http://repository.tuc.ac.ke:8080/xmlui/handle/123456789/25 2026-07-21T18:05:34Z 2026-07-21T18:05:34Z Geoffrey W. Khamala John W. Makokha Churchill Wanyera Kanike Raghavendra Kumar Mansour Almazroui Pelati Althaf Aqil Tariq http://repository.tuc.ac.ke:8080/xmlui/handle/123456789/671 2026-07-21T12:35:06Z 2026-05-20T00:00:00Z

Vertical Distribution of Cloud Area Fraction and Associated Cloud Radiative Effects over East Africa Using MERRA-2 Reanalysis and CERES (2000–2025) Data Geoffrey W. Khamala; John W. Makokha; Churchill Wanyera; Kanike Raghavendra Kumar; Mansour Almazroui; Pelati Althaf; Aqil Tariq The Cloud Area Fraction (CAF) and Cloud Radiative Effect (CRE) are among the key metrics for understanding Earth’s climate, as they modulate shortwave and longwave radiation. This study, therefore, analyzes the vertical distribution of CAF and their associated CRE over East Africa (EA) using the Modern-Era Retrospective Analysis for Research and Applications Version (MERRA-2) reanalysis and Clouds and the Earth’s Radiant Energy System (CERES) observational data from 2000 to 2025. The vertical CAFs for low, middle, and high clouds are computed at pressure levels and spatially averaged over EA. These are used to derive shortwave CRE (CREin Wm− 2), longwave CRE (CREin Wm− 2), and net CRE (in Wm− 2) from all-sky and clear-sky radiative fluxes. The validation of MERRA-2 CAF against CERES observations showed it to be reliable (r = 0.749), supporting its use for analyzing cloud-radiation patterns, including verti cal cloud distribution. The study of vertical CAF and CRE over EA using MERRA-2 reveals a strongly stratified cloud climatology shaped by topography, lake impacts, and large-scale circulation. The vertical CAFs at Low, mid, and high clouds show distinct spatial and temporal patterns, with high clouds intensifying the most with more than 60% of the total CAF (0.256), while the contribution of the low CAF against the total covers 18% (0. 076) is the least. Further, the spatiotemporal CRE is dominated by shortwave cooling, ranging from − 14.71 to -54.36 W m−2, which moderates regional temperatures. Cloud layers interact to influence rainfall: low clouds enhance local convection, mid clouds regulate moisture transport, and high clouds mark mature convective systems. This collectively reinforces EA’s bimodal rainfall pattern and highlights the critical role of clouds in regional climate dynamics. This study recommends future analyses that integrate long-term model outputs with satellite and ground-based observations to validate cloud patterns. Such emphasis should focus on the CREs of low, middle, and high clouds, their links to rainfall, and model uncertainties to improve understanding of the role of cloud dynamics over EA’s climate

2026-05-20T00:00:00Z Yonah Situma John W. Makokha Geoffrey W. Khamala Kanike Raghavendra Kumar Richard Boiyo Geoffrey W. Khamala, Geoffrey W. Khamala http://repository.tuc.ac.ke:8080/xmlui/handle/123456789/669 2026-07-21T09:25:40Z 2026-01-19T00:00:00Z

Long-term characterisation and discrimination of aerosol types across Africa using AERONET observations (1998–2024 Yonah Situma; John W. Makokha; Geoffrey W. Khamala; Kanike Raghavendra Kumar; Richard Boiyo; Geoffrey W. Khamala, Geoffrey W. Khamala Accurate identification and classification of atmospheric aerosols remain a significant challenge across Africa, limiting the precise assessment of their climatic effects. This study presents a comprehensive analysis of aerosol characteristics and aerosol types using ground-based observations from twelve selected AErosol RObotic NETwork (AERONET) sites across Africa during 1998–2024. Key aerosol optical properties, including aerosol optical depth (AOD), Ångstr¨ om exponent (AE), fine-mode fraction (FMF), and single scattering albedo (SSA), were examined to characterize their temporal variability and identify dominant aerosol types. A multivariate scattering-based classification approach was employed to improve aerosol discrimination. The results indicate that dust, biomass-burning, and urban/industrial aerosols are the predominant aerosol categories across the continent. Fine- and coarse-mode absorbing aerosols were particularly prevalent in rural tropical regions, highlighting the coexistence of natural and anthropogenic emission sources. Moreover, extensive biomass- burning activity in tropical Africa contributes substantially to regional aerosol loading and complexity. These findings provide valuable insights for improving the representation of aerosol processes in climate models and enhancing assessments of aerosol–climate interactions over Africa. The proposed multivariate framework advances aerosol classification and source attribution, supporting a better understanding of aerosol-related environmental and climatic impacts across the continent.

2026-01-19T00:00:00Z Yonah Situma John W. Makokha Peter N. Khakina Geoffrey W. Khamala Kanike Raghavendra Kumar Richard Boiyo http://repository.tuc.ac.ke:8080/xmlui/handle/123456789/668 2026-07-21T09:19:13Z 2026-06-19T00:00:00Z

Long-term characterisation and discrimination of aerosol types across Africa using AERONET observations (1998–2024 Yonah Situma; John W. Makokha; Peter N. Khakina; Geoffrey W. Khamala; Kanike Raghavendra Kumar; Richard Boiyo Accurate identification and classification of atmospheric aerosols remain a significant challenge across Africa, limiting the precise assessment of their climatic effects. This study presents a comprehensive analysis of aerosol characteristics and aerosol types using ground-based observations from twelve selected AErosol RObotic NETwork (AERONET) sites across Africa during 1998–2024. Key aerosol optical properties, including aerosoloptical depth (AOD), Ångstr¨ om exponent (AE), fine-mode fraction (FMF), and single scattering albedo (SSA), were examined to characterize their temporal variability and identify dominant aerosol types. A multivariate scattering-based classification approach was employed to improve aerosol discrimination. The results indicate that dust, biomass-burning, and urban/industrial aerosols are the predominant aerosol categories across the continent. Fine- and coarse-mode absorbing aerosols were particularly prevalent in rural tropical regions, highlighting the coexistence of natural and anthropogenic emission sources. Moreover, extensive biomass- burning activity in tropical Africa contributes substantially to regional aerosol loading and complexity. These findings provide valuable insights for improving the representation of aerosol processes in climate models and enhancing assessments of aerosol–climate interactions over Africa. The proposed multivariate framework advances aerosol classification and source attribution, supporting a better understanding of aerosol-related environmental and climatic impacts across the continent.

2026-06-19T00:00:00Z ONUNGA, JEREMIAH,PAUL,SHARONODONGO,ANYANGO http://repository.tuc.ac.ke:8080/xmlui/handle/123456789/609 2025-05-13T09:28:22Z 2025-01-20T00:00:00Z

HEALTH INFORMATION MANAGEMENT SYSTEM DATA UTILIZATION IN STRATEGIC MANAGEMENT DECISION MAKING IN HOSPITALS: SYSTEMIC REVIEW ONUNGA, JEREMIAH,PAUL,SHARONODONGO,ANYANGO A health information system is a structured effort to systematically collect, store, and share data essential to the effectiveness of a health system, encompassing various health-related functionalities. Gathering, organizing, and analyzing data is crucial to identify the health needs of a population, providing hospital management with insights to allocate resources effectively across the health workforce, essential medications, governance, and service provision. However, although hospitals typically rely on their staff to handle data, these key administrative responsibilities often lack support for using data to make informed decisions, leading to poor service delivery and unnecessary patient referrals. This desktop review explored how hospital management employs the Health Information Management System for strategic decision-making and identified challenges and barriers to leveraging this data. The review sourced materials from three databases, two journal websites, and Google Scholar, using search terms like “Health Management Information System,” “Strategic management,” “Decision-making,” “health data,” and “barriers to data use.” The analysis followed the Preferred Reporting Items for Meta-analysis and Systematic Review, resulting in 35 articles identified, with seven selected for inclusion after evaluation. Findings revealed that hospital management teams seldom use Health Management Information for decision-making, and health workers similarly underutilize this data for critical choices. Technical and organizational factors significantly hinder the use of Health Management Information for strategic decisions aimed at enhancing service delivery. To overcome these obstacles, the review suggests regular supportive supervision, training, capacity building, mentoring on basic health information skills, and improved feedback through collaboration among policymakers and stakeholders. Hospital administrators should promote a culture that values routine health information for evidence-based decisions across all areas by boosting resources, providing tools, computers, skilled staff, and automation, while offering regular refresher training to keep health personnel adept at using the system and emerging technologies. Management should prioritize the value of information and foster leadership to encourage positive attitudes toward its use. This review provides a valuable resource for informing the government about the initiative’s current status and hospital managers’ data management capabilities for relevant programs, while offering researchers ample material for deeper exploration.

2025-01-20T00:00:00Z