Articles | Volume 17, issue 14
Atmos. Chem. Phys., 17, 9035–9047, 2017
https://doi.org/10.5194/acp-17-9035-2017
Atmos. Chem. Phys., 17, 9035–9047, 2017
https://doi.org/10.5194/acp-17-9035-2017

Research article 27 Jul 2017

Research article | 27 Jul 2017

An improved hydrometeor detection method for millimeter-wavelength cloud radar

Jinming Ge1, Zeen Zhu1, Chuang Zheng1, Hailing Xie1, Tian Zhou1, Jianping Huang1, and Qiang Fu1,2 Jinming Ge et al.
  • 1Key Laboratory for Semi-Arid Climate Change of the Ministry of Education and College of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
  • 2Department of Atmospheric Sciences, University of Washington, Seattle, WA 98105, USA

Abstract. A modified method with a new noise reduction scheme that can reduce the noise distribution to a narrow range is proposed to distinguish clouds and other hydrometeors from noise and recognize more features with weak signal in cloud radar observations. A spatial filter with central weighting, which is widely used in cloud radar hydrometeor detection algorithms, is also applied in our method to examine radar return for significant levels of signals. Square clouds were constructed to test our algorithm and the method used for the US Department of Energy Atmospheric Radiation Measurements Program millimeter-wavelength cloud radar. We also applied both the methods to 6 months of cloud radar observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University and compared the results. It was found that our method has significant advantages in reducing the rates of both failed negative and false positive hydrometeor identifications in simulated clouds and recognizing clouds with weak signal from our cloud radar observations.

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Short summary
A modified method with a new noise reduction scheme that can reduce the noise distribution to a narrow range is proposed to distinguish clouds and other hydrometeors from noise and recognize more features with weak signal in cloud radar observations. It was found that our method has significant advantages in reducing the rates of both failed negative and false positive hydrometeor identifications in simulated clouds and recognizing clouds with weak signal from our cloud radar observations.
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