Department of Science, Technology and Engineering

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Browsing Department of Science, Technology and Engineering by Subject "Ångström Exponent"

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    Optical characterization of atmospheric aerosols via airborne spectral imaging and self-organizing map for climate change diagnostics
    (2018-08-03) Makokha, John W.; Odhiambo, J.O.
    Self-Organizing Map (SOM) analysis is used to perform optical characterization of both Aerosol Optical Depth (AOD) and Ångström Exponent (ÅE) retrieved from Moderate-resolution Imaging Spectroradiometer (MODIS) in relation to Precipitation Rate (PR) from Tropical Rainfall Measurement Mission (TRMM) over selected East African sites from 2000 to 2014 and further diagnose climate change over the region if any. SOM reveals a marked spatial variability in AOD and ÅE that is associated to changing aerosol transport, urban heat islands, diffusion, direct emission, hygroscopic growth and their scavenging from the atmosphere specific to each site. Temporally, all sites except Mbita and Kampala indicate two clusters in AOD that are associated to prevailing dry and wet seasons over East Africa. Moreover, all sites except Mbita and Mount Kilimanjaro show two clusters in ÅE that are related to aerosol mode of generation and composition over the region. The single cluster in AOD and ÅE over Mbita indicate that aerosol characteristics over the site are influenced by biomass burning and local air circulation rather than the monsoon precipitation throughout the study period.
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    Spatial-temporal characterization of atmospheric aerosols via airborne spectral imaging and growing hierarchical self-organizing maps
    (Scientific Research Publishing, 2018-06-25) Makokha, John W.; Odhiambo, J.O.
    Neural network analysis based on Growing Hierarchical Self-Organizing Map (GHSOM) is used to examine Spatial-Temporal characteristics in Aerosol Optical Depth (AOD), Ångström Exponent (ÅE) and Precipitation Rate (PR) over selected East African sites from 2000 to 2014. The selected sites of study are Nairobi (1˚S, 36˚E), Mbita (0˚S, 34˚E), Mau Forest (0.0˚ - 0.6˚S; 35.1˚E - 35.7˚E), Malindi (2˚S, 40˚E), Mount Kilimanjaro (3˚S, 37˚E) and Kampala (0˚N, 32.1˚E). GHSOM analysis reveals a marked spatial variability in AOD and ÅE that is associated to changing PR, urban heat islands, diffusion, direct emission, hygroscopic growth and their scavenging from the atmosphere specific to each site. Furthermore, spatial variability in AOD, ÅE and PR is distinct since each variable corresponds to a unique level of classification. On the other hand, GHSOM algorithm efficiently discriminated by means of clustering between AOD, ÅE and PR during Long and Short rain spells and dry spell over each variable emphasizing their temporal evolution. The utilization of GHSOM therefore confirms the fact that regional aerosol characteristics are highly variable be it spatially or temporally and as well modulated by PR received over each variable.