A daily rainfall data from 2020 to 2023 used in this study were obtained from Climate Hazards Centre InfraRed Precipitation with Station data (CHIRPS), located at the University of California Santa Baith high temperature.

CHIRPS is a quasi-global rainfall data set spanning over 40 years. From 1981 to the near present, CHIRPS has been creating gridded rainfall time series for trend analysis and seasonal drought monitoring by combining in-situ station data with 0.05˚resolution satellite images covering 50˚S-50˚N (and all longitudes) ( https://www.chc.ucsb.edu/data ).

This study covers daily data, from 2020 to 2023 inclusive. The lightning data was obtained from the World-Wide Lightning Location Network (WWLLN). They can be contacted at Earth and Space Sciences, University of Washington. The focus of this project is on Nigeria only, extracting the time and location of the lightning strokes.

It is a worldwide network that tracks lightning strokes across the entire globe. Operated by the University of Washington in Seattle, the World-Wide Lightning Location Network (WWLLN, pronounced “woollen”) is a network of very low frequency (VLF) radio lightning sensors. “Sferics”, or sudden lightning discharges, are the primary source of impulsive signals that dominate most ground-based observations in the 3 - 30 kHz VLF range. There is a significant amount of electromagnetic power that is radiated, primarily at very low frequencies (VLF), ranging from a few hertz to several hundred megahertz. WWLLN regularly creates maps of lightning activity across the whole earth according to its extensive global network of lightning sensors.

Research by [19] indicates WWLLN detection efficiency for strokes. Given that the VLF signal is less attenuated over oceans than over land during signal propagation, WWLLN may identify strokes with lesser energy across oceans compared to land. Consequently, WWLLN tends to identify more lower-energy strokes over the ocean’s regions and higher-energy strokes over land [19] . There is evidence that lightning over the oceans has more energy than lightning over land [20] [21] . WWLLN preferentially detects the more energetic lightning [22] .

Thunderstorm data for Nigeria only from 2021 and 2022, were also obtained from the Nigerian Meteorological Agency (NIMET)-Abuja. The geographical coordinates of each station were also obtained from the same source. Thunderstorm data were used as ground truth performance assessment of the Worldwide Lightning Location Network (WWLLN).

1) Downloading the WWLLN data for the globe from WWLLN online data records for the period of 2020 to 2023.

2) The data initially obtained in a compressed format (zip) and stored in a LOC file, was subsequently extracted.

3) Raw data is converted into comma-separated values (CSV) files using a specifically designed code. This enabled the transformation of the data from its original condensed format into a more accessible and analysable format.

4) This extraction was facilitated through the use of program codes, which applied geospatial coordinates (longitude and latitude) to pinpoint and isolate data points associated with individual stations within the Nigerian territory.

5) Aggregate daily data to yield monthly totals, which are combined to for MAM seasonal dataset for each station, to enable the examination of seasonal variability and trends.

6) Compute the coefficient of correlation.

For further analysis, with the aid of Geographic Information Systems (GIS) technology, was used as tools to show imageries of the yearly spatial variations of the thunderstorm and lightning data using heat maps. The data analysis was carried out and plotted with the use of MS Excel.

Thirty-seven (37) stations across Nigeria were considered for this study. The locations of study were specifically selected to have adequate representation of the various climatic zones and for comparisons among stations down south and up in the north. A comprehensive list of the 37 stations for this study is shown below in Table 1 according to their climatic zones. The longitude and latitude are approximate values.

The CORREL function in Excel was used to calculate the correlation coefficient between the two sets of data from WWLLN and from NIMET due to its various applications in data analysis, such as:

- Identifying relationships between variables

- Analysing trends and patterns

- Predicting outcomes based on correlations

- Evaluating the strength of relationships between variables

The correlation coefficient, denoted as r, ranges from −1 to 1, indicating the degree of correlation, as shown in Table 2 below.

The CORREL function takes the two ranges of data as inputs and returns the correlation coefficient. The syntax is: CORREL (array1, array2), Where array1 and array2 are the two ranges of data.

The temporal and seasonal variability of lightning strokes as reflected by the yearly total is shown below. Analysed plots showing this across the Rainforest, Guinea Savana and Sahel are depicted in Figure 2(a) . The different colours indicate the four years under study, thereby showing the years of high-frequency of lightning strokes and periods of low frequency. Figures 2(a)-(c) . represents the typical variation of LTG frequency across 37 stations in Nigeria, according to their climatic zones. The MAM season, which corresponds to the early part of the rainy season, is significantly characterized by increase in humidity and temperature as well as the onset of rainfall. An observation of the plots from 2020 to 2023 shows year-to-year variability in the values of LTG strokes in the Rainforest, Guinea Savanah and Sahel zone, with the Rainforest zone experiencing more LTG strokes than Guinea Savannah and Sahel zones accordingly. The highest LTG frequency of occurrence was in Calabar station (Rainforest zone) in the year 2023, with a maximum of 401 strokes, minimum LTG was in the Damaturu station (Sahel zone), no stroke was recorded in this station. A careful observation shows an increasing rate in the number of LTG strokes in Calabar station from 2021 to 2023, while no stroke is observed in Damaturu station throughout this period. Yenegoa had the second-highest lightning stroke occurrence. [23] and [24] established that the southern part of the country (where Calabar is situated) enjoys a double maxima rainfall while the northern part of the country (where we have Damaturu) enjoys a single maximum. This suggests that areas with prolonged rainfall seasons are more prone to thunderstorm activity. Longer rainfall seasons increase atmospheric instability, leading to more frequent thunderstorms. Double maxima rainfall patterns which is typical of tropical regions where Calabar is situated, enhance atmospheric instability and moisture content, favoring thunderstorm development. Single maximum rainfall, which is experienced in Damaturu implies shorter rainfall season, reduced atmospheric instability and therefore decrease in chances of lightning and thunderstorms

Figures 3(a)-(c) illustrates the thunderstorm counts for the years 2021 and 2022 as observed over 37 stations in Nigeria. It can be observed that thunderstorm counts from NIMET data has its highest frequency in Yenagoa station and its lowest counts across several stations, Damaturu inclusive. By comparing the thunderstorm (NIMET data) and the lightning (WWLLN data) frequencies, the highest frequency does not correspond with the same station (Calabar), implying that only fewer lightning strokes were produced by the thunderstorms in Yenegoa when compared with Calabar station. The pattern is the same with the rainforest having the most frequent occurrence of lightning and thunderstorms, and the Sahel zone having the least frequent occurrence of lightning and thunderstorms. [25] observe that WWLLN has been capable of detecting almost every thunderstorm since 2005, although this work did not perform thunderstorm extraction from lightning (WWLLN) data.

Figures 4(a)-(f) shows the TS and LTG distribution across stations in Nigeria from latitude 4˚ N to latitude 14˚N in the MAM season. the areas with the highest frequencies of thunderstorms (TS) are located between latitudes 4˚and 8˚N, which corresponds to the coastal plains in southern Nigeria. Specifically, Calabar, Asaba, and Yenagoa stations recorded the highest number of thunderstorm events. Calabar station which is one of the stations with peak in TS activities can be related to the study by [26] , the study showed more lightning flashes were predominant in the Nigeria-Cameroon rainforest zone than other zones. Calabar shares boundary with Cameroon, which is a mountainous area with consistently high lightning activities. Convective systems are more frequent in the location because of the topography [26] . Not all of these convective systems will develop into thunderstorms, or some may not be recognized as thunderstorms by observers. In contrast, areas with low frequencies of thunderstorms are situated between latitudes 12˚and 14˚N, covering the northern savanna and Sahel regions of Nigeria. Damaturu station did not record any thunderstorm events throughout the study period. The data from NIMET and WWLLN follows this same pattern.

The correlation value r, in the Table 3 was gotten from the square root of R² in the scatter plot (not shown) and regression analysis of WWLLN and NIMET. The general implication of the result in Table 3 is that rainforest zone shows consistent positive correlations, while Savana and Sahel zones show weaker or inconsistent correlations.

Rainfall distribution during the MAM season in Figures 5(a)-(d) show a clear gradient from south to north. The southern region experiences high and relatively consistent rainfall due to their proximity to the coast and the influence of the ITCZ. The central region (Lat. 9 - 12) has moderate rainfall with higher variability, marking a transition zone. The northern region receives low and highly variable rainfall, with a later onset of the rainy season, which reflects the semi-arid climate of the Sahel. Although rainfall is consistent in the southern region, it can be observed that there’s annual variation in some stations. From 2021 to 2023 there’s decrease in rainfall in Calabar station while there’s increase in Jos station from 2020 to 2022.

Comparing this result to the result on thunderstorms in previous section it can be seen that just as Calabar and Yenagoa regions experienced more thunderstorms these stations also experienced the highest amount of rainfall during the MAM season. In 2022 Yenagoa had the highest number of TS Counts which is 86 and the highest amount of rainfall (364.5 mm), although MAM is the onset of the rainy season these peak numbers recorded in Yenagoa station account for one of the reasons why there was severe flood in Yenagoa that year.

In the MAM period, as shown in Table 4 , the correlations in the rainforest, savanna, and Sahel zones show moderate positive correlations, indicating that during this season, rainfall and lightning activities are moderately related.