A study on the atmospheric and environmental impacts of aerosol, cloud and precipitation interaction.
| dc.contributor.advisor | Chetty, Naven. | |
| dc.contributor.author | Yakubu, Abdulaziz Tunde. | |
| dc.date.accessioned | 2024-02-01T17:15:14Z | |
| dc.date.available | 2024-02-01T17:15:14Z | |
| dc.date.created | 2022 | |
| dc.date.issued | 2022 | |
| dc.description | Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg. | |
| dc.description.abstract | Understanding the mechanisms and processes of aerosol-cloud-precipitation interactions (ACPI) is essential in the determination of the specific role of aerosols in modulating extreme weather events and climate change in the long run. Atmospheric aerosols are mainly of various types and are emitted from differing sources. Considering they commonly exist in the heterogeneous forms in most environments, they significantly influence the incoming solar energy and the general perturbation of the clouds depending on their constituents. Thus, a systemic identification and characterisation of these particles are essential for proper representation in climate models. To better understand the process of climate change, this research explores the climate diversity of South Africa to examine aerosol sources and types concerning the atmospheric aerosol suspension over the region and their role in clouds and precipitation formation. The study further provided answers to the cause of extreme precipitation events, including drought and occasional flooding experienced over the region. Also, an insightful explanation of the process of ACPI is provided in the context of climate change. Furthermore, the research found that the effective radiative forcing (RF) over South Africa as monitored in Cape Town and Pretoria is negative (i.e., cooling effect) and provided an analysis of the cause. Similarly, the validation of some satellite datasets from MISR (Multiangle Imaging Spectroradiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) instruments against AERONET (Aerosol Robotic Network) is conducted over the region. Although a significant level of agreement is observed for the two instruments, intense improvements are needed, especially regarding measurements over water surfaces. Finally, the study demonstrated the proficiency of effective rainfall prediction from satellite instrument cloud datasets using machine learning algorithms. | |
| dc.identifier.doi | https://doi.org/10.29086/10413/22634 | |
| dc.identifier.uri | https://hdl.handle.net/10413/22634 | |
| dc.language.iso | en | |
| dc.subject.other | Aerosol. | |
| dc.subject.other | Cloud. | |
| dc.subject.other | Climate. | |
| dc.subject.other | Machine learning. | |
| dc.subject.other | Remote sensing. | |
| dc.title | A study on the atmospheric and environmental impacts of aerosol, cloud and precipitation interaction. | |
| dc.type | Thesis | |
| local.sdg | SDG4 |