Abstract
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.
Highlights
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 current study presents novel techniques for spatial-temporal aerosol characterization over East Africa for over a decade of monthly selected aerosol optical and microphysical properties i.e. Aerosol Optical Depth (AOD), Ångström Exponent (ÅE) and Mass Concentration (MC) from Moderate Resolution Imaging Spectrometer (MODIS) through self-organizing map (SOM) and GHSOM toolboxes in MATLAB
A total of 69 - 72 clusters are revealed during GHSOM classification of the PR dataset, the high number of clusters was attributed to the highest sample space as compared to the rest of the datasets used in this work
Summary
Recent initiatives by organizations such as NASA among others have increasingly deployed a number of passive remote sensing platforms that provide systematic and accurate long-term measurements of aerosol characteristics over the globe. This initiative hasn’t been reciprocated adequately by the science community since a large percentage of the data used is low, in part because of a lack of efficient and effective analysis tools. Accurate extraction of key features and characteristic patterns of variability from a large data set is vital to correctly monitor atmospheric processes and how they alter climate change [4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
