Browsing by Author "Khamala, Geoffrey W."

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Anomaly Detection in Selected Aerosol Optical Properties and Associated Climate Variables Using a Multivariate Hidden Markov Model: A Case Study over Kenya
(Open Access Library Journal, 2025-10-23) Wanjala, Dennis W.; Makokha, John Wanjala; Khamala, Geoffrey W.
Understanding aerosol climate interactions is crucial for monitoring atmospheric changes and supporting climate resilience efforts, particularly in vulnerable regions such as Kenya. This study applies a Multivariate Hidden Markov Model (HMM) to detect anomalies in key Aerosol Optical Properties (AOP) i.e., Aerosol Optical Depth (AOD), Single Scattering Albedo (SSA), and Ångström Exponent (AE) alongside associated climate variables; Surface Air Temperature (SAT) and Rainfall Rate (RR), over the period 2000-2022. Satellitebased datasets from MODIS, MERRA-2, and TRMM were used to derive monthly means, and descriptive statistics and linear regression were initially employed to characterize long-term variability. The objectives of this study were to examine the temporal and spatial variability of key aerosol and climate parameters over Kenya, detect and classify anomalies in the multivariate dataset using HMM and to interpret the climatic and environmental implications of detected anomalies and their possible causes. The HMM approach successfully identified temporal patterns and hidden states, enabling the detection of significant anomalous periods, particularly between 2010 and 2016, which aligned with regional biomass burning events and transboundary pollution episodes. Results indicate that AOD and SSA anomalies correspond with periods of elevated temperature and reduced rainfall, highlighting potential climate-aerosol feedbacks. The findings demonstrate the utility of multivariate HMMs in capturing the complex dynamics of aerosol-climate interactions and provide a foundation for improved air quality monitoring and climate impact assessments in Kenya which is critical for improving environmental monitoring and enhancing regional climate adaptation strategies.
Enhancing climate resilience: A data-driven north rift weather prediction system for real-time forecasting and agricultural decision support
(Heliyon, 2025-02-07) Makokha, John Wanjala; Barasa, Peter Wawire; Khamala, Geoffrey W.
This study presents the development and integration of predictive models for the Normalized Difference Vegetation Index (NDVI) and Bare Soil Index (BSI) using the XGBoost algorithm within the North Rift Weather Prediction System (NRWPS) to enhance ecosystem monitoring in Kenya’s North Rift region. Trained on a comprehensive dataset spanning 1995 to 2020, which includes precipitation (from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS)), temperature (TerraClimate), historical NDVI (Landsat 4–5 Thematic Mapper (from 1995 to 2013) and Landsat 7 Enhanced Thematic Mapper plus (ETM+) (from 2014 to 2020)), and BSI (SoilGrids) data, the models effectively capture the complex relationships between environmental factors and vegetation health. The BSI model achieved an MSE of 0.029, an MAE of 0.019, and an R-squared score of 0.93, while the NDVI model yielded an MSE of 0.002, an MAE of 0.024, and an R-squared score of 0.945. These results demonstrate the models’ strong predictive accuracy, enabling precise assessments of vegetation health and bare soil exposure. By analyzing temporal variations in vegetation health and land degradation from 1995 to 2020, the study identifies a significant inverse relationship between NDVI and BSI, where increasing bare soil exposure corresponds to declining vegetation health. The analysis also reveals that climatic factors particularly temperature (minimum and maximum) and precipitation play a critical role in shaping these trends, with high temperatures after 2000 associated with reduced NDVI, while regions with higher precipitation show healthier vegetation and lower BSI. The successful development of the NRWPS model provides significant opportunities for informing land management strategies, conservation efforts, and agricultural practices, enabling data-driven decision- making. Moreover, its integration into larger decision support systems allows for proactive interventions to mitigate land degradation and climate change stressors. This study emphasizes the importance of sustainable land-use practices and climate adaptation strategies to preserve vegetation health and manage ecosystem vulnerabilities effectively in the wake of regional climate change with the North Rift region most affected.
Long Term Characterization of Dust and Its Radiative Forcing over Kenya Using Satellite and Model-Based Data
(Atmospheric and Climate Sciences, 2025-09-02) Simiyu, Kizito W.; Khamala, Geoffrey W.; Makokha, John Wanjala
Dust aerosols play a critical role in atmospheric processes, influencing air quality, climate, and weather patterns through their interactions with radiation and cloud formation. This study aimed at characterizing the spatiotemporal distribution of dust and quantifying its radiative forcing over Kenya using a combination of satellite observations and model-based measurements. Multi-year datasets from MERRA-2 and MODIS were utilized to analyze dust loading and spatial variability. Additionally, radiative forcing (RF) derived from MERRA-2 and satellite observations was estimated to assess dust-induced changes in surface of-atmosphere (BOA), top-of-atmosphere (TOA) and within atmosphere (ATM). The findings on spatiotemporal variability of dust over Kenya, highlight high concentrations in northern regions during dry months and reductions during wet seasons (MAM and OND). While on particle size distribution, the analysis shows coarse-mode dominance in dry periods, depicting dominance of dust. On the other hand, dust mass concentrations peak in the northwest part of the study domain. Further, RF analysis indicates dust induces BOA and TOA cooling but atmospheric heating, with peak heating in June to July, local dry months. This study therefore recommends an enhanced integrated dust monitoring and modeling system, especially during dry seasons, to capture Dust AOD, size, and mass concentration. Further, targeted mitigation measures like afforestation, land-use planning and early warning systems should be prioritized to reduce dust emissions, improve climate model accuracy, and protect public health in vulnerable regions.
Long-Term Assessment of Deforestation and Its Impacts on Aerosol Optical Properties and Climate Variables over Mau Forest Complex Using Multisensory Data
(Atmospheric and Climate Sciences, 2025-09-17) Jepchirchir, Caroline M.; Khamala, Geoffrey W.; Makokha, John Wanjala
The deforestation has profound implications on aerosol properties and climatic variables. Deforestation disrupts local climate by altering temperature, aerosol optical properties and impacting air quality and modifies precipitation patterns; and degrades vegetation health. However, the long-term impacts of deforestation on aerosol optical properties and climate variables over Mau remain not very well investigated, especially considering the context of altered anthropogenic and natural emission sources. This study bridges this gap through a comprehensive assessment of deforestation impacts on aerosol optical properties and climate variables over Mau Forest complex bounded by (0.2S, 35.2E) and (0.8S, 35.8E) using multisensory data from 2001-2024. The findings by the present study reveal predominantly negative trends of NDVI, recorded by season JF, JJAS and OND of value −6.63032E−4 ± 0.00137, −1.356E−4 ± 0.00101 and −1.31586E−4 ± 7.59717E−4, respectively, indicating a decrease in vegetation health and density over the year often linked to rainfall patterns. Decline in NDVI is influenced by deforestation, which further exacerbates the impacts of natural reduction in vegetation cover. Conversely, during the season of MAM, the trend of NDVI is generally weak positive trend of value 4.70595E−4 ± 0.00193 year−1 indicating an increase in vegetation health and density. Furthermore, the spatial trends over domain region is characterized by Aerosol optical depth (<0.2) and high value of Angstrom exponent (>1) and moderate value >0.7, is attributed by 1) deforestation for example anthropogenic activities and human activities hence released significant amounts of aerosols particles into the atmosphere 2) climate change occasioned by meteorological parameters such as temperature inversions accompanied by reduced precipitation which are favorable conditions for increased aerosol emissions leading to the enhanced AOD. Correlation between NDVI and AOD is negative, attributed to increase in deforestation rate that results in reduced NDVI values. The statistically significant impacts of deforestation on aerosols optical properties and NDVI prove the modulating role of aerosol optical properties in regional climate processes. Policymakers must prioritize emission control actions targeted at biomass burning and scientists must keep investigating high-resolution aerosol optical properties, climate interactions using integrated ground and satellite observations to advance climate impact assessment over Mau Forest complex in Kenya.
Long‑term climatology and spatial trends of absorption, scattering, and total aerosol optical depths over East Africa during 2001–2019
(Environmental Science and Pollution Research, 2021-03-28) Khamala, Geoffrey W.; Makokha, John Wanjala; Boiyo, Richard; Kumar, Kanike Raghavendra; Kumar, Kanike Raghavendra
The unprecedented increase in anthropogenic activities, coupled with the prevailing climatic conditions, has increased the aerosol load over East Africa (EA). Given this, the present study examined the trends in total, absorption, scattering, and total aerosol extinction optical depth (TAOD, AAOD, SAOD, and TAEOD) over EA, alongside trends in single scattering albedo (SSA). For this purpose, the AOD of different optical properties retrieved from multiple sensors and the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) model between January 2001 to December 2019 were utilized to estimate trends and assess their statistical significance. The spatial patterns of seasonal mean AOD from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor and MERRA-2 model were generally characterized with high (>0.35) and low (<0.2) AOD centers over EA observed during the local dry and wet seasons, respectively. Also, the spatial trend analysis revealed a general increase in TAOD, being positive and significant over the arid and semi-arid zones of the northeastern part of EA, which is majorly dominated by locally derived dust. The local dry (wet) months generally experienced positive (negative) trends in TAOD, associated with seasonal cycles of rainfall. High and significant positive trends in AAOD were dominated over the study domain, attributed to an increased amount of biomass burning, variations in soil moisture, and changes in the rainfall pattern. The trends in TAEOD showed a distinct pattern, except over some months that depicted significant increasing trends attributed to changes in climatic conditions and anthropogenic activities. At last, the study domain exhibited decreasing trends in SSA, signifying strong absorption of direct solar radiation resulting in a warming effect. The study revealed patterns of trends in aerosol optical properties and forms the basis for further research in aerosols over EA.
Long-Term Merra-2 Characterization of Black Carbon’s Surface Mass Concentrations and Its Impact to Climate Change over East Africa
(Atmospheric and Climate Sciences, 2025-09-30) Kutoto, Jacob W.; Makokha, John Wanjala; Ekisa, Tom; Khamala, Geoffrey W.
Black carbon (BC), which is one of the short-lived climate forcers, largely influences the local modulation of the climate, particularly in regions that are sensitive such as East Africa. However, the long-term trends and meteorological impacts of BC in this region remain not very well investigated, especially considering the context of altered anthropogenic and natural emission sources. This study bridges this gap through a comprehensive spatio-temporal examination of BC surface mass concentration for East Africa from 1980 to 2023 using data from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). It has also established statistical correlation between BC concentrations and the selected meteorological parameters, i.e. , surface air temperature, specific humidity, surface wind speed, and total surface precipitation. Time-series analysis, spatial visualization, and Pearson correlation were applied to analyze the MERRA-2 datasets. Results showed pronounced intra- and inter-annual variability in BC distribution with high concentrations (>8 × 10−12 kg/m3) mostly over western Uganda and northwestern Kenya and Tanzania during boreal winter. Such space hotspots were linked to both local sources (biomass burning, automobile pollution) and long-range atmospheric transport from Asian and Middle Eastern industrial regions. The effect of natural sources such as West African bushfires and Saharan dust storms, was also reflected by transboundary dispersion patterns due to wind systems in operation. Correlation analysis found that surface wind speed showed a statistically significant negative correlation with BC concentrations during all seasons, particularly March-May (r = −0.57, R2 = 0.31) and June-August (r = −0.51, R2 = 0.24), indicating high winds favour BC dispersion. Specific humidity in addition to precipitation was moderately positively correlated with BC, particularly during the September-November season (r = 0.47, R2 = 0.20), showing complex interactions between atmospheric moisture and aerosol lifecycles. Surface air temperature was most strongly seasonally correlated with BC during the short rains (r = 0.55, R2 = 0.29), showing the two-way effect of BC on atmospheric warming and radiative forcing. In short, the investigation indicates that BC concentrations over East Africa exhibit distinct spatial and temporal patterns driven by both human and natural processes. The statistically significant correlations with meteorological parameters prove the modulating role of BC in regional climate processes. Policymakers must prioritize emission control actions targeted at biomass burning and urban pollution, and scientists must keep investigating high-resolution BCclimate interactions using integrated ground and satellite observations to advance climate impact assessment in East Africa.
Seasonal Variability in Aerosol Microphysical Properties over Selected Rural, Urban and Maritime Sites in Kenya
(Open Access Library Journal, 2018-10-24) Khamala, Geoffrey W.; Odhiambo, Jared O.; Makokha, John Wanjala
Atmospheric aerosols are posing a great threat to the already stressed climate with the effects being felt more on African continent. Their presence and interaction with the clouds contribute to the strongest uncertainty in aerosol characteristics and Earth’s energy budget hence; calling for a long term assessment to be done. The present study analyses long term spatiotemporal microphysical aerosol characteristics (namely: effective radius ( eff r ) and surface-area concentration), using AErosol RObotic NETwork (AERONET) framework over Kenyan urban atmosphere (Nairobi-1˚S, 36˚E), rural atmosphere (ICIPE-Mbita-0˚S, 34˚E) and maritime atmosphere (CRPSM-Malindi-2˚S, 40˚E). AERONET framework was used due to its availability over the selected sites; it is also located in sites that provided contrasting aerosols type, source and characteristics and due to its synergism with other frameworks. The findings indicated a spatial and temporal variability in microphysical properties over CRPSM-Malindi, Nairobi and ICIPE-Mbita. CRPSM-Malindi is dominated with coarse aerosols in all seasons while Nairobi with coarse mode in the DJF and MAM seasons. ICIPE-Mbita is on the other hand dominated with fine aerosols in all season. In terms of size distribution, the three AERONET sites displayed a bimodal distribution inflecting at 0.44 μm and fine mode radius of 0.15 μm while CRPSM-Malindi recorded a coarse mode of 3.86 μm and Nairobi and ICIPE-Mbita with 5.06 μm. The coarse aerosols have a higher concentration than the fine aerosols in all AERONET sites because of aerosol coagulation and dominance of certain type of aerosols that are coarse in nature.
Spatial-Temporal Assessment of Changes in Aerosol Optical Properties Pre, during, and Post COVID-19 Lockdowns over Kenya, East Africa
(Open Access Library Journal, 2024-04-26) Mutama, Peter M.; Makokha, John Wanjala; Kelonye, Festus Beru; Khamala, Geoffrey W.
In reference to the contribution of natural and anthropogenic activities to pollution levels over Kenya, investigation of the changes in aerosol optical properties during COVID-19 lockdowns was assessed. To achieve its objective the present study used aerosol Optical Depth (AOD), Angstrom exponent (AE) and Single Scattering Albedo (SSA) from Ozone Monitoring Instrument (OMI) and Moderate-resolution Imaging Spectroradiometer (MODIS) satellite sensors, to analyze the variations in aerosol properties for pre, during and post COVID-19 pandemic. This was achieved by doing a phase wise analysis of the spatial-temporal variation over Kenya during the lockdown phase. A comparison to reference period was done for the pre-lockdown, during lockdown and post lockdown phases. 24-hour mean value data retrieval over Kenya was obtained from the Modern-Era Retrospective analysis for Research and Applications (MERRA-2) model from 1st April to 30th June 2019 - 2021. It was evident that the emissions into the atmosphere over Kenya did not reduce relatively during the COVID-19 lockdowns. The spatial-temporal variability of the pollutants (AOD, AE AND SSA) did not depict a significant deviation from the normal in the lockdown phase as compared to the same season in the previous one year and a year after lockdowns. This was because of the migration of aerosols from regional sources, dominance of natural sources such as geothermal activities and low stringent levels on lockdown protocols. However meteorological factors have had great influence on the variability and seasonality of the aerosol optical properties over the sampled region, with the March-April-May (wet season) recording lower values of AOD and June-July-August (dry season) registering the highest values of AOD. In summary lockdowns did not alter values of aerosol optical properties over Kenya due to limited control of anthropogenic emissions. The findings of this proposed study can be utilized by the scientific community and regulators to strengthen the emergency response to check on high pollution in Kenya until cleaner technologies are put in place.
Spatial-Temporal Assessment of Gaseous and Particulate Matter Pollutants during COVID-19 Lockdown over Kenya, East Africa
(Voice of the Publisher, 2025-09-16) Mutama, Peter M.; Makokha, John Wanjala; Kelonye, Festus B.; Khamala, Geoffrey W.
Varied naturally occurring and anthropogenic emissions within the Kenyan territory contribute to elevation of levels of organic and inorganic, gaseous and particulate pollutant types. A study to ascertain main contributing factors to the status quo was vital. The study compares satellite-derived datasets for five main pollutant parameters, such as Black carbon (BC), Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Ozone gas (O3) for three equal periods: pre-lockdown (April-June 2019), lockdown (April-June 2020) and post-lockdown (April-June 2021). The study utilized Aura/Ozone Monitoring Instrument (OMI), Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), and MODIS (Moderate-resolution Imaging Spectroradiometer) satellite sensors to ascertain the variations in anthropogenic emissions into the atmosphere during COVID-19 lockdowns in Kenya. This was achieved by doing a phase-wise analysis of spatial-temporal variation of the fore mentioned five main pollutants over Kenya during the pre, during and post lockdown phases. The datasets obtained were manipulated using the Adobe Illustrator (2015 series) and the Grid Analysis and Display System (Grads) Version 2.2.1.oga.1 for the above-mentioned pollutants from 2019 to 2021 (April-June). It was evident that the spatial-temporal variability of the pollutants did not depict a significant reduction in the lockdown phase. This was because of the migration of aerosols from regional sources, the dominance of natural sources such as geothermal activities, and low stringent levels of lockdown protocols. However, meteorological factors had a great influence on the variability of the concentration of pollutants over the sampled region with the MAM (March-April-May), considered wet, season recording lower concentrations and JJA (June-July-August), considered a dry season, registering the highest concentrations.
Spatiotemporal analysis of absorbing aerosols and radiative forcing over environmentally distinct stations in East Africa during 2001–2018
(Science of The Total Environment, 2023-03-15) Khamala, Geoffrey W.; Makokha, John Wanjala; Boiyo, Richard; Kumar, Kanike Raghavendra
East Africa (EA) suffers from the inadequate characterization of atmospheric aerosols, with far-reaching consequences of its inability to quantify precisely the impacts of these particles on regional climate. The current study aimed at characterizing absorption and radiative properties of aerosols using the long-term (2001–2018) AErosol RObotic NETwork (AERONET) and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data over three environmentally specific sites in EA. The annual mean absorption aerosol optical depth (AAOD ), absorption Angstrom Exponent (AAE ), total effective radius (R ), and total volume concentration (μm/μm) revealed significant spatial heterogeneity over the domain. The study domain exhibited a significant contribution of fine-mode aerosols compared to the coarse-mode particles. The monthly variation in SSA 440 nm over EA explains the strength in absorption aerosols that range from moderate to strong absorbing aerosols. The aerosols exhibited significant variability over the study domain, with the dominance of absorbing fine-mode aerosols over Mbita accounting for ∼40 to ∼50 %, while weakly absorbing coarse-mode particles accounted for ∼8.2 % over Malindi. The study conclusively determined that Mbita was dominated by AAOD mainly from biomass burning in most of the months, whereas Malindi was coated with black carbon. The direct aerosol −2 radiative forcing (DARF) retrieved from both the AERONET and MERRA-2 models showed strong cooling at the top of the atmosphere (TOA; −6 to −27 Wm ) and the bottom of the atmosphere (BOA, −7 to −66 Wm ). However, significant warming was noticed within the atmosphere (ATM; +14 to +76 Wm ), an indication of the role of aerosols in regional −2 −2 PDF climate change. The study contributed to understanding aerosol absorption and radiative characteristics over EA and can form the basis of other related studies over the domain and beyond.
Spatiotemporal Analysis of Aerosol Optical Properties and Their Associations Using Satellite and Ground-Based Observations
(Atmospheric and Climate Sciences, 2025-09-29) Wanjala, Dennis W.; Makokha, John Wanjala; Khamala, Geoffrey W.
Aerosols play a critical role in Earth’s climate system. They influence cloud formation, atmospheric dynamics and Earth’s energy balance. This study presents a comprehensive spatiotemporal analysis of aerosol optical depth (AOD), Angstrom Exponent (AE), Single scattering Albedo (SSA) and their associations with primary climate variables such as Surface Air Temperature (SAT) and Rainfall Rates (RR). The present study derived its data from both satellites based remote sensing data and ground based observation, i.e. , Moderate Resolution Imaging Spectrometer (MODIS), Modern Era Retrospective Analysis for Research and Application 2 (MERRA-2) and Tropical Rainfall Mission (TRMM) between the years 2000 to 2022. These data platforms are run and maintained by National Aeronautics and Space Administration (NASA). The researcher examined monthly and annual trends. Hidden Markov models were employed to determine the patterns and potential cause of variabilities and the link between aerosol optical properties and climate variables. The researcher determined trends in AOP and evaluated the trends in climate variables using HMM. Satellite-based dataset provided enhanced spatial resolution, accurate and observation. The findings gave more insight into aerosol dynamics and accurate climate modelling; the researcher addressed critical gaps in understanding the interactions between aerosols and climate variables in Kenya, a region highly vulnerable to the impacts of climate change and air quality degradation, hence better environment planning policy. Identified hidden patterns and transitions that were often overlooked by traditional methods. The methodological innovation is not only relevant for Kenya but also adaptable to other regions facing similar environmental challenges, thereby contributing to the broader field of atmospheric sciences.
Spatiotemporal Assessment of Deforestation Effects on Aerosol Optical Characteristics and Climate Variability over the Mau Forest Complex Based on MERRA-2 Reanalysis
(Open Access Library Journal, 2026-04-24) Jepchirchir, Caroline M.; Khamala, Geoffrey W.; Makokha, John Wanjala
The deforestation has far-reaching effects on aerosol characteristics and climatic variables. Deforestation disrupts the local climate by altering temperature, aerosol optical properties, and impacting air quality. Further, it also modifies precipitation patterns at varied scales. Nevertheless, the long-term impacts of deforestation on climate variables and aerosol properties over Mau remain not very well explored, especially considering the context of altered natural emissions and anthropogenic sources. This study bridges this gap through an in-depth analysis of deforestation impacts on aerosol characteristics and climate variables over the Mau Forest complex bounded by (0.2S, 35.2E) and (0.8S, 35.8E) using satellite and model-derived data from 2001 to 2024. The findings of the present study reveal that Aerosol optical depth (<0.2) and Ångström exponent (>1) are predominantly attributed to deforestation and climate change. The Correlation analysis found that surface temperature has a strong negative correlation with Aerosol Optical Depth (AOD), with a coefficient of <−0.3, and is influenced by deforestation activities such as land clearing, agricultural activities, and dust storms. In addition, precipitation identified a moderate positive correlation with AOD, with values ranging from 0.1 to 0.4, attributed to factors such as the complex interplay of aerosol types, size distribution, and dust and atmospheric dynamics like strong winds, which can transport aerosols over long distances, and the presence of moist air masses. Besides aerosol optical depth (AOD), Ångström Exponent (AE), precipitation, and temperature are interconnected, influencing each other through complex atmospheric processes. Increased precipitation led to reduced AOD due to wet scavenging of aerosols. On the other hand, temperature affects aerosol formation and distribution. Changes in AOD, in turn, can impact precipitation patterns and temperature through radiative forcing. In short, the investigation indicates that aerosols’ optical properties over the Mau Forest complex exhibit distinct spatial and temporal patterns driven by both human and natural processes. The statistically significant correlations with meteorological parameters such as precipitation and temperature prove the modulating role of aerosol optical properties in regional climate processes. The policymakers must therefore prioritize emission control actions targeted at biomass burning, and scientists must keep investigating high-resolution aerosol optical properties-climate interactions using integrated ground and satellite observations to advance climate impact assessment over the Mau Forest complex in Kenya.
Statistical Analysis of Aerosols Characteristics from Satellite Measurements over East Africa Using Autoregressive Moving Average (ARIMA)
(Open Access Library Journal, 2022-11-04) Khamala, Geoffrey W.; Makokha, John Wanjala; Boiyo, Richard
Aerosols have become a major subject of concern at global, regional and local scales. They influence Earth’s radiation budget by scattering and absorbing solar energy resulting in atmospheric cooling and warming respectively. However, immense efforts have been devoted to monitoring atmospheric aerosols using various techniques ranging from in-situ, ground and satellite-based remote sensing and modeling techniques. Thus, time series analysis and forecasting have gained momentum over recent decades. The current study performed a time series analysis using Box-Jenkins procedure-based ARIMA (Autoregressive Integrated Moving Average) model for aerosol properties (Total Aerosol Optical Depth, TAOD; Absorption Aerosol Optical Depth, AAOD; Scattering Aerosol Optical Depth, SAOD and Direct Aerosol Radiative Forcing, DARF) over EA derived from satellite platforms. The formulation process in MATLAB followed by the current study has been outlined with a view to generating the best fitting seasonal ARIMA (p, q , d ) × (P Q D ) model. The finding for the forementioned characteristics reveals clear seasonal variation, hence, differencing was done. The Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) of differenced series are estimated and the significant lags are used to find out the order of the model. The statistical parameters (RMSE, MAE, MAPE, MASE and normalized BIC) were estimated for testing the validity of ARIMA models so formulated. The current study found that ARIMA (1, 0, 0) × (2, 1, 2)12 model is adequate for forecasting and was therefore used to forecast aerosol characteristics for the year 2022-2025 over EA domain. ARIMA model ascertained can be applied to other fields of study such as climatology, and climate change among other areas to predict future values so that timely control measures can effectively be planned.
The spatiotemporal and dependency analysis of selected meteorological parameters and normalized difference vegetation index with aerosol optical depth over east Africa
(Heliyon, 2024-10-29) Khamala, Geoffrey W.; Makokha, John Wanjala; Boiyo, Richard
The unprecedented rise in atmospheric aerosols, coupled with their intricate interactions with the environment through a wide array of physical, chemical, and biological processes, has profoundly impacted global climate. Their presence in the atmosphere scatters and absorbs solar radiation, thus altering the amount of sunlight reaching the Earth’s surface. These direct effects, along with the indirect effects of aerosols, have significantly altered atmospheric temperatures, land surface processes, global surface temperature, hydrological cycle, and ecosystems. Understanding the complex interplay between aerosols and climatic variables necessitates a multidisciplinary approach, such as dependency modeling. Addressing these challenges, the current study conducts a spatiotemporal correlational analysis of selected key meteorological parameters with aerosol optical depth over East Africa (EA) using multisensory data from Moderate-resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Application (MERRA-2) model, and Tropical Rainfall Measurement Mission (TRMM). Employing a weighted least squares regression (WLS) model, the study quantifies trends in the time series of climatic variables and Normalized Difference Vegetation Index (NDVI), further utilizing a statistical dependency modeling technique for correlational analysis. The trend analysis reveals a significant decreasing trend in surface wind speed (SWS) in most months, with sporadic positive trends attributed to anthropogenic activities, notably biomass burning, observed in January. Spatial trend analysis of Precipitation Rate (PR) displays heterogeneity, with significant negative trends in January and March, and positive trends in February, April, November, and December. Negative trends during May to August are attributed to increased anthropogenic activities, while enhanced positive trends in May correlate with low aerosol optical depth (AOD) during this period. Surface air temperature (SAT) exhibits diverse variations across the region, with dry months recording higher averages and trends than wet months. The study notes heterogeneous correlations in NDVI over the study area, with positive and negative correlations observed in different regions. Specifically, positive correlations are noted along the coastal and Lake Victoria regions, attributed to improved PR enhancing vegetation cover in these areas.