1 Introduction

Various activities in Earth's upper regions are closely linked to solar activities and heliospheric magnetic field processes [1]. Cosmic rays, predominantly originating from outside the heliospheric space and abundant at the heliopause [2, 3], comprise highly energetic particles, mainly protons and alpha particles, striking the Earth's upper atmosphere from space [4, 5].

These high-energy particles, predominantly protons and atomic nuclei, are emitted from diverse cosmic sources, including supernova explosions and active galactic nuclei [3, 6]. Upon entering Earth’s atmosphere, these particles interact with air molecules, causing ionisation and generating secondary particles such as electrons and muons [7, 8]. The relationship between cosmic rays and atmospheric dynamics is established by the process of ionising air molecules with high-energy cosmic ray particles [9,10,11]. This ionisation can have an impact on the creation of aerosols and cloud condensation nuclei (CCN) in the atmosphere [12]. This process has the ability to affect the development and characteristics of clouds, as well as the Earth's radiation budget and climate [12, 13]. These cosmic rays stem from two primary sources: heliospheric and galactic cosmic rays (GCR). Both cosmic ray intensity and atmospheric temperature closely correlate with solar activity variations [14], exhibiting fluctuations over different timeframes.

Ionised air molecules and secondary particles can contribute to the creation of new aerosol particles via a process known as ion-induced nucleation. These recently created aerosol particles have the ability to function as cloud condensation nuclei (CCN), which are essential for the creation of cloud droplets [13, 15]. CCN in the atmosphere impact the microphysical characteristics of clouds, including their abundance, size distribution, and duration. Variations in these characteristics can affect the cloud's ability to reflect sunlight (albedo), the distribution of rainfall, and the overall energy balance, which in turn affects atmospheric circulation and climate [16]. Cloud cover and features can impact atmospheric circulation patterns, temperature gradients, and the distribution of energy in the Earth's atmosphere [17, 18]. These alterations in the movement of air in the atmosphere can also impact weather patterns, the amount of rainfall, and eventually, the overall climate of the Earth [13].

Solar activity, marked by events such as solar flare eruptions, coronal mass ejections, and greenline emissions, plays a pivotal role in determining ionospheric and Earth's upper layer dynamics, subsequently impacting our weather systems. These solar events, occurring with an approximately 11-year periodicity, interact with the Earth's atmosphere, influencing processes like heating, convection, and circulation, necessitating a comprehensive understanding of their effects on the Earth's atmosphere. The cosmic ray ionisation mechanism can impact clouds' creation and characteristics [19]. Because of their increased intensity and ability to penetrate more resounding, extragalactic cosmic rays could significantly impact cloud cover and microphysics more than galactic cosmic rays [19, 20]. Ionised air molecules may participate in many chemical processes, which impact the concentration of trace gases and the creation of aerosols [15, 16].

The equatorial electrojet is a concentrated and powerful flow of electrical currents that move towards the east in the E-region of the ionosphere, specifically within a limited band around the Earth's magnetic equator, usually within ± 3° latitude of the magnetic equator [21, 22]. Nigeria is situated in the western part of the African continent, spanning from around 4° N to 13° N latitude in its southern half. The country is positioned within the equatorial electrojets region, characterized by intense eastward currents in the upper atmosphere [21,22,23]. The magnetic equator, where the equatorial electrojet is concentrated, traverses central Nigeria at around 7° N geographic latitude. Thus, the southern region of Nigeria, encompassing towns such as Lagos, Ibadan, and Benin City, is situated within or in close proximity to the latitude range affected by the equatorial electrojet currents, critical for understanding their impact on the weather and meteorological systems over Nigeria.

Classified as a tropical country, Nigeria encompasses four major climate zones with vegetation distributed across varying latitudes and longitudes, as outlined in Table 1 [24]. Consequently, Nigeria receives consistent solar radiation and energy, capable of significantly affecting atmospheric conditions throughout the year [25, 26].

Table 1 The geographical coordinates and regions of Nigeria's climatic areas based on four major weather classifications
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The mechanisms through which solar activity influences precipitation and temperature are intricate, involving intricate loops with atmospheric and oceanic systems [27]. Solar activity has garnered interest due to its potential effects on weather conditions, particularly in Nigeria. Numerous studies have investigated the relationship between solar activity and climate change in Nigeria, highlighting the significance of solar activity in shaping weather patterns [24, 28,29,30]. Sloan and Wolfendale [31] identified a probable link between solar activity and the average global surface temperature. This consists of a temperature oscillation with an amplitude of ± 0.07 °C and a duration of 22 years. Examining cosmic rays, a comparable oscillation is observed, but with a time lag of 1–2 years. In a study conducted by Wollin et al. [32] in 1971, a connection was established between increased magnetic activity and cooler temperatures. This conclusion was drawn based on observed correlations, suggesting a potential cause-and-effect relationship between changes in Earth's magnetic field and climate.

The cosmic ray rate exhibits a 22-year oscillation with an amplitude greater than the long-term change in the cosmic ray rate. Utilizing the South Atlantic Anomaly (SAA) region as a representation of Earth's surface and the Global Sea Level (GSL) increase as an indicator of the climate system, a study carried out by Campuzano et al. [33] in 2018 corroborated the concept that there is a connection between the geomagnetic field and Earth’s climate, offering fresh insights into the underlying physical processes that could be responsible. Kirkby and Carslaw [34] regarded the fluctuation of Galactic Cosmic Rays (GCR) as a reliable indicator of changes in solar irradiance or UV radiation. They hypothesized that these changes are closely linked to long-term variations in solar magnetic activity. However, the study discovered that the climate experiences cooling on both short and long-time scales when exposed to high cosmic ray intensity, and warming when exposed to low cosmic ray intensity.

The correlation between galactic cosmic rays and low cloud cover is significantly less compared to the correlation between solar irradiance and low clouds as observed by Kristjánsson et al. [35]. However, after detrending the cloud cover data, the correlation experiences a substantial improvement. Furthermore, solar radiation's impact on energy availability and utilization in Nigeria has been examined, shedding light on climate-sensitive agricultural activities and energy variations across different locations. This study aims to significantly contribute to exploring the connections and importance of space weather to climate dynamics, the impact of extragalactic cosmic rays on Earth's atmosphere and atmospheric dynamics, which are crucial for understanding climate dynamics, improving climate models, studying the ozone layer, understanding atmospheric chemistry and air quality, and providing insights into the complex interactions between cosmic radiation, atmospheric processes, and climate dynamics.

Understanding the relationship between solar activity and our local space environment and weather system, alongside precise long-term prediction of these activities, holds promise for enhancing meteorological and weather forecast capabilities. Solar and weather data serve as crucial inputs for research on atmospheric phenomena, extensive weather analysis, and forecasting. This study aims to deepen our comprehension of Nigerian atmospheric and weather conditions and the underlying physical systems governed by solar activity. Additionally, it seeks to contribute statistical documentation of Nigerian climatic conditions correlated with solar activities, facilitating machine learning applications for effective forecasting techniques and identifying favorable atmospheric conditions for agricultural production across geographical and regional climates in Nigeria.

2 Data and method

As previously mentioned, the data utilized in this study comprises solar and atmospheric weather data spanning from 1964 to 2019, covering five solar cycles from cycle 19 to cycle 24.

2.1 Data

(i) Solar Flare Index: The solar flare index quantifies chromospheric variability in the solar atmosphere. This data was collaboratively produced by the Astronomical Institute Ondřejov Observatory of the Czech Academy of Sciences and the Kandilli Observatory of Istanbul, Turkey. It is available in daily, monthly, and annual resolutions spanning from 1937 to 2020. We acquired the dataset for each month and subsequently implemented the 13-month moving average. The specific information on this data has been disseminated by Velasco et al. [36] and may be obtained through https://doi.org/10.7910/DVN/U5GR3D

(ii) Cosmic Ray Intensity: In this investigation, we utilize pressure-corrected daily cosmic ray data provided by the Cosmic Ray Station at University of Oulu/Sodankyla Geophysical Observatory. The data is configured with an auto resolution of 1440 min and can be obtained by downloading it from http://cosmicrays.oulu.fi/. The CRI data, which was adjusted for pressure, was collected at an altitude of 15 m above sea level, with a geographic latitude of 65.05° N and a longitude of 25.47° E. The measurement was conducted using a 9-NM-64 detector (Chalk River tubes) at a vertical cutoff stiffness of around 0.8 GV.

(iii) Atmospheric Weather: The data used in carrying out this study was generated and collected through ERA5, a global organization dedicated to the dissemination of a wide range of atmospheric, ocean-wave, and land-surface parameters from 1950 to the present. The ERA 5 dataset has a spatial resolution of 0.25 degrees in latitude and longitude. ERA-5 is the latest iteration of the ECMWF reanalysis, developed for the Copernicus Climate Change Service (3CS). The data offers superior data accuracy with a spatial resolution of 25 km, surpassing previous reanalysis sources. ERA5 reanalysis utilises physical principles to combine model data and global observations, creating comprehensive and consistent climate datasets. ERA-5 climate data packages are available for both pressure levels (upper air fields) and single levels including atmospheric (temperature, relative humidity, and precipitation), ocean wave, and land surface quantities [37,38,39,40]. The data may be accessed using the following link: https://cds.climate.copernicus.eu/cdsapp/dataset/reanalysis-era5-single-levels?tab=form. The data is further processed by applying the 13-month moving average.

2.2 Methods

2.2.1 Cross-correlation analysis

Cross-correlation analysis (CCA) is a statistical technique used to evaluate the degree of association between two real continuous functions, denoted as \({\delta }_{xy}\). The resulting value is dimensionless and falls within the range of − 1.0 to + 1.0, calculated as the ratio of variances. The cross-correlation coefficient between the two data series is defined as:

$$\text{CC}\left(\Delta \right)= \frac{{\sum }_{\text{i}=1}^{\text{n}}\left[\text{x}\left(\text{i}\right)-\left\{\text{x}\right\}\right][\text{y}\left(\text{i}+\Delta \right)-\left\{\text{y}\right\}]}{(\text{n}-1){\updelta }_{\text{x}}{\updelta }_{\text{y}}}$$
(1)

where \(\text{CC}\left(\Delta \right)\)= the cross-correlation coefficient. \(x\) and \(y\) = represent the data, namely; temperature, precipitation, relative humidity, solar flare index, and cosmic ray intensity, which are both used simultaneously to represent two of the datasets in the analysis. \(\Delta\) = time lag or shift between two data input. n is the length of the data. \({\delta }_{x}\) and \({\delta }_{y}\) are the standard deviations that normalise the cross-correlation coefficient to guarantee its range is from − 1 to 1. Details are provided in [41,42,43,44,45].

2.2.2 Wavelet coherence (WTC)

Wavelet Coherence (WTC) functions is basically a correlation coefficient, indicating significant shared power regions between two pairs of time-varying data in both their time and frequency domains. In this case, comparisons between atmospheric weather parameters such as precipitation, temperature, and relative humidity are considered, along with solar activity parameters including the solar flare index and cosmic ray intensity. The approach described in this study is both distinctive and valuable for calculating the time and frequency of signals. It is capable of detecting strong coherence even when the signals have low power levels [46].

The significance of wavelet transform coefficients (WTC) arises from the observation that the wavelet cross-spectrum is not suitable for assessing the interrelationship between two progressions [47, 48]. The concept of wavelet coherence may be applied to two time-varying signals, with atmospheric weather data considered as a and solar activity data as b, and expressed as follows:

$${\text{R}}_{\text{n}}^{2}\left(\text{s}\right)= \frac{|\text{S}({\text{s}}^{-1}{\text{X}}_{\text{n}}^{\text{ab}}\left(\text{s}\right)){|}^{2}}{\text{S}({\text{s}}^{-1}{|\text{X}}_{\text{n}}^{\text{a}}\left(\text{s}\right){|}^{2} *\text{ S}({\text{s}}^{-1}{\text{X}}_{\text{n}}^{\text{b}}\left(\text{s}\right){|}^{2}}$$
(2)

The cross-wavelet transform of the two discrete time signals a and b (such as; temperature, precipitation, relative humidity, solar flare index, and cosmic ray intensity) can be defined as;

$${{\text{X}}^{\text{ab}}}={{\text{X}}^{\text{a}}}{{\text{X}}^{\text{b*}}}$$
(3)

where \({\text{X}}^{\text{a}}\) and \({\text{X}}^{\text{b}}\) characterise the continuous wavelet transform of the two discrete time signals a and b. The * characterizes the complex conjugation. The arg \({\text{X}}^{\text{ab}}\) is the complex argument that signifies the local relative phase between a and b in the time–frequency domain; that specifically denotes the phase angle difference between a and b [46, 49]. S represents the smoothing operator. Due to the presence of edge artefacts, the localization of wavelet is not perfect. To mitigate this problem, the cone of influence (COI) was introduced. The cone of influence (COI) refers to the region where the wavelet power reduces by a factor of \({\text{e}}^{-2}\) for a discontinuity at the edges, as described by Grinsted et al. [46]. Comprehensive and detailed applications can be obtained from [46,47,48, 50, 51].

3 Results and discussions

3.1 The correlations

Table 2 provides the basic statistical analysis of the parameters, including mean values, length, lags, coefficients of correlation, types, and significance levels of correlation. In Table 3, we provide parameters for solar activities alongside total weather parameters. Further details are discussed along with other figures. The variations of atmospheric weather parameters, which include the temperature, precipitation, and relative humidity across various climatic regions in Nigeria, are presented in Fig. 1. Abuja, Kano, and Nguru are located in the northern region (Guinea and Sahel Savannah) along 8.92 to 13.70 degrees latitudes, and Akure, Lagos and Port Harcourt are in the Southern regions (Coastal and Derived) located within latitudes 4.5 and 8.9-degree latitudes show different variation patterns and values. The northern parts' temperature is relatively higher (in multiple) than the southern values. The lowest value was recorded in Akure (south) in 1989. The precipitation recorded higher values in the southern regions, while the northern show an inconsistently low pattern. This shows a reverse trend with relative humidity, where the southern relative humidity sustained near-constant values between 24 and 27%. The highest record was in Abuja, with Kano following at a distance second, and Lagos has the lowest value.

Table 2 The statistical analysis of the parameters which include, the mean values, length, lags, coefficient of correlations, types and correlation significant levels
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Table 3 The statistical analysis for the average parameters across Nigeria including, the mean values, length, lags, coefficient of correlations, types and correlation significant levels
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Fig. 1
figure 1

The variations of temperature, precipitation, and relative humidity across various climatic regions in Nigeria are depicted

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Figure 2 illustrates the variations of temperature, precipitation, and relative humidity across various climatic regions in Nigeria. The figure depicts temperature and relative humidity on the left y-axis and precipitation on the right y-axis. The colors blue, pink, and orange represent relative humidity (RH), temperature, and precipitation (PPT), respectively. The left y-axis displays the temperature and RH values, while the right presents the values for PPT.

Fig. 2
figure 2

The distributions of temperature, precipitation, and relative humidity across various climatic regions in Nigeria are depicted. The blue, pink, and orange colours represent relative humidity (RH), temperature, and precipitation (PPT), respectively

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The figure indicates nearly constant temperatures for northern cities like Abuja, Kano, and Nguru, averaging around 25 degrees Celsius. Southern temperatures exhibit more irregular variations across the region, particularly following seasonal changes. Relative humidity was unusually high from 1964 and sharply decreased in Nguru and Kano, while Abuja showed a steady decline. Conversely, the southern region experienced the opposite trend, with humidity increasing in 1969 and 1970 before steadily decreasing. On average, RH was higher in Abuja compared to any other region, with an average value of 62.64 percent, while Port Harcourt (PHC) had the lowest average value at 24.77 percent. Further details of these properties are presented in Table 2. Relative humidity is higher in the northern region compared to the south. Precipitation exhibits different behaviors in the north and south regions, with significantly higher values observed in the south compared to the north. It also behaves oppositely, recording its lowest value in Nguru at 11.0 mm, while its highest values of 90 mm were recorded in PHC. These differences could be attributed to factors such as cloudiness, temperature, latitudes, and sea waves across the southern regions, as observed earlier [25, 26, 52,53,54].

The variations in solar activity and atmospheric weather indices are presented in Fig. 3. The cosmic ray is represented by the color blue, while the solar flare index (SFI) is represented by the color orange. The values for SFI are presented on the right-hand y-axis, while the opposite axis represents the cosmic ray intensity (CRI). Both parameters exhibit opposite patterns and tend to have variations similar to solar cycles. These parameters are utilized to evaluate the influence of solar magnetic activity in the upper atmosphere, especially for Nigeria. They have been extensively studied and are well-known for their attributes and opposite behaviors [3, 55, 56].

Fig. 3
figure 3

The upper panel depicts the variations in solar activity and atmospheric weather indices, with cosmic rays (Gy/h) and solar flares (W/m2) exhibiting opposite patterns and similar variations to solar cycles. The lower panel shows variations in average temperature, precipitation, and relative humidity variations across Nigeria

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The average values for temperature, precipitation, and relative humidity across Nigeria are presented in the second panel of Fig. 3. The colors orange, blue, and pink represent relative humidity, precipitation, and temperature, respectively. The temperature maintains steady variations with an average value of 24.41 degrees Celsius, while the relative humidity shows a sudden drop from 1964 to 1969 before maintaining relatively regular fluctuations. Precipitation exhibits a similar pattern to relative humidity before 1970. Unusual fluctuations were observed between 2005 and 2019, with 2014 having the highest peak since 1964. This peak was observed almost at the same time as the high SFI in 2015. The precipitation has an average value of 56.28 mm. More details are provided in Table 2.

Figure 4 depicts the cross-correlation analyses between precipitation and temperature across selected regions and cities in Nigeria. The analysis reveals a negative correlation between temperature and precipitation across the northern region, with Nguru exhibiting the highest anti-correlation at a significant level of 0.6. Conversely, the relationship in the southern region is directly opposite, except for PHC, which shows a weak and negative correlation. The highest positive correlation is recorded in Akure at 0.3. This indicates that temperature variations influence the amount of rainfall in the region, which could further be attributed to increased growth and production of certain crops in the south [57]. No lag is recorded except for PHC, where temperature leads. Details are provided in Table 2.

Fig. 4
figure 4

Cross-correlation between temperature and precipitations across Nigeria. The abscissa designates the shift in monthly temperature change

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The relationships between relative humidity and temperature are illustrates in Fig. 5. The analysis reveals that the behaviors exhibited by these parameters in the southern region are opposite to those exhibited in the north. Relative humidity correlates negatively with temperature across northern cities, with Kano recording the highest negative correlation at 0.53. Conversely, the relationships across southern cities are strong, positive, and significant, with PHC recording a correlation coefficient of 0.75. No lag is recorded, with details provided in Table 2. This difference in behavior could be attributed to factors such as sea waves and atmospheric coupling, as observed earlier.

Fig. 5
figure 5

Cross-correlation between temperature and relative humidity across Nigeria. The abscissa designates the shift in monthly temperature change

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In Fig. 6, we present the relationships between precipitation and relative humidity across Nigeria. The analysis reveals their distinct behaviors, which could be related to latitudinal distributions of temperature and relative humidity. As observed earlier by Tinsley and Yu [16], the concentrations of ions influence cloud properties like droplet size distribution, albedo, and precipitation rates, impacting atmospheric temperature and dynamics, and their effectiveness depends on latitude, altitude, cloud type, and atmospheric sulfate content. The relationship is negative in the south and positive in the north. This indicates that precipitation may depend on relative humidity, which is positive in the north, while the opposite is observed in the south. The analysis shows no lag or lead, and further information is provided in Table 2.

Fig. 6
figure 6

Cross-correlation between precipitations and relative humidity across Nigeria. The abscissa designates the shift in monthly precipitation change

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In Fig. 7, we examine the correlations between Nigeria's atmospheric weather parameters and solar activity parameters, including temperature, precipitation, relative humidity, cosmic rays, and the solar flare index. These parameters were selected based on their behaviors observed in previous studies [3, 55, 56]. The left-hand side panels depict the relationships between cosmic rays and the atmospheric weather parameters, while the right-hand panels depict the relationships for the solar flare index.

Fig. 7
figure 7

Cross-correlation between average atmospheric weather conditions (temperature, precipitation, relative humidity) across Nigeria and the solar activity parameters. The left-hand side panels present the relationships between cosmic rays and the atmospheric weather parameters, while the right panels present analyses for the solar flare index

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Observations reveal that cosmic ray intensity correlates positively with these atmospheric weather parameters, with relative humidity recording the highest value at a correlation coefficient of 0.42, while temperature exhibits the lowest value. The weak but positive correlations suggest little or no major influence of cosmic ray intensity on the upper atmosphere within Nigeria or major weather conditions. On the other hand, the solar flare index shows negative correlations with temperature, relative humidity, and precipitation. These negative correlations are also weak.

3.2 The wavelet coherence

Figure 8 presents the analysis conducted by wavelet coherence (WTC) for the solar activity index and atmospheric weather within the Nigerian upper atmosphere. The relative phase connections between the entities are depicted by the direction of the arrows. Upward-pointing arrows indicate a leading phase connection, while downward-pointing arrows indicate otherwise. Antiphase connections are indicated when arrows point in opposite directions. The cone of influence (COI), shown by the white dashed line, accounts for edge effects. Regions with high coherence values, approaching 1, signify a robust connection between the two signals at specific frequencies and throughout specified time intervals. Conversely, low coherence levels (approaching 0) indicate minimal or non-existent correlation. The phase relationship or disparity, shown by arrows, indicates whether the signals are in-phase (converging) or out-of-phase (diverging).

Fig. 8
figure 8

The wavelet coherence (WTC) analysis was performed to examine the relationship between the solar activity index and atmospheric weather (selected parameters) in Nigeria's upper atmosphere

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Figure 8a–c depict the relationship between cosmic ray intensity and temperature, precipitation, and relative humidity, respectively, while the lower panels Fig. 8d–f depict the relationships for solar flare index. Figure 8a illustrates weak interactions between cosmic ray intensity and temperature, with phase synchronizations observed from 2006 to 2019 at variations of 16–32 months. Various phase relationships are captured at periodicities around 32–64 months for relative humidity in Fig. 8c. The analyses for solar flare index are presented in Fig. 8d–f. Figure 8d shows more anti-phase relationships between temperature and solar flare index, especially from 1980 to 2019, with periodicities around 16–32 months. In Fig. 8e, precipitation exhibits more phase synchronizations, mostly between 64 and 128 periodicities from 1970 to 1980, compared to temperature. More phase coherence is observed leading with relative humidity in Fig. 8f. The analysis indicates that fluctuations in atmospheric weather across regions could be linked to various factors that may not be directly captured but influence weather activities. The results presented are above the 95 percent confidence level.

In comparison with previous studies, the decadal rainfall anomaly exhibits a consistent increase in positive anomalies towards the coastal areas, suggesting a rise in rainfall characteristics. Conversely, negative anomalies demonstrate an increase towards the northern regions, indicating a decline in rainfall characteristics. The correlation study indicated that there was no statistically significant link between rainfall and both sunspot and geomagnetic activity index [57], observed that significant runoffs and flooding have occurred in numerous areas of Nigeria as a result of longer and more intense periods of rainfall. Significant runoffs and flooding have occurred in numerous areas of Nigeria as a result of longer and more intense periods of rainfall. In separate observations, precipitations in the future are anticipated to be more erratic, with an increase in southern regions, while rising sea levels are expected to intensify coastal floods and land submersion [28, 30].

Ojo et al. [58] estimated solar radiation using air temperature and geographical coordinates using data obtained from Nigerian Meteorological Agency (NIMET) selected across Nigerian climatic regions. The study revealed a positive correlation between increasing latitude and solar radiation using data obtained from NIMET. Mechiche-Alami et al. [59] reported that, West Africa’s climate is anticipated to become increasingly drier as temperatures rise and rainfall patterns become more unpredictable. Furthermore, climate change was responsible for 25 percent of the browning trends and 52 percent of the greening trends, with Nigeria contributing the majority of the browning trends. Previously, [60], showed the impacts of climate change in Nigerian agriculture and rural life, attributing variability in the weather and the causes of climate change in Nigeria to release of greenhouse gases from gas flaring, open burning, car emissions, deforestation, and the use of solid fossil fuels for cooking.

Antisymmetric variations were found between series of ionograms recorded for Equatorial spread-F (ESF) in West Africa [61]. Okeke and Audu [62] investigated the effects of geomagnetic and solar activity on climatic parameters in Nigeria. The study highlighted the significance of solar activity in influencing weather patterns in Nigeria. Audu and Okeke [54] explored the impact of solar and geomagnetic activities on Nigerian atmosphere, revealing that changes in cloud covers are linked to rainfall and temperature, providing insights into potential solar activity influences on climate change. The effects of cosmic rays were not ascertained in the study. Elmhamdi et al. [63] examined the impact of solar eclipses on terrestrial atmospheric parameters found that humidity's response differs from temperature's, with partial eclipses affecting temperature, relative humidity, and vapor pressure.

4 Conclusions

In this paper, we investigated the variations of atmospheric weather parameters in relation to solar activity parameters, including temperature, precipitation, relative humidity, solar flare index, and cosmic ray intensity. The study aimed to understand the influence of solar activity within the equatorial electroject region, using atmospheric weather data obtained from locations across Nigeria as a basis for understanding the parameters contributing to weather fluctuations and instabilities. Our main findings can be summarized as follows:

  • Relative humidity (RH) is higher in the northern region compared to the south. Precipitation (PPT) exhibits different behaviors in the north and south regions, with significantly higher values observed in the south.

  • Temperature correlates negatively with precipitation across the northern region, with Nguru showing the highest anti-correlation at a significant level of 0.6. In the southern region, the relationship is opposite, except for PHC, which exhibits weak and negative correlation. The highest positive correlation is recorded in Akure at 0.3 signifying higher rainfall.

  • The temperature in the northern parts is comparatively higher than in the southern region, however the opposite was seen for precipitation in the south. The greatest values for relative humidity were recorded in the northern location.

  • Relative humidity correlates negatively with temperature across northern cities, with Kano recording the highest negative correlation at 0.53. Conversely, relationships across southern cities are strong, positive, and significant, with PHC recording 0.75. No lag was recorded. This substantial relationship suggest that changes in temperature and relative humidity may be a function of latitudinal atmospheric current distributions.

  • The relationships between precipitation and relative humidity across Nigeria exhibit distinct behavior, which could be attributed to differences in ionospheric conductivity across the country. The relationship is negative in the south and positive in the north, suggesting precipitation may depend on relative humidity, which is positive in the north and vice versa in the south.

  • Cosmic ray intensity correlates positively with atmospheric weather parameters, with relative humidity recording the highest correlation coefficient at 0.42, while temperature shows the lowest value. However, these correlations indicate little or no major influence of cosmic ray intensity on the upper atmosphere within Nigeria or major weather conditions.

  • The solar flare index shows negative correlations with temperature, relative humidity, and precipitation, with weak correlations observed, suggesting an insignificant influence on weather conditions within the region.

  • Weak coherence between cosmic ray intensity and temperature were observed, with phase relationship observed from 2006 to 2019 at variations of 16–32 months. Precipitation exhibits similar behavior to temperature with cosmic ray, but with different phase synchronizations observed at different times.

  • Cosmic ray intensity and solar flare index exhibit opposite behaviors and tend to vary but exhibit similar wavelengths to solar cycle patterns.

To improve data accuracy, we recommend using locally installed equipment in conjunction with satellite data for capturing weather parameters. Therefore, we suggest the development and deployment of locally built equipment for in-situ data capture to enhance accuracy.