%0 Journal Article
%T Spatial-Temporal Characterization of Atmospheric Aerosols via Airborne Spectral Imaging and Growing Hierarchical Self-Organizing Maps
%A John W. Makokha
%A Jared O. Odhiambo
%J Journal of Geoscience and Environment Protection
%P 101-110
%@ 2327-4344
%D 2018
%I Scientific Research Publishing
%R 10.4236/gep.2018.66008
%X Neural network analysis based on Growing Hierarchical Self-Organizing Map
(GHSOM) is used to examine Spatial-Temporal characteristics in Aerosol
Optical Depth (AOD), <span style=\"white-space:nowrap;\">&amp;Aring;</span>ngstr&amp;ouml;m Exponent (<span style=\"white-space:nowrap;\">&amp;Aring;</span>E) and Precipitation Rate (PR)
over selected East African sites from 2000 to 2014. The selected sites of study
are Nairobi (1<span style=\"white-space:nowrap;\">กใ</span>S, 36<span style=\"white-space:nowrap;\">กใ</span>E), Mbita (0<span style=\"white-space:nowrap;\">กใ</span>S, 34<span style=\"white-space:nowrap;\">กใ</span>E), Mau Forest (0.0<span style=\"white-space:nowrap;\">กใ</span> - 0.6<span style=\"white-space:nowrap;\">กใ</span>S; 35.1<span style=\"white-space:nowrap;\">กใ</span>E -
35.7<span style=\"white-space:nowrap;\">กใ</span>E), Malindi (2<span style=\"white-space:nowrap;\">กใ</span>S, 40<span style=\"white-space:nowrap;\">กใ</span>E), Mount Kilimanjaro (3<span style=\"white-space:nowrap;\">กใ</span>S, 37<span style=\"white-space:nowrap;\">กใ</span>E) and Kampala
(0<span style=\"white-space:nowrap;\">กใ</span>N, 32.1<span style=\"white-space:nowrap;\">กใ</span>E). GHSOM analysis reveals a marked spatial variability in AOD
and <span style=\"white-space:nowrap;\">&amp;Aring;</span>E 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, <span style=\"white-space:nowrap;\">&amp;Aring;</span>E 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, <span style=\"white-space:nowrap;\">&amp;Aring;</span>E 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.
%K Aerosol Optical Depth
%K &#197
%K ngstr&#246
%K m Exponent
%K Neural Network
%K Satellite Spectral Imaging
%K Precipitation Rate
%K East African Atmosphere
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=85491