TITLE:
Optical Characterization of Atmospheric Aerosols via Airborne Spectral Imaging and Self-Organizing Map for Climate Change Diagnostics
AUTHORS:
John W. Makokha, Jared O. Odhiambo
KEYWORDS:
Aerosol Optical Depth, Self-Organizing Map, Angstrom Exponent, Neural Network, Remote Sensing, East African Atmosphere
JOURNAL NAME:
Open Access Library Journal,
Vol.5 No.8,
August
23,
2018
ABSTRACT: 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 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 AE that are related to aerosol mode of generation and composition over the region. The single cluster in AOD and ？E 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.