Statistical Cluster Analysis of Global Aerosol Optical Depth for Simplified Atmospheric Modeling
Abstract
Atmospheric aerosols originating from natural and anthropogenic sources have implications for modeling atmospheric phenomena, as aerosols conditions can change significantly and rapidly because depending on local geography and atmospheric conditions. A computational k-means clustering algorithm is applied to a global set of data obtained from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) to yield a set of 25 clusters that discriminate based on land type, elevation, and atmospheric conditions to predict statistical aerosol optical depth (AOD) information. Analysis of annual 2016 MERRA-2 data in addition to data from four meteorological seasons for the five year period from 2012 through 2016 resulted in five separate sets of 25 clusters. The clustered AOD information is available with decision trees, qualitative cluster descriptions, and color-coded cluster maps to assist in identifying which cluster to use in retrieving AOD information. The results of this analysis have applications in atmospheric modeling where knowledge of approximate or typical aerosol conditions are needed in lookup table form without requiring access to large atmospheric databases or computationally intensive aerosol models.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 2020
- Accession Number
- AD1114168
Entities
People
- Anna H. Rubinstein
- Joshua E. Szekely
- Noah T. Plymale
Organizations
- Institute for Defense Analyses