GEP  Vol.6 No.6 , June 2018
Spatial-Temporal Characterization of Atmospheric Aerosols via Airborne Spectral Imaging and Growing Hierarchical Self-Organizing Maps
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.
Cite this paper: Makokha, J. and Odhiambo, J. (2018) Spatial-Temporal Characterization of Atmospheric Aerosols via Airborne Spectral Imaging and Growing Hierarchical Self-Organizing Maps. Journal of Geoscience and Environment Protection, 6, 101-110. doi: 10.4236/gep.2018.66008.

[1]   Solomon, S. (2007) Climate Change 2007: The Physical Science Basis: Working Group I Contribution to the Fourth Assessment Report of the IPCC (Vol. 4). Cambridge University Press, Cambridge.

[2]   Stocker, T. (2014) Climate Change 2013: The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.

[3]   Petrou, M. (2004) Preface. Pattern Recognition Letters, 25, 1459.

[4]   Liu, Y. and Weisberg, R.H. (2011) A Review of Self-Organizing Map Applications in Meteorology and Oceanography. In: Mwasiagi, J.I., Ed., Self Organizing Maps - Applications and Novel Algorithm De-sign, InTech.

[5]   Kohonen, T. (1982) Self-Organized Information of Topologically Correct Features Maps. Biological Cybernetics, 43, 59-69.

[6]   Kohonen, T. (2001) Self-Organizing Maps. Springer-Verlag, New York, Berlin, Heidelberg.

[7]   Oja, M., Kaski, S. and Kohonen, T. (2003) Bibliography of Self-Organizing Map (SOM) Papers: 1998-2001 Addendum. Neural Compu-tingSurveys, 3, 1-156.

[8]   Hong, Y., Hsu, K., Sorooshian, S. and Gao, X. (2004) Precipitation Estimation from Remotely Sensed Imagery Using an Artificial Neural Network Cloud Classification System. Journal of Applied Meteorology, 43, 1834-1853.

[9]   Liu, Y., Weisberg, R.H. and He, R. (2006) Sea Surface Temperature Patterns on the West Florida Shelf Using the Growing Hierarchical Self-Organizing Maps. Journal of Atmospheric and Oceanic Technology, 23, 325-328.

[10]   Dittenbach, M., Rauber, A. and Merkl, D. (2002) Uncovering the Hierar-chical Structure in Data Using the Growing Hierarchical Self-Organizing Map. Neurocomputing, 48, 199-216.

[11]   Pampalk, E., Widmer, G. and Chan, A. (2004) A New Approach to Hierarchical Clustering and Structuring of Data with Self-Organizing Maps. Intelligent Data Analysis, 8, 131-149.

[12]   Makokha, J.W., Odhiambo, J.O. and Godfrey, J.S. (2017) Trend Analysis of Aerosol Optical Depth and Angstrom Exponent Anomaly over East Af-rica. Atmospheric and Climate Sciences, 7, 588-603.

[13]   Ichoku, C., Kaufman, Y.J., Remmer, L.A. and Levy, R. (2004) Global Aerosol Remote Sensing from MODIS. Advances in Space Research, 34, 820-827.

[14]   De Graaf, M., Tilstra, L.G., Aben, I. and Stammes, P. (2010) Satellite Observa-tions of the Seasonal Cycles of Absorbing Aerosols in Africa Related to the Monsoon Rainfall, 1995-2008. Atmospheric Environment, 44, 1274-1283.

[15]   van Vliet, E.D.S. and Kinney, P.L. (2007) Impacts of Roadway Emissions on Urban Particulate Matter Concentrations in Sub-Saharan Africa: New Evidence from Nairobi, Kenya. Environmental Research Letters, 2, 045028.

[16]   Mabasi, T. (2009) Assessing the Impacts, Vulnerability, Mitigation, and Adaptation to Climate Change in Kampala City. Fifth Urban Research Symposium. Kampala.

[17]   Fairman, J.G., Nair, U.S., Christopher, S.A. and Mölg, T. (2011) Land Use Change Impacts on Regional Climate over Kilimanjaro. Journal of Geophysical Research: Atmospheres, 116, D03110.

[18]   Makokha, J.W. and Angeyo, H.K. (2013) Investigation of Radiative Characteristics of the Kenyan Atmosphere due to Aerosols Using Sun Spectrophotometry Measurements and the COART Model. Aerosol and Air Quality Research, 13, 201-208.

[19]   Ngaina, J.K. and Mutai, B.K. (2013) Observational Evidence of Climate Change on Extreme Events over East Africa. Global Meteorology, 2, 6-12.

[20]   Ngaina, J.N., Mutai, B.K., Ininda, J.M. and Muthama, J.N. (2014) Monitoring Spatial-Temporal Variability of Aerosol over Kenya. Ethiopian Journal of Environmental Studies and Management, 7, 244-252.

[21]   National Environmental Management Authority, Kenya (NEMA) (2013) Mau at a Glance. NEMA Report.

[22]   Eck, T., Holben, B.N., Ward, D.E., Dubovik, O., Reid, J.S., Smirnov, A., Mukelabai, M.M., Hsu, N.C., O’Neill, N.T. and Slutsker, I. (2001) Characterization of the Optical Properties of Biomass Burning Aerosols in Zambia during the 1997 ZIBBEE Field Campaign. Journal of Geophysical Research 106, 3425-3448.