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Dr. Thomas Börner DLR Oberpfaffenhofen Microwaves and Radar Institute D-82234 Weßling

Methodology for obtaining physical parameters from fully polarimetric coherent weather radar data: A first approach to unsupervised Entropy-Alpha-classification. Dr. Thomas Börner DLR Oberpfaffenhofen Microwaves and Radar Institute D-82234 Weßling. Outline.

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Dr. Thomas Börner DLR Oberpfaffenhofen Microwaves and Radar Institute D-82234 Weßling

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  1. Methodology for obtaining physical parameters from fully polarimetric coherent weather radar data: A first approach to unsupervised Entropy-Alpha-classification Dr. Thomas Börner DLR Oberpfaffenhofen Microwaves and Radar Institute D-82234 Weßling

  2. Outline • Introduction to the H-a decomposition theorem • Classification scheme • Analysis and interpretation of the polarimetric time series data set • Conclusions • Future activities

  3. Decomposing the Scattering Matrix

  4. Entropy H and a angle

  5. a = 0 a = 45 a = 90 What does a tell us?

  6. H-a feasible area

  7. “Classic” Products Zyy [dBZ] ZDR [dB]

  8. Extracted Products Entropy H a angle [deg]

  9. Populated H-a plane

  10. Zyy and Classification Zyy [dBZ] Classes

  11. 15 consecutive PPI Sector Scans Zyy [dBZ] Classes

  12. Is there a Z-H relation?

  13. Is there a Z-a relation?

  14. Conclusions • It has been shown that it is possible to apply the H-a decomposition to polarimetric weather radar data and to retrieve meaningful results. • The classification provides knowledge about different types of scatterers without having to access empirical a-priori knowledge. • Proper interpretation of PPI scans is difficult, because it is unclear what the radar beam is actually scanning. • Regions with ground clutter can be easily detected, which might help to enhance clutter filtering.

  15. Future Activities • Analyse RHI scans: different layers are easier to distinguish  enhance the classification scheme! • Compare results with other sources of information about particles, preferably simultaneously collected. • Compare results with other classification methods. • Use additional parameters as l1 or the anisotropy to refine the classification scheme.

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