Detection of Ground Clutter from Weather Radar Using a Dual-Polarization and Dual-Scan Method

Details

• Journal Title:
  Atmosphere
• Personal Author:
  Golbon-Haghighi, Mohammad-Hossein ; Zhang, Guifu ; Li, Yinguang ; Doviak, Richard
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies)
• Description:
  A novel dual-polarization and dual-scan (DPDS) classification algorithm is developed for clutter detection in weather radar observations. Two consecutive scans of dual-polarization radar echoes are jointly processed to estimate auto- and cross-correlation functions. Discriminants are then defined and estimated in order to separate clutter from weather based on their physical and statistical properties. An optimal Bayesian classifier is used to make a decision on clutter presence from the estimated discriminant functions. The DPDS algorithm is applied to the data collected with the KOUN polarimetric radar and compared with the existing detection methods. It is shown that the DPDS algorithm yields a higher probability of detection and lower false alarm rate in clutter detection.
• Keywords:
  Clutter Detection Dual-polarization Dual-scan (DPDS) Observations Radar Clutter Detection Dual-polarization Dual-scan (DPDS)
• Source:
  Atmosphere 2016, 7(6), 83
• DOI:
  https://doi.org/10.3390/atmos7060083
• Document Type:
  Journal Article
• Funding:
  Grant no. NA11OAR4320072
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:96f6b62d73448be7033c42d6a8e7800a6ec100dd322d3bf5b07cfdff63f184a1
• Download URL:
  https://repository.library.noaa.gov/view/noaa/32180/noaa_32180_DS1.pdf
• File Type:
  [PDF - 2.10 MB ]