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.

Two‐year observations of a Ka‐band Zenith Radar at the Semi‐Arid Climate and Environment Observatory of Lanzhou University (SACOL) are used to document the midlatitude cirrus cloud macroproperties. Generally, cirrus occurs 41.6% of the observation time and most frequently appear at about 7.2 km above ground level. The cirrus macroproperties are strongly coupled with large‐scale atmospheric states; thus, its occurrence and location over the SACOL have significant seasonal variations. A k‐mean clustering method is used to classify cirrus into four distinct regimes without a prior knowledge about the meteorological process. Contrasting to the different cirrus physical properties in each regime, the cirrus event of each regime has a distinct seasonal distribution and the synoptic conditions from the ERA‐Interim reanalysis responsible for each cirrus regime are also quite different. Since global climate models typically overestimate cirrus cloud thickness due to inadequate parameterization or coarse grid resolution, we examined the probability density functions of large‐scale vertical velocity associated with each cirrus regime and the relationship between cirrus thickness and vertical velocity. It is found that the differences of the vertical velocity probability density functions among the cirrus regimes are as distinct as their macroproperties and a significant correlation exists between cirrus thickness and the vertical velocity, although the large‐scale vertical motion is nearly as likely to be descending as ascending when cirrus clouds are observed. This may imply that large‐scale vertical velocity can be used to constrain the variations of cirrus thickness simulated by global climate models.

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