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Cluster Analysis of AMSR-E Brightness Temperatures

Cluster Analysis of AMSR-E Brightness Temperatures. Danny Braswell and Roy Spencer 23 September 2014. Defining cluster analysis.

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Cluster Analysis of AMSR-E Brightness Temperatures

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  1. Cluster Analysis of AMSR-E Brightness Temperatures Danny Braswell and Roy Spencer 23 September 2014

  2. Defining cluster analysis • In a general sense, cluster analysis is the grouping of a set of items into subsets of items with similar characteristics. For example, in a restaurant you could group (cluster) patrons based what they ordered, or whether or not they added salt to their food, or even the color of their clothes. Another way would be to cluster them based on separation distance, e.g. those sitting together at each table. • Separation distance is one of the more common ways of clustering items. This can be in a 2 or 3 dimensional real space or in a higher order multi-dimensional space. • Accounting for the distance is usually done in one of two ways: absolute distance or squared distance. • Squared distance places progressively greater weights on larger separations, and is the more commonly used.

  3. Defining cluster analysis (cont) • For our purposes, we use “cluster analysis” to mean the dividing of M points in N dimensions into K clusters so that the within-cluster sum of squares is minimized. • Each cluster is defined by the coordinates of its centroid. • As a simple example in 2-D, consider random points on a square grid. • Each point is defined by its (x,y) coordinates.

  4. Random points on a square grid 10,000 points

  5. Random points on a square grid classified into 4 clusters Cluster centroids marked in red

  6. Random points on a square grid classified into 6 clusters Cluster centroids marked in red

  7. Clustering of AMSR-E Tbs • For AMSR-E, a point is defined by 9 simultaneous AMSU channel differences computed from the 10 channels: - 10V, 10H - 18V, 18H - 23V, 23H - 36V, 36H - 89V, 89H • Reference channel for differences: 18V • 9 dimensions - each channel difference is considered a dimension • Differences used to remove temperature signal • 10 clusters • Used K-means algorithm by Hartigan and Wong (ref at end) to perform clustering

  8. AMSR-E Data Used • AMSR-E Level 2A (v12) • Land only • ASC/DSC orbits combined • Days: 1/15/2008, 6/15/2008

  9. 10 land clusters

  10. Average Tbs for each cluster 1 6 4 2 7 Tb (K) 9 8 5 3 10 Channel

  11. Clusters 1, 2 2 - snow 1 - dense veg

  12. Clusters 3, 4 4 – snow/storms 3 – glacial ice

  13. Clusters 5, 6 6 – sparse veg 5 – deep snow

  14. Clusters 7, 8 8 - snow 7- marginal desert/sea ice

  15. Clusters 9, 10 10 – glacial ice 9 – desert/glacial ice

  16. Reference for:K-means Clustering Algorithm • Hartigan, J. A.; Wong, M. A. (1979). "Algorithm AS 136: A K-Means Clustering Algorithm". Journal of the Royal Statistical Society, Series C28 (1): 100–108. • Fortran 77 subroutine asa_136.favailable from: http://lib.stat.cmu.edu/apstat/136

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