%0 Journal Article
%T Optical Characterization of Atmospheric Aerosols via Airborne Spectral Imaging and Self-Organizing Map for Climate Change Diagnostics
%A John W. Makokha
%A Jared O. Odhiambo
%J Open Access Library Journal
%V 5
%N 8
%P 1-10
%@ 2333-9721
%D 2018
%I Open Access Library
%R 10.4236/oalib.1104698
%X <div style="text-align:justify;">
	<span style="line-height:1.5;">Self-Organizing
Map (SOM) analysis is used to perform optical characteri- zation of both Aerosol Optical Depth (AOD) and
Angstrom Exponent (AE) 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 AE that is associated
to changing aerosol transport, urban heat islands, diffusion, direct emission,
hygroscopic growth and their scavenging fro</span><span style="line-height:1.5;">m 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 AE
that are related to aerosol mode of generation and composition over the region.
The single cluster in AOD and £żE 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.</span> 
</div>
%K Aerosol Optical Depth
%K Self-Organizing Map
%K Angstrom Exponent
%K Neural Network
%K Remote Sensing
%K East African Atmosphere
%U http://www.oalib.com/paper/5297155