An Investigation of GNSS Radio Occultation Data Pattern for Temperature Monitoring and Analysis over Africa

Abstract

Climate change monitoring and analysis is a critical task that involves the consideration of both spatial and temporal dimensions. Theimproved spatial distribution of the global navigation satellite system (GNSS) ground-based Continuous Operating Reference (COR) stations can lead to enhanced results when coupled with a continuous flow of data over time. In Africa, a significant number of COR stations do not operate continuously and lack collocation with meteorological sensors essential for climate studies. Consequently, Africa faces challenges related to inadequate spatial distribution and temporal data flow from GNSS ground-based stations, impacting climate change monitoring and analysis. This research delves into the pattern of GNSS radio occultation (RO) data across Africa, addressing the limitations of the GNSS ground-based data for climate change research. The spatial analysis employed Ripley’s F-, G-, K-, and L-functions, along with calculations of nearest neighbour and Kernel density. The analysis yielded a Moran’s p-value of 0.001 and a Moran’s I-value approaching 1.0. For temporal analysis, the study investigated the data availability period of selected GNSS RO missions. Additionally, it examined seasonal temperature variations from May 2001 to May 2023, showcasing alignment with findings from other researchers worldwide. Hence, this study suggests the utilisation of GNSS RO missions/campaigns like METOP and COSMIC owing to their superior spatial and temporal resolution.

1. Introduction

The 2022 report on the State of the Climate in Africa emphasises the concerning acceleration of temperature elevation on the continent, coupled with a rise in extreme weather events and climate-induced risks [1]. Despite these urgent challenges, the financial assistance designated for climate adaptation falls significantly short of the necessary amount, essentially constituting a minimal contribution [2,3,4,5,6]. Various regions of the Sahel experienced substantial flooding during the monsoon season, with Nigeria, Niger, Chad, and the southern portion of Sudan being particularly impacted [7,8]. The investigation of climate change in the realm of global warming has been significantly influenced by the spatial distribution of GNSS stations on the African continent, as well as the continuous temporal data streaming from these ground-based stations [9,10,11]. Radio occultation is the most suitable method in climate monitoring and analysis due to its global coverage, very high horizontal and vertical resolution, all-weather capability, high cost-effectiveness, self-calibrating nature, higher accuracy, and high stability [12,13]. Radio occultation is a rather new method forthe indirect measurement of temperature, pressure, and water vapour in the stratosphere and troposphere. The technique is based on utilising the radio signals continuously broadcast by the GNSS satellites (GPS/GLONASS/Galileo) orbiting the Earth at an altitude of about 20,000 km above the Earth’s surface [12]. The radio occultation (RO) technique is an active satellite-to-satellite limb-sounding conceptthat exploits the global navigation satellite system (GNSS) signals [9,10,13].

Figure 1 shows the concept of global n avigation satellite system radio occultation (GNSS RO) in which the GNSS satellite is sending a signal to the Low Earth-Orbiting (LEO) Satellite (the receiver) such as COSMIC, GRACE, METOP CHAMP, etc., when the signal passes through the atmosphere. In Figure 1, α = the bending angle, a = the impact parameter, and r = the curvature radius, respectively.

Table 1. The GNSS RO missions.

S/N	Missions	Date	Status	Country	Altitude (Km)	Inclination Angle (°)	Coverage
1	CHAMP	2001–2008	Non-operational	Germany	454	87.3	Global
2	GRACE	2007–2017	Non-operational	Germany	485	89	Global
3	COSMIC-1	2006–2019	Non-operational	Taiwan	800	72	Global
4	TERRASAR-X	2008–2019	Operational	Germany	514.8	79.5	Global
5	CNOFS	2010–2015	Non-operational	USA	464	13	Global
6	KOMPSAT5	2015–2019	Operational	Korea	550	97.6	Global
7	PAZ	2018–2019	Operational	Spain	514	97.4	Global
8	SACC	2006–2011	Non-operational	Argentina	702	98	Global
9	METOP-A	2007–2015	Non-operational	Europe	827	98.7	Global
10	METOP-B	2013–2019	Operational	Europe	827	98.7	Global
11	METOP-C	2019–now	Operational	Europe	817	98.7	Global
12	COSMIC-2	2019–now	Operational	Taiwan	800	72	Global

presents the GNSS RO missions with their date (life span timeline), purpose, status, country, altitude, and inclination angle. The GNSS RO technique utilises a high precision GNSS receiver mounted on an LEO satellite to track the navigation signals transmitted by the GNSS satellites. As the Low Earth-Orbiting (LEO) satellite rises or sets behind the Earth, the view of the GNSS satellites changes, and signals pass through both the ionosphere and natural atmosphere and are affected significantly [14].

2. Materials and Methods

2.1. Study Area

The research area under consideration is the African continent (Figure 2). The magnitude of the research domain spans from longitudes 17° W to 51° E of the Greenwich meridian and from latitudes 37° N to 35° S of the Equator. Africa ranks as the second most extensive continent globally, following Asia [15,16,17,18,19,20,21]. Its borders are demarcated by the Mediterranean Sea to the north, the Red Sea to the east, the Indian Ocean to the south, and the Atlantic Ocean to the west. The Equator acts as a dividing line, splitting the continent into nearly symmetrical halves. Africa exhibits distinctive features across eight primary zones: the Sahara, the Sahel, the Ethiopian Highlands, the savanna, the Swahili Coast, the rainforest, the African Great Lakes, and Southern Africa [15,16]. The entire area of Africa is 30,365,000 square kilometres, and the continent is roughly 8000 km long from north to south and 7400 km wide from east to west [17,18,19,20,21,22,23,24,25,26,27].

2.2. Data

The GNSS RO data used in this study is from May 2001 to May 2023, and was downloaded from the University Corporation for Atmospheric Research (UCAR) website (https://data.cosmic.ucar.edu/gnss-ro/, accessed on 23 December 2023). The temperature dataset used in this research was derived from the GNSSRO data from the UCAR website.

2.2.1. Data Processing

The data was processed using Python programming language script through Anaconda Navigator (version 2.6.2) in JupyterLab environment, and the output from the processing was saved in CSV Excel file format. the python libraries used were geopandas as gpd, pandas as pd, numpy as np, matplotlib.pyplot as plt, sklearn.neighbors, scipy.stats, pysal.lib, pysal.explore, and os. The missions selected for investigation in this research endeavour, namely CHAMP (2001–2007), COSMIC-I (2006–2019), and COSMIC-II (2020–2023), aimed to acquire datasets that comprehensively encompassed the temporal span of 2001 to 2023, while METOP was employed to mitigate certain data deficiencies that occurred throughout the study period.

2.2.2. Data Analysis

The processed data was also analysed by the use of Python script; the spatial analysis was performed based on Ripley’s F-, G-, K-, and L-functions, nearest neighbour and kernel density estimation.

The F-, G-, K-,and L-Functions

In spatial data analysis, point patterns are analysed using the F-, G-, K-,and L-functions, which are all spatial statistics. Different elements of the geographic distribution of point occurrences are revealed by each function.

The purpose of the F-function, also known as the Pair Correlation Function, is to quantify the likelihood of locating a point at a specific distance from a reference point in comparison to a random distribution. Its application is in the analysis of the spatial clustering or dispersion of point occurrences in data [31,32,33,34]:

$$\mathrm{F}\left(\mathrm{r}\right)={\displaystyle \frac{1}{n\lambda }}{\sum }_i{\sum }_{j\ne i}I\left(‖{\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}‖\le \mathrm{r}\right)$$

(1)

The G-function, which is also known as Ripley’s K-function, is responsible for evaluating the spatial clustering or dispersion of point events within a defined range of distances. It contrasts the actual point distribution with what would typically be anticipated under a fully random distribution. This function is valuable for identifying clustering trends or spatial uniformity in point arrangements [33,35,36]:

$$\mathrm{G}\left(\mathrm{r}\right)={\displaystyle \frac{1}{{\mathrm{n}}^2\mathsf{\lambda }}}{\sum }_{\mathrm{i}}{\sum }_{\mathrm{j}\ne \mathrm{i}}\mathrm{I}\left(‖{\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}‖\le \mathrm{r}\right)$$

(2)

Ripley’s L-function, or the K-function, counts the average number of points that are on average close to each other. It assesses whether point occurrences show clustering or dispersion, or if they are distributed uniformly [35,37,38,39]:

$$K\left(\mathrm{r}\right)={\displaystyle \frac{1}{\mathrm{n}}}{\sum }_{\mathrm{i}}{\displaystyle \frac{1}{\mathsf{\lambda }}}{\sum }_{\mathrm{j}\ne \mathrm{i}}\mathrm{I}\left(‖{\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}‖\le \mathrm{r}\right)$$

(3)

$$\mathrm{L}\left(\mathrm{r}\right)=\sqrt{{\displaystyle \frac{G\left(r\right)}{\pi }}}-\mathrm{r}$$

(4)

where:

F(r) is the F-function at distance r; G(r) is the G-function; K(r) is the K-function; L(r) is the L-function; n is the number of points; λ is the intensity (average density) of the point pattern; xi and xj are the positions of points i and j, respectively; I(∣∣xi−xj∣∣ ≤ r) is the indicator function, which equals 1 if the distance between points i and j is less than or equal to r, and 0 otherwise.

Using a limited number of data points, kernel density estimation is a non-parametric technique for estimating the probability density function of a continuous random variable. From point data, it computes a smoothed density surface that can be seen as a continuous surface that shows the density of occurrences over space [40,41]:

$$\widehat{\mathrm{f}}\left(\mathrm{x}\right)={\displaystyle \frac{1}{\mathrm{n}{\mathrm{h}}^2}}{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\mathrm{K}\left({\displaystyle \frac{\mathrm{x}-{\mathrm{x}}_{\mathrm{i}}}{\mathrm{h}}}\right)$$

(5)

where:

the number of points is n; the bandwidth (smoothing parameter) is h; the data points are xi; the Kernel function is K; and the predicted density at position x is denoted by f (x).

Moran’s I is employed in spatial statistics for evaluating spatial autocorrelation, which pertains to the extent of correlation among the values of a variable within a spatial context. This metric determines if comparable values exhibit a tendency to aggregate (positive spatial autocorrelation) or if divergent values tend to be spread apart from each other (negative spatial autocorrelation) [42].

$$\mathrm{I}={\displaystyle \frac{\mathrm{n}}{\mathrm{W}}}{\displaystyle \frac{{\sum }_{\mathrm{i}}{\sum }_{\mathrm{j}}{\mathrm{w}}_{\mathrm{i}\mathrm{j}\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)\left({\mathrm{x}}_{\mathrm{j}}-\overline{\mathrm{x}}\right)}}{{\sum }_{\mathrm{i}}{\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)}^2}}$$

(6)

where: n is the number of observations; W is the sum of spatial weights; xi and xj are the values of the variable at locations i and j, respectively; $\overline{\mathrm{x}}$ is the mean of the variable values; and wij is the spatial weight between locations i and j.

Moran’s p-value serves as an indicator of the statistical significance associated with Moran’s I statistic, elucidating whether the spatial autocorrelation observed holds significance. The determination of whether the spatial pattern observed is likely to have arisen solely by random chance is derived from a small p-value (commonly below 0.05), thus implying the existence of spatial autocorrelation. The p-value linked to Moran’s I serves as an indicator of the statistical significance of spatial autocorrelation. It evaluates whether the spatial arrangement observed deviates significantly from what could be anticipated under spatial randomness. A p-value below 0.05 implies a statistically significant spatial arrangement, pointing towards the existence of spatial autocorrelation [42,43,44].

3. Results

3.1. Spatial Analysis

In the present study, spatial point analysis was conducted utilising the distance method. The nearest-neighbour spatial association measure assesses the spatial closeness among the sets of points about radio occultation events (ROEs) within the specified research area. The cross F-function, G-function, K-function, and L-function exhibit greater flexibility and are commonly applied across diverse academic disciplines [44,45,46,47,48]. In this particular section, the spatial examination of the CHAMP mission is depicted in Figure 3.

The data displayed in Figure 3 reveals that the K-, L-, G-,and F-functions demonstrate values below 1.0 throughout the 0 to 200 km radius range, aiming to examine the clustered radio occultation event (ROE). Moreover, they exhibit values exceeding 10,000 km between the ROE across Africa, which signifies a sparse collocation of the ROE. The proximity of the nearest neighbour unveils the presence of over 500 similar points close to each other within the defined study area, indicating a dense distribution of ROE points within a radius less than 1 km. The examination of the Kernel density function output yields a Moran’s value of 0.661 and a corresponding p-value of 0.001. This outcome implies a statistically notable spatial pattern along with positive spatial autocorrelation.

The data illustrated in Figure 4 discloses that the K-, L-, G-, and F-functions showcase values below 1.0 across the 0 to 400 km radius range, with the objective of analysing the clustered ROE. Furthermore, they demonstrate values surpassing 10,000 km between the ROE throughout Africa, indicating a sparse collocation of the ROE. The proximity of the nearest neighbour uncovers the existence of approximately 250 similar points in close proximity within the designated study area, suggesting a dense distribution of ROE points within a radius of less than 1 km, with a generally sparse distribution of the ROE across the study area. The analysis of the Kernel density function output produces a Moran’s value of 0.575 and a corresponding p-value of 0.001. This result suggests a statistically significant spatial pattern accompanied by positive spatial autocorrelation.

Based on the data illustrated in Figure 5, it is evident that the functions K-, L-, G-, and F demonstrate values approximately around 0.5 and below 0.5 within the radius range of 0 to 400 km, which represents the furthest distance between the ROE across Africa. The proximity of the nearest neighbour indicates the presence of over 200 clustered points within the specified study area, suggesting a dense distribution of ROE points within a radius of less than 1 km and a sparse distribution across the study region. The examination of the Kernel density function yields a Moran’s value of 0.751 with a corresponding p-value of 0.001, indicating a statistically significant spatial arrangement and the existence of positive spatial autocorrelation.

From the data presented in Figure 6, it can be observed that the K-, L-, G-, and F-functions exhibit values below 1.0 across the 0 to 40 km radius range, representing the maximum distance between the ROE over Africa. The proximity of the nearest neighbour reveals the existence of more than 250 clustered points within the designated study area, indicating a concentrated distribution of ROE points within a radius of less than 1 km. The analysis of the Kernel density function results in a Moran’s value of 0.511 with a corresponding p-value of 0.001, suggesting a statistically significant spatial pattern and the presence of positive spatial autocorrelation.

From the data presented in Figure 7, it is observed that the K-function performs at about 1.0, while the L-, G-, and F-functions exhibit values below the threshold of 1.0 within the range of 0 to 1 km radius for ROE points across the African region. The proximity analysis of the nearest neighbour reveals that more than 150 points are densely clustered the distribution of the ROEs as seen on (g) in the study area, indicating a high level of spatial aggregation among the ROE points within a radius of less than 1 km. Furthermore, the analysis of the Kernel density function demonstrates a Moran’s value of 0.440 with a corresponding p-value of 0.001, suggesting a statistically significant spatial pattern and confirming the existence of fairly positive spatial autocorrelation in the dataset.

3.2. Temporal Analysis

In this particular section, an analysis was conducted on the duration of data availability pertaining to the GNSS RO missions that were utilized in the research, with reference to the designated study period. The data availability for the missions selected in this study is illustrated in Figure 8.

Figure 8 depicts the duration of GNSS RO missions in relation to their launch year, decommissioning, and their specified study time limit. TERRASAR-X was launched in 2008 and remains operational to this day. CHAMP was launched in 2001 and ceased operations in 2008. GRACE was active from 2007 to 2017, METOP-A from 2007 to 2021, METOP-B from 2013 to 2023, METOP-C from 2019 to present, COSMIC-I from 2007 to 2019, and COSMIC-II from 2020 to present.

3.3. Seasonal Variation

Seasonal fluctuations are defined as the anticipated changes in diverse phenomena that arise due to the shifting seasons over the course of the year. These fluctuations have the potential to impact a broad spectrum of elements, encompassing meteorological trends, economic operations, consumer tendencies, agricultural practices, and even individual well-being and physical condition [49,50,51,52]. Figure 9, Figure 10, Figure 11, Figure 12, Figure 13 and Figure 14 illustrate the seasonal variations from 2001 to 2023, with each figure containing four years seasonal variation.

Figure 9 demonstrates the four years seasonal variation from 2001 to 2004 row-wise and the seasons column-wise (the subplots/subfigures through the rows represent the years from 2001 to 2004 and the subplots/subfigures through the columns represent the seasons). The initiation of the CHAMP mission in May 2001 has resulted in a lack of data availability for the winter, as depicted in the first row of Figure 9 (2001). During the spring season, temperatures range from −15 °C to +15 °C. Particularly, the regions of northwest Algeria, the southern part of Morocco, and Swaziland exhibit the lowest temperatures of about −30 °C in this period of the season. Moving on to the summer season, temperatures fluctuate between −20 °C and +20 °C, with notably lower temperatures of about −30 °C observed in the northeast of Libya and the southern part of South Africa. As we transition to autumn, temperatures vary between −30 °C and 30 °C, respectively. The second row of Figure 9 (2002) shows the temperature range between −45 °C to +30 °C for the winter most of West Africa shows significantly higher temperatures in this season. Spring displays temperatures between −40 °C and +30 °C, summer displays temperatures between −30 °C and +30 °C, and autumn displays temperatures between −45 °C and +30 °C, respectively. The year 2003 depicts the seasonal fluctuations in temperature, ranging from −60 °C to +30 °C in winter, from −30 °C to +30 °C in spring, from −45 °C to +30 °C in summer, and a temperature span of −45 to +30 °C in autumn. The year 2004 illustrates the seasonal fluctuations in temperature during the year 2004, ranging from −45 °C to +30 °C in winter, −40 °C to +30 °C in spring, and −45 °C to +30 °C in summer and autumn, respectively.

Figure 10 illustrates the temperature variations during different seasons from 2005 to 2008, respectively. the years are shown row-wise and the seasons column-wise (the subplots/subfigures through the rows represent the years from 2005 to 2008 and the subplots/subfigures through the columns represent the seasons). In the year 2005, winter season temperatures ranged from −60 °C to +30 °C, with certain regions in central Africa experiencing even lower temperatures of approximately −60 °C to −75 °C. During spring, the temperature range is between −45 °C to +30 °C, with temperatures dropping to about −75 °C around the Gulf of Guinea. Similarly, in the summer season, temperatures vary from −45 °C to +30 °C, with some areas in Burkina Faso and Mozambique recording temperatures as low as −75 °C. Lastly, in autumn, temperatures range between −45 °C to +30 °C, respectively. From the seasonal variations in the year 2006, it can be observed that the temperature during the winter season varies from −45 °C to +30 °C, reaching a minimum of −75 °C in areas such as Ivory Coast and Togo. In both the spring and summer seasons, temperatures fluctuate between −30 °C to +30 °C, peaking in regions like the central part of Angola, the northern part of Egypt, and the western part of the Democratic Republic of Congo during the summer. As for autumn, temperatures range from −15 °C to +30 °C, with some areas in west and central Africa experiencing higher temperatures. From the variations in the year 2007, the winter season illustrates a spectrum of temperatures ranging from −45 °C to +15 °C, while the spring spans from −30 °C to +30 °C. Similarly, the summer varies between −45 °C to 15 °C, and the autumn between −30 °C to +30 °C. Specifically, the autumn of 2007 exhibits elevated temperature readings in the northwest of Niger Republic, the southern region of Algeria, Ghana, Ivory Coast, and certain areas of the Democratic Republic of Congo, Sudan, and Uganda. Seasonal temperature fluctuations can be seen in the year 2008. During winter, temperatures range from −45 °C to +15 °C, with the coldest recorded in the northern region of Africa. Spring temperatures vary from −20 °C to +40 °C, while summer temperatures range from −30 °C to +30 °C, and autumn temperatures range from −45 °C to +30 °C.

Figure 11 shows the four-year seasonal variation from 2009 to 2012 with the years row-wise and the seasons column-wise (the subplots/subfigures through the rows represent the years from 2009 to 2012 and the subplots/subfigures through the columns represent the seasons). In 2009, the seasonal variations illustrate the seasonal temperature variations depicting winter temperatures ranging from −40 °C to +20 °C, spring temperatures from −25 °C to +30 °C, summer temperatures from −20 °C to +40 °C, and autumn temperatures from −25 °C to +25 °C. In the year 2010, the temperature seasonal variations are illustrated in the winter ranging from −45 °C to +15 °C, spring from −40 °C to +40 °C, summer from −40 °C to +20 °C, and autumn from −30 °C to +15 °C, respectively. The third row of Figure 11 demonstrates the seasonal temperature fluctuations of the year 2011. During winter, temperatures fluctuate between −45 °C to +15 °C, while in spring they range from −30 °C to +30 °C. Summer temperatures vary between −30 °C to +20 °C, and in autumn they range from −40 °C to +15 °C. The fourth row of Figure 11 illustrates the seasonal temperature fluctuations in the year 2012. During winter, temperatures span from −45 °C to +15 °C, while in spring they range from −40 °C to +20 °C. Summer temperatures vary from −30 °C to +30 °C, and autumn temperatures range between −45 °C to +15 °C.

Figure 12 illustrates the seasonal temperature fluctuations from the year 2013 to 2016, having the years row-wise and the seasons column-wise (the subplots/subfigures through the rows represent the years from 2013 to 2016 and the subplots/subfigures through the columns represent the seasons). In the year 2013, during the winter, temperatures ranged from −45 °C to +15 °C, while in spring they varied between −40 °C to +40 °C. Summer temperatures fluctuate between −30 °C to +30 °C, and autumn temperatures range from −30 °C to +15 °C. The year 2014 demonstrates the seasonal temperature fluctuations of four different seasons. During winter, temperatures fluctuate between −45 °C and +15 °C, while in spring they range from −30 °C to +45 °C. Summer temperatures vary from −40 °C to +30 °C, and autumn temperatures range between −45 °C and +15 °C. The seasonal temperature variations for the year 2015 are also presented in Figure 12, with temperatures varying from −30 °C to +35 °C in the spring and from −45 °C to +20 °C in the winter. Autumn temperatures range from −30 °C to +25 °C, while summer temperatures range from −30 °C to +30 °C. The seasonal temperature variations for the year 2016 are also shown in Figure 12. Temperatures vary from −40 °C to +30 °C in the winter and from −30 °C to +35 °C in the spring. Autumn temperatures range from −25 °C to +30 °C, while summer temperatures range from −40 °C to +30 °C.

The seasonal temperature variations from 2017 to 2020 are shown in Figure 13, the years are presented row-wise and the seasons column-wise (the subplots/subfigures through the rows represent the years from 2017 to 2020 and the subplots/subfigures through the columns represent the seasons). In the year 2017, temperatures varied from −30 °C to +40 °C in the spring and from −45 °C to +30 °C in the winter. Autumn temperatures range between −30 °C and +30 °C, while summer temperatures range between −40 °C and +30 °C. The seasonal temperature variations for the year 2018 are also presented in this figure, with temperatures varying from −40 °C to +40 °C in the spring and from −45 °C to +30 °C in the winter. Autumn temperatures range from −30 °C to +30 °C, while summer temperatures range from −45 °C to +30 °C. The seasonal temperature variations for 2019 are shown; temperatures vary from −25 °C to +30 °C in the spring and from −45 °C to +20 °C in the winter. Autumn temperatures range from −30 °C to +25 °C, while summer temperatures range from −20 °C to +35 °C. The year 2020 seasonal temperature variations depicted temperatures that vary from −40 °C to +30 °C in the spring and from −40 °C to +35 °C in the winter. Autumn temperatures range from −40 °C to +25 °C, while summer temperatures range from −40 °C to +30 °C.

Figure 14 shows the four-year seasonal variation from 2021 to 2023. In this figure, the years are presented row-wise and the seasons column-wise (the subplots/subfigures through the rows represent the years from 2021 to 2023, and the subplots/subfigures through the columns represent the seasons). The seasonal temperature variations for 2021 depict temperatures that vary from −40 °C to +35 °C in the spring and from −45 °C to +20 °C in the winter. Autumn temperatures range from −45 °C to +30 °C, while summer temperatures range from −45 °C to +25 °C. In the year 2022 during winter, temperatures ranged from −60 °C to +30 °C, with elevated temperatures observed in the southwest borders of Niger, Benin, and Burkina Faso, as well as along the northern sea boundary of Libya and parts of the Algeria–Morocco borders. Conversely, lower temperatures were recorded in the southern regions of Morocco and the eastern part of Ethiopia, extending to the western part of Somalia. Spring showcases temperatures ranging from −30 °C to +60 °C, with the coldest readings noted in central Morocco, central Mali, and certain areas of Zambia, while higher temperatures are evident in some parts of southern Tanzania. Summer temperatures vary between −30 °C to +40 °C, with cooler temperatures prevailing in central DRC. Lastly, autumn experiences temperature ranges from −30 °C to +30 °C, respectively. The year 2023 presented a result with only three seasons (winter, spring, and summer) due to the research timeline which is from May 2001 to May 2023 with an extension of two months (June and July) to complete the summer season for better analysis. During the winter period, temperatures fluctuate between −45 °C and +30 °C, while in spring they range from −35 °C to +35 °C. Furthermore, summer temperatures vary between −30 °C and +30 °C.

3.4. Heat Map

This section presents heatmaps for the years 2001, 2011, and 2021, respectively. The section shows temperature variations across different areas within the African continent.

Figure 15 illustrates the monthly heatmap on Africa from May to December 2001; eight plots were presented in this figure due to the fact that the CHAMP RO dataset begins in the month of May 2001 [(A) May, (B) June, (C) July, (D) August, (E) September, (F) October, (G) November, and (H) December, respectively], utilising GNSSRO data that is accessible for the aforementioned year. The regions characterised by darker shades in the figure denote lower temperature values, indicating a prevalence of reduced temperatures in certain areas of Africa between September and December, while the regions depicted in brighter or lighter hues signify elevated temperature values, reflecting an occurrence of higher temperatures in various parts of Africa from May to August, respectively. The visualisation architecture of the heatmap is attributable to the spatial resolution inherent in the dataset procured during the year 2001 from the CHAMP GNSSRO mission satellite, which represents the sole mission that yielded datasets in the year 2001.

Figure 16 illustrates the thermal map for the year 2011 throughout the African continent, revealing elevated temperature patterns across the region, while certain areas within specific countries exhibit comparatively lower temperatures. The COSMIC mission dataset was utilised for the year 2011, which possesses superior spatial and temporal resolution in comparison to the CHAMP mission.

The illustration in Figure 17 delineates the heatmap for the year 2021, which elucidates the temperature distribution throughout the African continent. The figure exhibits the average temperature across the continent, with certain regions within specific nations exhibiting diminished temperature values. The COSMIC-II mission dataset was used in this section.

3.5. Trend Analysis

This section presents an extensive analysis employing the Mann-Kendell and Sen’s slope trend analysis, which is predicated on the annual and monthly mean temperatures across the African continent. An examination of the temperature trend, accompanied by a confidence band representing a 95% confidence interval based on the annual mean temperature, was similarly utilised; additionally, the temperature residuals derived from the mean yearly temperature are illustrated in Figure 18.

Figure 18 present annual average temperature trend analysis using Mann-Kendelltrend analysis which indicates an increasing trend with the Mann-Kendell p-value: 0.007 and Sen’s slope of 0.059 °C/year, respectively

Figure 19 illustrates the analytical trend of temperature alongside a 95% confidence interval of ±79.01 °C over a comprehensive duration of 22 years; this analysis further reveals a significant increase in the p-value of 0.007 and Sen’s slope, which suggests a progressive warming rate of 0.059 °C per annum, respectively. Moreover, the analysis unveiled the temperature residuals in relation to the annual temperature trajectory, with the year 2020 exhibiting the highest residual value of 1.57 °C, while 2019 recorded the lowest residual value of −0.528, and the year 2012 demonstrated a residual value of 0.000.

Figure 20 illustrates the monthly temperature time series seasonal Mann-Kendall trend analysis, wherein the blue line denotes the mean monthly temperature and the red line represents the twelve (12) month rolling average. This seasonal Mann-Kendalltrend analysis indicates a significant trend (not attributable to randomness) with a p-value of 0.000, which is less than the threshold of 0.05.

Figure 21 elucidates the analysis of monthly temperature trends, which was conducted by examining identical months across a span of 22 years (for instance, the January data from 2002 through 2023, subsequently followed by February, March, and concluding with December). It is worth noting that the data for the year 2001 commences in May, while for the year 2023, it begins in July. The examination of Sen’s slope indicates a progressive increase in temperature for each month over the 22-year period, annually. Specifically, January exhibits a warming trend of 0.064 °C/year, February shows an increase of 0.072 °C/year, March reflects a rise of 0.051 °C/year, April experiences a warming of 0.068 °C/year, May demonstrates an increase of 0.070 °C/year, June records a rise of 0.086 °C/year, July reveals an increase of 0.057 °C/year, both August and September show a warming trend of 0.081 °C/year each, October reflects a rise of 0.071 °C/year, November indicates an increase of 0.050 °C/year, and December concludes with a warming trend of 0.070 °C/year, respectively.

4. Conclusions

This study aims to consider employing the METOP and COSMIC missions for enhanced research outcomes in forthcoming investigations. From the spatial analysis of the radio occultation event (ROE) conducted during Remote Sensing missions, it is evident that METOP exhibits a commendable spatial distribution, followed by COSMIC-I showing an improved spatial pattern, with; COSMIC-II displaying the most optimal spatial arrangement. These findings suggest a notable enhancement in the spatial distribution of ROE data in the subsequent missions compared to the initial ones.

In the temporal examination of the radio occultation (RO) missions, namely CHAMP, COSMIC-I, and COSMIC-II, these missions have furnished data spanning from May 2001 to May 2023 for this study. Conversely, other missions such as GRACE, METOP, and TERRASAR-X were leveraged to fill the data gaps during the research timeframe. Furthermore, an assessment of seasonal fluctuations within the study duration was also conducted. The results of this research correspond with the conclusions drawn from earlier investigations [53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73]. Radio occultation techniques exhibited exceptional spatial and temporal resolution over Radiosonde techniques this further substantiates its pre-eminence (in relation to higher spatial and temporal accuracy, superior vertical and horizontal resolution, capability to operate under diverse weather conditions, and self-calibration) in comparison to alternative techniques. COSMIC-2 exhibits the most advantageous spatial and temporal coverage relative to other missions [53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73].

5. Recommendations

In the present study, it was determined that the CHAMP mission offers a favourable temporal resolution alongside a limited spatial resolution. The METOP missions, conversely, exhibit commendable spatial and temporal resolutions, while the COSMIC missions boast the most superior spatial and temporal resolution. Consequently, based on these findings, it is suggested that the METOP and COSMIC missions be considered and employed for enhanced research outcomes in future investigations.