W210M-Range Wind Speed at 10m Range. WCRP . WD10M Wind Direction at 10m. WS10M Wind Speed at 10m. WS50M Wind Speed at 50m. The effects of high temperatures due to global warming in various parts of the world has raised great concerns. This study, therefore, investigated the long-term temperature trends in four major cities representing different climatic zones in Ghana. Using data from NASA's Prediction of Worldwide Energy Resource (POWER) project, comprehensive statistical analyses were conducted to assess local climate warming and its implications for the region. The study employed various techniques, including linear regression trend analysis and machine learning via the eXtreme Gradient Boosting (XG-Boost) approach to train, test and predict temperature variations. Furthermore, to visualize temperature patterns, Land Surface Temperature (LST) profile maps were generated from the RSLab platform, which provided enhanced accuracy of 1.52 deg C compared to traditional methods. The results, revealed local warming trends in Accra, Kumasi, and Kete-Krachi. Accra, being heavily industrialized, experienced the highest temperature variability among the cities. Demographic factors were not significant in explaining temperature trends in these cities. The XGBoost model demonstrated good performance, as evidenced by low (RMSE) scores, indicating its effectiveness in capturing temperature patterns over a span of several decades. Based on mid-2023 projections, Wa unexpectedly had the highest mean temperature among the four cities. The estimated mean temperatures for Accra, Kumasi, Kete-Krachi, and Wa in the mid-year 2023 are 27.86 deg C, 27.15 deg C, 29.39 deg C, and 30.76 deg C, respectively. These findings have contributed to a better understanding of local climate warming in Ghana's major cities within a short time projection. The XGBoost model with the predictions made will offer valuable insights for policymakers and local communities, enabling the formulation of effective climate change adaptation and mitigation strategies for sustainable cities. Dating back to the pre-industrial ages, Jean-Baptiste Joseph Fourier suggested that the atmosphere of the Earth serves as an envelope of Greeenhouse Gases (GHGs) with a temperature that is increasing (Fourier, 1824). Today it is known from several sources including the Intergovernmental Panel on Climate Change (IPCC) that the warming of the Earth's climate system is unequivocal. The IPCC, since its establishment in the 1990s, has released a series of and other products (Arias & et al., 2021). Climate according to Sorokhtin et al. (2007) is a set of long-term patterns of changes in the state of the atmosphere with the possible atmospheric state parameters such as pressure, humidity and others. This study focuses on temperature. All of the atmospheric state parameters are directly or indirectly being influenced by man. Usually, global warming which results from climate change is quantified through the average increase in the global surface temperature of the Earth (Shaftel et al., 2021). This variable is particularly important for climate policies following the framing of the Paris Agreement in terms of ambitions to stay below specific levels of the (GSAT) relative to pre-industrial climate. The prospects of this have, therefore, been constrained by the stabilization of Carbon dioxide at concentrations around 450, 650, and 1000 ppm which governs the seasonal cycle of temperature within the atmosphere (Lindvall et al., 1999). Two seasonal cycles can be considered in this case, (1) a component due to direct solar absorption in the atmosphere; and (2) a component due to the flux of energy from the surface to the atmosphere. The POWER project derives its data primarily from NASA's (WCRP) / Global Energy and Water Cycle Experiment (GEWEX), Surface Radiation Budget (SRB) project (Version 2.9) and the Global Modeling and Assimilation Office (GMAO) (GEOS) assimilation model (V 4.0.3) V (Zhang et al., 2007), -whose original version came from a finite-volume Gen-eral Circulation Model (fvGCM) which are valuable for the investigation of various climate phenomena, such as El Nino events, monsoons, and climate variability on different time scales. fvGCM has a Physical-space Statistical Analysis System (PSAS). This new system with its Physics was developed at the Data Assimilation Office, NASA (GSFC) Kiehl et al. (1985Kiehl et al. ( , 1998)). NASA's POWER project offers a valuable resource for studying climate-related variables, including surface air temperature, solar radiation, and other meteorological parameters, across different regions of the globe. By leveraging the comprehensive and reliable data provided by POWER, this research aims to assess the long-term temperature trends in Ghana's major cities and analyze the extent of global warming within these urban environments. The primary objective of this study is to conduct a thorough statistical analysis of the air temperature data collected by NASA's POWER project over a span of three decades. By utilizing advanced statistical techniques, such as regression trend analysis, and time series modeling Stocker et al. (2013) we will iteratively train and evaluate these data via eXtreme Gradient Boosting (XGBoost) an ML approach using an ensemble of weak decision trees, where each new tree is built to correct the errors made by the previous trees (). These findings will identify and quantify the long-term temperature trends in Ghana's major cities which hold significant implications for both policymakers and local communities in the various climatic zones in Ghana hence formulating effective climate change adaptation and mitigation strategies. This can also inform urban planning and infrastructure development, allowing for more resilient and sustainable cities that can withstand the challenges posed by climate change. Global warming is an urgent issue. Due to this, many great sustainable solutions have been designed to mitigate its impacts. In Africa, most studies have applied in-situ data in solving the problem with the POWER data remaining highly unused. Outside the continent <empty citation> have either compare their data with POWER or used POWER as a supplement to their studies. We deem it necessary to put in the effort to validate some of the finding done in Africa specifically in Ghana on the same issue using a worldwide database like POWER. This study therefore analyses temperature Variations in Ghana with the NASA's POWER project, hence Local Warming in its Major Cities located in different climatic zones.
Accra is located in the Coastal Zone, Kumasi is located in the Forest Zone, Kete-Krachi is located in the Transition Zone and Wa is located in the Savannah Zone. The aim of the study is: To study temperature variations over four climatic zones in Ghana using the Prediction of Worldwide Energy Resources (POWER) Data. The specific objectives of the study include the following;  To use Statistical Analysis (LR, F-Test etc.) of POWER data to study the seasonal variations of temperature changes over the 4 climatic zones in Ghana. To use Landsat data to observe the temperature profile of the hottest years on record of the study area within the study period. To compare the POWER data with Landsat-derived LST profile maps. To predict using the POWER data the temperature variations over the 4 Climatic zones. 0.5 Literature Review Bessah et al. (2022) conducted periodic climate zoning in Ghana based on rainfall, relative humidity, maximum and minimum temperatures. The study adopted cluster and PCA analysis methods, leading to the classification of Ghana into Savannah, Forest, and Coastal zones based on assessed parameters. Here, a transition zone between the Savannah and the forest zone has been added.However, following the assessment of solar radiation resources from the NASA-POWER reanalysis products (Chandler et al., 2013) for tropical climates in Ghana, Quansah et al. (2022) showed that it is possible to expand the clean energy industry in Ghana by comparing 22 on-site measurements of sunlight radiation with that of POWER for long term reference. Basically, they created a detailed database of solar radiation and related parameters, focusing on short time intervals. This involves gaining a deep understanding of how solar radiation is distributed and correlated in different regions and time periods. Additionally, accurate forecasts were developed to determine the performance of solar power systems and hence assessed the technical impact of variations in solar radiation on national electric grids. By utilizing POWER for 'Solar City' identification in Ghana, recent indications via data from the Ghana Meteorological Agency (GMET), have provided some evidence of a general quite significant soaring of temperatures in Ghana leading to Local Warming, from about 1945 up to 1990 and hence future investigations called for (Stephens, 1995). In accordance with the second World Climate Conference held in Geneva, 1990, which advocated sustained research on global climate system changes and short-term regional analyses, Klutse et al. (2020) focused on projected temperature increases in Northern Ghana from 1980 to 2014. (RCMs) and General Circulation Models (GCMs) to downscale climate projections from GMET data, they found significant variations in minimum air temperature: 0.5 o C under RCP 2.6 by 2080 and 2.5 o C under RCP 8.5 for all stations. Along with global assessments, the study suggests that global warming is on the rise (). Statistical analysis has, however, been carried out to assess different studies. Some of which include accurate models to estimate global solar radiation which includes mean bias error (Jordan Filho, 1971), root mean square error, mean absolute percentage error, the coefficient of determination and uncertainty at 95%. Also, linear and non-linear regression models such as; Angstrom-Prescott model and Duffy and Beckman (Duffie et al., 1994) while (Kaplanis, 2006) used a pocket calculator model (Jains model and a proposed S. Kaplanis model) to predict hourly global solar rays that were latter compared with measured values of this parameter over two biggest cities in Greece. Other known statistical methods proposed by (Collares-Pereira & Rabl, 1979) showed that these statistics can be used to calculate the heating and cooling loads of buildings. A service life model was, however, developed by (Talukdar & Banthia, 2013) to evaluate the effects of climate change on these concrete infrastructures. After the development of models, four categories of statistical indicators could be used to assess the performance of the developed model (Gueymard, 2014). This includes Class A-The indicators of the error of individual points, the ideal value is zero for a perfect model. Class B-The indicators of overall performance, the ideal value is one for a perfect model. Class C-The indicators of distribution similitude. Class D -The qualitative indicators (Lindvall et al., 1999). Results of statistical modeling and predictions would later be of great importance to the World Bank's G5 Sahel report showing that the number of days with temperature above 35 degrees Celsius, considered the wet-bulb global temperature upper survivable limit for human beings, will soar to more than 40 per year by 2050. A consequence of these temperatures will be heat-induced stress and mortality, and the heat will affect the vulnerable, outdoor laborers, and poverty-affected populations the most. Though statistics are not a Wunderwaffe (Von Storch, 1999) to extract a wealth of information from a limited sample of observations they serve as an indispensable tool in the evaluation of limited empirical evidence. According to (Katz, 2010) upcoming challenges involved in developing statistical models to accurately capture and understand the characteristics of complex climate extreme events, such as heatwave temperature variability would need to consider the spatial dependence for numerical models for preferred output. This is suggested because of the sparsity of data characterized by in situ meteorological measurement devices and or setups. In both cases statistical tests to evaluate our models as well as map visualizations were conducted via the following software: Jupyter Notebook is the first to mention here. It served as an open-source web application that enabled the creation and sharing of documents containing live code, visualizations, and narrative text (). It provided an interactive computing environment where codes were executed in cells, allowing for an iterative and exploratory approach to POWER analysis. Furthermore, Jupyter Notebook served as a versatile platform for data manipulation, statistical analysis, and model development. The integration of code cells with explanatory text and visualizations in a single document facilitated the reproducibility and documentation of the research process which were later shared via Git. ArcMap by Chockalingam et al. (2015), served as a powerful geographic information system (GIS) software developed by (ESRI).
In this study, It provided a comprehensive set of tools and functionalities for analyzing, visualizing, and managing our geospatial data with its 10.7.1 version. It allowed for the manipulation and visualization of various data layers of the satellite imageries, and shapefiles, enabling the creation of informative and visually appealing maps. Though ArcMap 10.7.1's extensive toolset facilitated tasks such as cartographic design of the LST maps, these were later discarded for reasonable purposes to the use of an alternative cloud-based LST platform (which uses Google Earth Engine (GEE) plug-in) that would be addressed later, enhancing the research's geospatial data exploration and interpretation capabilities. Generally, the workflow for this project followed the following steps and shown in Figure 3  Data was acquired from the NASA's POWER database  The data was pre-processed to check for empty spaces. The data was then converted into a csv file format that can be recognized by the jupyter notebook application software. The statistical analysis which includes ANOVA test and T-tests hypothesis were formulated based on the LRT analysis. Landsat data was then deployed from USGS for LST visualization on the RSlab global LST platform. The LRT analysis of POWER was then compared with the LST maps. For the variations observed so far, the 95% confidence interval and 95% prediction intervals were also analyzed given in The Analysis of Variance (ANOVA) (Heumann & Shalabh, 2016) test to further validate our findings was carried out for all cities based on the following: (1) Null Hypothesis (H0): The LRT model has no significant effect on explaining the variation in the Mean Temperature, and (2) Alternative Hypothesis (HA): The LRT model has a significant effect on explaining the variation in the mean temperature. In Table 1, the ANOVA test for the mean temperature variations over Accra is shown. The term (df) is the degrees of freedom, the term (sum sq) is the sum of squares, the term (mean sq) is the mean of squares. The p value being 6.498156 x 10 8 is much smaller than the arbitrarily-set target significance level of  = 0.05. Thus, the LRT model for Accra is further supported hence we reject the null hypothesis. The reported p value in all cases is (1 -CFD) for the F-distribution at the obtained f-value where CFD is the cumulative density function for the F-distribution, evaluated here with the first degree of freedom being 1 and the second degree of freedom being 39. The ANOVA table for Kumasi is in Understanding the significance of the b0 component and b1 component of all LRT models done so far was further carried out through the student test under the following hypothesis. (1) For b0, Null hypothesis (H0): The mean temperature in a specific year is equal to the mean temperature in a reference year (1980). Alternative hypothesis (HA), The mean temperature in a specific year is not equal to the mean temperature in a reference year. (2) For b1:Null hypothesis (H0): There is no linear relationship between the relative year and the mean temperature. Alternative hypothesis (HA): There is a linear relationship between the relative year and the mean temperature. The t-statistics, the upper bound and lower bound was used as an evaluation tool in this case. Table 5 shows the output of the T-test for all four cities. This results now validate the fact that, without reasonable doubt it is statistically certain that the city of Accra leads the local warming in Ghana among all four cities with t-statistical values of (395.22, 6.6502) followed by Kete-Krachi with t-statistics of (224.63, 1.9382) and then Kumasi to Wa in that order. Unlike the test of ANOVA, the high values gotten from the t-statistics for Kete-Krachi do validate now that Kete-Krachi is one of the rising warmest cities making us to confidently reject the first null hypothesis of ANOVA in favor of the alternative hypothesis for Kete-Krachi. The only problem now lies in Wa but kumasi being the third warmest city in the country could be ideal. Particularly for Wa, we have led to the proposition that the T2M variations observed may be influenced by population dynamics and its associated anthropogenic activities. To investigate this claim, an analysis was conducted on population census data spanning from 1984 to 2021 which falls within our study period for all four cities with the observed distribution map and table. These results shown in Figure 6 and Table 6, provide a concise interpretation of the findings. Despite the recent decrease in the population of Accra, temperatures are still of great variability. This then could be the result of the industrialization of this region which results in many releases of GHGs. Kumasi being in the forest zone and having a great decrease in her population by -78% has a well-explainable LRT model that perfectly ascertains our claims. Therefore, a decrease in local warming over Kumasi is ideal. Kete-Krachi experiencing local warming could be attributed to its increasing population. Despite the absence of previous census data for this region, the findings suggest that the current population of Kete-Krachi has a significant influence on her temperature above 2 meters in height hence its soaring data as compared to Accra. Wa's past and current population can be thought of as a neutral agent in explaining or having rather no effect on the temperature changes in the city, regardless of an increase or decrease as a constant increase is seen throughout these years. The accurate calculation and visualization of the LSTs using Landsat's Thermal Infrared Band in ArcGIS Desktop have posed challenges; due to the wide range of temperature values observed (ranging from tens to thousands of degrees Celsius), and the unavailability of data. To address this limitation and improve the LST profiles, an alternative approach was adopted by utilizing the estimator available on the RSLab platform, which specifically caters to Landsat 5, 7, and 8 data while considering only the top 3 warmest years (Parastatidis et al., 2017).
The RSLab platform leverages a Single Channel (SC) algorithm based on the radiative transfer equation in Equation 1, utilizing radiance-at-the-sensor data from a single band (). Other methods of LST retrieval from Landsat data include using the radiative transfer equation in-situ radiosounding data (Sobrino et al., 2004) and the mono-window algorithm developed by (Qin et al., 2001) (Kuhn, Johnson, et al., 2013). XGBoost is similar to the traditional Gradient Boosting Machine (GBM) framework but offers better performance by using a more regularized model formalization to control overfitting (T. Chen et al., 2022). It has become widely used by data scientists and has been employed in more than half of the winning solutions in machine learning challenges hosted at Kaggle (He, 2016). Here, the XGBoost algorithm fits a series of small decision trees in a sequential process, where each tree is adjusted to the residuals from the previous trees. Some parameters worth knowing for training this model are: maximum depth which controls the complexity of the boosted structure and helps prevent overfitting, the learning rate determines the contribution of each tree to the model and prevents overfitting by making the boosting process more conservative, the gamma parameter specifies the minimum loss reduction required to make further partitions on leaf nodes, column and observation sampling ratios can be used to subsample variables and observations for each tree, we assume the model would improve as it gets to be trained on subsequent city data therefore, the time limit served as a constraint to avoid bias. The results of prediction were done Weekly, Monthly, and only yearly for best-performing City-models. Because of its less performance, the weekly predictions for Accra yielded a (RMSE) of 3.582. This is illustrated by Figure 12 This was followed by Kumasi who had an RMSE value of 1.464 as shown in Figure 13 The second-best performer was Wa with an RMSE of 1.343 with predictions of T2M being done from 2019-03-01 to 2021-04-18. The XGBoost has therefore improved so well that it outperforms itself in the previous projections made for Kumasi and Accra. We illustrate the predictions for Wa in Figure 14 below. Though Wa has a huge unexplainable phenomenon causing constant decreasing temperatures it is confidently thought that with huge data from more atmospheric parameters, we can train and learn from these phenomena and better tell what factors contribute to the decreasing temperatures in Wa. Kete Krachi produced the best T2M predictions while giving off an RMSE of 0.9876. This is demonstrated in The mid-2023 prediction of mean temperature at 2 meters above the surface in these four cities is a confirmation of the projections by the XGBoost model verifying the better performance of Wa's temperature variability. This prediction of the constant high temperatures, particularly in Wa was not well explained by the LRT and though the LST showed a gradual change in this, there was always a high expectation that temperatures are significantly changing in Wa which could lead to local warming in the city. From Figure 16 the predictions of Wa which is Figure 16d indicates that in mid 2023, T2M is changing at a rate of 0.034  C / mid-year and the mean temperature is 30.76  C. Figure 16c indicates that the rate of change in T2M over Kete-Krachi in mid 2023 This confirms how temperature increase in geolocation could be influenced by the direct thermal energy coming from the land, water bodies, and other man-made features that are good absorbers and bad reflectors of thermal infrared energy. It is, therefore, important that, further research would focus on actually validating these results for a continental study that has been done with in-situ data by comparing it with that of POWER.