Mitigation of wind turbine clutter for weather radar by signal separation

Faruk Uysal, Ivan Selesnick, Bradley M. Isom

Research output: Contribution to journalArticlepeer-review


This paper addresses the mitigation of wind turbine clutter (WTC) in weather radar data in order to increase the performance of existing weather radar systems and to improve weather analyses and forecasts. We propose a novel approach for this problem based on signal separation algorithms. We model the weather signal as group sparse in the time-frequency domain; in parallel, we model the WTC signal as having a sparse time derivative. In order to separate WTC and the desired weather returns, we formulate the signal separation problem as an optimization problem. The objective function to be minimized combines total variation regularization and time-frequency group sparsity. We also propose a three-window short-time Fourier transform for the time-frequency representation of the weather signal. To show the effectiveness of the proposed algorithm on weather radar systems, the method is applied to simulated and real data from the next-generation weather radar network. Significant improvements are observed in reflectivity, spectral width, and angular velocity estimates.

Original languageEnglish (US)
Article number7378306
Pages (from-to)2925-2934
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number5
StatePublished - May 2016


  • Dynamic clutter mitigation
  • signal separation
  • sparse optimization
  • weather radar
  • wind turbine clutter (WTC)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • General Earth and Planetary Sciences


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