheric layers, which may be helpful in predicting the weather and climate.

Пятьдесят лет исследования поведения интенсивности ГКЛ в периоды инверсии гелиосферного магнитного поля. II. Инверсия ГМП на внутренней границе гелиосферы

Phenomena in the outer layer of the solar atmosphere, the heliosphere, including the supersonic solar wind, the heliospheric magnetic field (HMF) carried by it, and cosmic rays propagating in the heliosphere are important for many processes occurring in this layer. For some of these processes such as geomagnetic activity or propagation of cosmic rays, not only the strength, but also the direction of the field is significant. Nonetheless, if in this regard the situation during periods of low sunspot activity is quite clear — the heliosphere is divided into two hemispheres with opposite polarity (toward the Sun/away from the Sun), — during periods of high sunspot activity when the HMF inversion occurs, there is no simple model of this phenomenon. The paper is a sequel to the study of the HMF inversion phenomenon and associated effects in the intensity of galactic cosmic rays (GCR). Previously, general ideas about the 22-year cyclicity in the characteristics of the Sun, heliosphere, and cosmic rays have been formulated, and the effects observed in the GCR intensity, which we associate with the HMF inversion, have been discussed in detail. This paper deals with a model of HMF inversion, associated only with the evolution of the magnetic field in the layer between the photosphere and the base of the heliosphere due to changes in the distribution of photospheric fields from one solar rotation to the next one, and shows that this is not enough to explain the main effects in the GCR intensity. In this layer, the magnetic field is the main energy factor. A more complete model of HMF inversion, including the transformation of its characteristics due to the interaction of different-speed solar wind streams in the heliosphere itself, where the solar wind is the main energy factor, will be discussed in the next paper.

Global Effect of New Active Regions on Coronal Holes and Their Wind Streams

Solar wind prediction algorithms and simulations of coronal events often employ photospheric field maps that are assembled over a 27 day solar rotation. This has stimulated efforts to update and better synchronize the maps by applying flux transport and including observations of the back side of the Sun. Here, using potential-field source-surface extrapolations, we address the question of how the emergence of a large active region (AR) on the Sun’s farside affects the coronal field and configuration of coronal holes on the Earth-facing side. We find that, if the new AR is located ∼135°–180° in longitude from Earth, the effect on the coronal field and solar wind near the central meridian will be almost negligible. This is because, when sunspot activity is relatively low, the outermost AR loops will become connected to the nearby polar fields; when sunspot activity is high, the newly emerged flux will connect to neighboring ARs. However, large ARs that emerge near the solar limb may sometimes have a significant effect on the field near the central meridian. In particular, a coronal hole having opposite polarity to that of the nearest sector of the AR may partially close down, resulting in slower wind; conversely, if the coronal hole has the same polarity as the facing AR sector, it will tend to increase in areal size, resulting in faster wind. In most cases, the main effect of a new AR will be to redistribute open flux between itself and neighboring coronal holes (including the polar holes) through interchange reconnection.

Machine learning models for predicting geomagnetic storms across five solar cycles using Dst index and heliospheric variables

This study aims to improve the understanding of geomagnetic storms by utilizing machine learning models and analyzing several heliophysical variables, such as the interplanetary magnetic field, proton density, solar wind speed, and proton temperature. Rather than relying on traditional correlation-based methods, we employ advanced machine learning techniques to examine the complex relationships between these factors and geomagnetic storms. Our analysis covers a large dataset spanning six solar cycles, including the current 25th cycle, to provide comprehensive insights into the dynamics of these storms.Our study highlights the significance of the interplanetary magnetic field as a key predictor of geomagnetic storms, challenging previous beliefs that primarily focused on sunspot activity. By using high-resolution data, we uncover new patterns and provide a more detailed analysis of the factors influencing geomagnetic storms. We emphasize the importance of considering a range of heliophysical variables, such as proton temperature and flow pressure, which offer new insights into the complex dynamics driving these storm events.The application of machine learning models, particularly Random Forest and Gradient Boosting, demonstrated superior predictive accuracy compared to traditional methods. Our results reveal that the Dst-index MIN, scalar B, and alpha/proton ratio are among the most influential factors, accounting for a significant portion of the prediction model’s accuracy. These findings underscore the utility of machine learning in identifying critical drivers of geomagnetic activity and enhancing forecast precision.Additionally, our research underscores the need for comprehensive models that can accurately predict geomagnetic storms by integrating various data sources. This machine learning approach not only improves predictive accuracy but also enhances our understanding of the underlying mechanisms of space weather. The insights gained from this study have important implications for both scientific research and practical applications, such as improving early warning systems for geomagnetic storms and mitigating their potential impacts on Earth.

Reconstructing 273 Years of Potential Groundwater Recharge Dynamics in a Near-Humid Monsoon Loess Unsaturated Zone Using Chloride Profiling

Understanding the historical groundwater recharge process and its influencing factors is crucial for effectively managing regional groundwater resources amidst future climate change. However, the availability of high-resolution hydroclimate archives remains severely limited. In this study, we used a 59 m chloride profile within the unsaturated loess zone to reconstruct the potential groundwater recharge (PGR) records spanning 273 years in a near-humid area on the Loess Plateau. Spectral analysis was employed to identify the principal influencing factors on PGR across various time scales. The reconstructed hydrological records revealed three wet periods and four dry periods from 1745 to 2007 AD, with PGR rates ranging from 66.7 to 222.4 mm yr−1 during wet periods and 20.0 to 66.7 mm yr−1 during dry periods. In addition, spectral analysis indicated multiple cycles, ranging from 2.1 to 50.0 years, within the PGR history. Temperature, precipitation, and sunspot activity emerged as the key factors governing the rate of PGR over the 3-year, 7-year, and 11-year time scales, respectively, highlighting the combined influence of solar activity and climate on the PGR process. These findings enhance our understanding of groundwater recharge and environmental climate dynamics in the near-humid loess unsaturated zone and other regions exhibiting similar hydroclimatic conditions.

The Interconnection between Climate Cycles and Geohazards in Urban Areas of the Tourist Island of Mallorca, Spain

The island of Mallorca has experienced major interventions and transformations of the territory, with unprecedented urban development related to growing tourism activity. In this paper, we present a spatio-temporal analysis—by using spectral analysis techniques—of climate cycles on the island of Mallorca (Spain) and their correlation with the occurrences of landslides and flash floods. Both geohazards are closely related to wet periods, which are controlled by different, well-known natural cycles: ENSO, the NAO, sunspot, etc. Geostatistical methods are used to map the distribution of rainfall, as well as a spatial representation of the spectral confidence of the different natural cycles, to define the hazardous areas on the island. The cycles with the greatest influence on rainfall in Mallorca are El Niño–Southern Oscillation (ENSO) (5.6 y and 3.5 y), the North Atlantic Oscillation (NAO) (7.5 y), and Quasi-Biennial Oscillation (QBO). Recorded events of both rockfalls and flash floods exhibit a strong correlation with the climate indices of QBO, ENSO, the NAO, and sunspot activity. This correlation is particularly pronounced with QBO, as this cycle has a higher frequency than the others, and QBO is observed as part of the other cycles in the form of increases and decreases during periods of higher ENSO, NAO, and sunspot values. However, the impact of flash floods is also significant in the southeast part of the island, despite its lower levels of rainfall. The most dangerous episodes are related to ENSO (6.4 y) and the NAO. The validation of the methodology employed is strengthened by incorporating information from the flash flood data, as it offers comprehensive coverage of the entire island, compared to the landslide database, which is confined to the Serra de Tramuntana region. The study reveals that the city of Palma and the municipality of Calvià, as well as the central and eastern urban areas of the island, are the most vulnerable regions to intense rainfall and its consequences.

Hemispheric Distribution of Halo Coronal Mass Ejection Source Locations

The hemispheric asymmetry of solar activity is one of the essential physical consequences of the interior dynamo process. However, the hemispheric distribution of halo coronal mass ejection (HCME) source locations has not been investigated in detail. Based on the HCME catalog identified from the Large Angle and Spectrometric Coronagraph Experiment on board the Solar and Heliospheric Observatory, we perform a hemispheric distribution analysis of the HCME source locations from 1996 April to 2022 June. The main results are as follows. (1) The HCME source locations are confined to the active region belt, and there is no “rush to the poles” phenomenon that is unique to large-scale magnetic activity. (2) The HCME source locations exhibit a general hemispheric asymmetry, and autoregressive moving-average model results show that the asymmetry of HCME source locations is significantly different from that of sunspot activity. (3) The hemispheric distribution of cycle 24 is different from that of cycle 23, potentially as a result of the heliospheric dynamic pressure having noticeably decreased after the polarity reversal of cycle 23. Our results contribute to a more comprehensive understanding of the hemispheric asymmetry of energetic magnetic structures and give a new perspective on understanding the geoeffectiveness of HCMEs.

Sunspot activity prediction based on adaptive hybrid algorithms

Sunspot activity prediction based on adaptive hybrid algorithms

Fifty years of studying the GCR intensity during inversion of heliospheric magnetic fields I. Observations

The effects of the 22-year variation of solar magnetic fields in the galactic cosmic ray (GCR) intensity were first observed and interpreted as manifestations of inversion of the high-latitude solar magnetic field in properties of heliospheric magnetic fields by the Lebedev Physical Institute team in 1973. Since then, these effects have been studied already for 50 years.&#x0D; The situation with the heliospheric magnetic field is clear for periods of medium and low sunspot activity — the heliosphere consists of two unipolar “hemispheres” separated by a wavy global heliospheric current sheet and characterized by a general polarity A (unit quantity with the sign of the radial component of the heliospheric magnetic field in the northern hemisphere). Yet there is no consensus on what the inversion of the heliospheric magnetic field is and which effects in the GCR intensity are connected with this phenomenon.&#x0D; In this article, we briefly formulate general concepts of the 22-year variation in characteristics of the Sun, heliosphere, and GCR intensity and discuss the observed effects in the GCR intensity, which we attribute to the heliospheric magnetic field reversal. Models for this phenomenon and the results of GCR intensity calculations with these models will be discussed in the next article.

Пятьдесят лет исследования поведения интенсивности ГКЛ в периоды инверсии гелиосферного магнитного поля. I. Наблюдаемые эффекты

The effects of the 22-year variation of solar magnetic fields in the galactic cosmic ray (GCR) intensity were first observed and interpreted as manifestations of inversion of the high-latitude solar magnetic field in properties of heliospheric magnetic fields by the Lebedev Physical Institute team in 1973. Since then, these effects have been studied already for 50 years.&#x0D; The situation with the heliospheric magnetic field is clear for periods of medium and low sunspot activity — the heliosphere consists of two unipolar “hemispheres” separated by a wavy global heliospheric current sheet and characterized by a general polarity A (unit quantity with the sign of the radial component of the heliospheric magnetic field in the northern hemisphere). Yet there is no consensus on what the inversion of the heliospheric magnetic field is and which effects in the GCR intensity are connected with this phenomenon.&#x0D; In this article, we briefly formulate general concepts of the 22-year variation in characteristics of the Sun, heliosphere, and GCR intensity and discuss the observed effects in the GCR intensity, which we attribute to the heliospheric magnetic field reversal. Models for this phenomenon and the results of GCR intensity calculations with these models will be discussed in the next article.

Planetary influences on the solar cycle: A nonlinear dynamics approach.

We explore the effect of some simple perturbations on three nonlinear models proposed to describe large-scale solar behavior via the solar dynamo theory: the Lorenz and Rikitake systems and a Van der Pol-Duffing oscillator. Planetary magnetic fields affecting the solar dynamo activity have been simulated by using harmonic perturbations. These perturbations introduce cycle intermittency and amplitude irregularities revealed by the frequency spectra of the nonlinear signals. Furthermore, we have found that the perturbative intensity acts as an order parameter in the correlations between the system and the external forcing. Our findings suggest a promising avenue to study the sunspot activity by using nonlinear dynamics methods.

Wavelet Analysis of Ozone Driving Factors Based on ~20 Years of Ozonesonde Measurements in Beijing

A long-term vertical ozone observational dataset has been provided during 2001–2019 by ozonesonde measurements in Beijing on the North China Plain. Previous studies using this dataset primarily focused on the vertical characteristics of climatological ozone and its variation; however, the driving factors of ozone variation have not been well discussed. In this study, by applying the wavelet analysis method (including continuous wavelet transform and cross wavelet) and sliding correlation coefficients to ~20 years of ozonesonde measurements collected in Beijing, we analyzed the dominant modes of ozone column variability within three height ranges over Beijing (total column ozone: TOT; stratospheric column ozone: SCO; and tropospheric column ozone: TCO). Moreover, we also preliminarily discussed the relationship between these three ozone columns and the El Niño Southern Oscillation (ENSO), Quasi-biennial Oscillation (QBO), and 11-year solar activity cycle. The results revealed that the ozone columns within the three height ranges predominantly adhered to interannual variability patterns, and the short-term variabilities in TOT and SCO may have been related to eruptive volcanic activity. In comparison to the TOT and SCO, the TCO was more susceptible to the forcing influences of high-frequency factors such as pollutant transport. Similar to the results in other mid-latitude regions, strong ENSO and QBO signals were revealed in the interannual ozone column variability over Beijing. The TOT and SCO showed positive anomalous responses to ENSO warm-phase events, and the peak of the ENSO warm phase led the winter peaks of the TOT and SCO by approximately 3–6 months. During the strong cold–warm transition phase in 2009–2012, the TOT and SCO showed a significant positive correlation with the ENSO index. The strong seasonality of the meridional circulation process driven by the QBO led to a significant positive correlation between the QBO index and the TOT and SCO in the interannual cycle, except for two periods of abnormal QBO fluctuations in 2010–2012 and 2015–2017, whereas the TCO showed a time-lagged correlation of approximately 3 months in the annual cycle relative to the QBO due to the influence of the thermodynamic tropopause. In addition, analysis of the F10.7 index and the ozone columns revealed that the ozone columns over Beijing exhibited lagged responses to the peaks of sunspot activity, and there was no obvious correlation between ozone columns and 11-year solar activity cycle. Given the complex driving mechanism of the climatic factors on local ozone variability, the preliminary results obtained in this study still require further validation using longer time series of observational data and the combination of chemical models and more auxiliary data.

Depth asynchronous time delay reservoir for nonlinear time series forecasting task

The forecasting of nonlinear time series has a wide range of applications both in theoretical research and industrial production, as the fitting of nonlinear systems, the early warning of sunspot activity and the prediction of network traffic peaks in advance. These tasks require models with strong nonlinear mapping capabilities and sufficient short-term memory capacity. Single-node time delay Reservoir (TDR) have been frequently applied on nonlinear time series forecasting tasks. To address the lack of memory capacity of traditional TDR and its extended version (Delay Decoupled Reservoir (DDR), Deep Time-Delay Reservoir (DTDR)), we propose a Depth Asynchronous Time-Delay Reservoir (DATDR) model. Firstly, the model retains the deep network structure of the DTDR to ensure the dynamic properties of the model, and the history states of each layer are from the previous layers instead of the current layer. Secondly, the same signal input method as the DDR is used, i.e., each layer is fed with the original signal, and this data input method ensures that the feature of signal does not decay gradually during the transmission between layers. Finally, a time delay operator is inserted into two adjacent layers, and the memory capacity of the model could be controlled effectively by adjusting the delay time of the time delay operator to accommodate some time series prediction tasks that require long memory capacity. In this paper, eight datasets from three different types are used to validate the proposed model, including NARMA10-30, Mackey-Glass Chaos time series, He ́non map series, Sunspot Sequence, and two real network traffic datasets. Experiments show that proposed model gets significant improvement in memory capacity compared with TDR, DDR, and DTDR, and performs better for some nonlinear time series datasets especially some long-term memory datasets.

Inter-annual variation of the mean temperature and rainfall in Chad and the solar activity during the period from 1950 to 2020

Given the increased variability of rainfall, the resurgence of extreme meteorological phenomena such as droughts, floods, heat waves, and violent winds and considering the influence of solar activity on certain meteorological parameters, a study on the variation of temperature and rainfall in relation to solar activity is necessary. The present study aimed to analyze the inter-annual evolution of meteorological parameters (mean temperature and rainfall) in Chad over the period from 1950 to 2020 in two geographically distinct areas (Sudanian and Sahelian zone) and then to establish a link between these parameters and solar activity to predict the variations of these two parameters over time. The rainfall results showed that the wettest years were 1959 in N'Djamena and 1951 in Moundou. On the other hand, the driest years recorded were 1984 in N'Djamena and 1998 in Moundou. The study of the mean temperature in these two areas during the same period led to the conclusion that the hottest year was 2010 in N'Djamena; however, in Moundou the hottest year recorded was 1997. Globally, in the two zones concerned, analysis of the results on variations in rainfall showed a downward trend. However, the study of average temperature showed a steady rise over the same period. The superposition of rainfall and mean temperature with the sunspot numbers Rz led to the conclusion that high sunspot activity increases temperature and thus warming, while low sunspot activity brings high precipitation.