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Investigating PCA as a technique for examining stagnation in a storm over mountainous regions

Investigating PCA as a technique for examining stagnation in a storm over mountainous regions. Erin Burke Ecology of Mountain Landscapes 11.18.2004. Storm Data. July 14 th -15 th , 1997 Short but intense storm some stations reporting 1.40 inches of rain in 15 minutes

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Investigating PCA as a technique for examining stagnation in a storm over mountainous regions

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  1. Investigating PCA as a technique for examining stagnation in a storm over mountainous regions Erin Burke Ecology of Mountain Landscapes 11.18.2004

  2. Storm Data • July 14th-15th, 1997 • Short but intense storm • some stations reporting 1.40 inches of rain in 15 minutes • Flooding, bridge washouts, road washouts

  3. What is principal components analysis (PCA)? • Mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. • The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.

  4. PCA (con’t) • The objectives of principal components analysis are: • To discover or to reduce the dimensionality of the data set. •  To identify new meaningful underlying variables. • In remote sensing, PCA basically is a coordinate transformation in multi-band imagery.

  5. Methods • 19 images ranging from 20:59 on July 14th to 16:59 on July 15th • Compared PCA for entire storm vs. when storm stagnant over Green Mountains • Created Time Series Analysis for entire duration of storm • Created Principal Components Analysis for five hours of storm stagnation

  6. Results – TSA for Entire Storm COMPONENT 1 • Explains 60.04% of the variability in the storm • Component 1 represents mathematical average of all 19 images • 0600h and 1200h not as strongly correlated

  7. Results – PCA for Storm Stall COMPONENT 1 • Explains 73.30% of the variability in the storm • Component 1 represents mathematical average of just 6 images • Loadings indicate that all images are highly correlated with pattern

  8. Results – TSA for Entire Storm COMPONENT 2 • Explains 9.20% of the variability in the storm • Component 2 shows microscale features • Images display negative anomalies on tail ends of storm • 0600 and 1200 deviates from patterns again

  9. Results – PCA for Storm Stall COMPONENT 2 • Explains 6.81% of the variability in the storm • Component 2 shows microscale features • Loadings indicate that images vary in correlation

  10. Results TSA & PCA: Component 3 Entire Storm: Subset: 6.55% of variability 6.69% of variability

  11. Conclusions • Component 1 captures more variability when storm was stagnant over Green Mountains vs. over entire track • Component 2 shows less variability for stalled storm, however shows more microscale features than for entire storm • Component 3 also shows more microscale features than for entire storm

  12. Conclusions • While investigating the other components is necessary for a concrete conclusion… • Preliminary research shows that PCA analysis seems to provide a sound way to investigate storm systems and their internal structure even more in depth when stalled over mountain features.

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