Can embedded liquid layers be classified in polar clouds using a zenith-pointing radar?



Silber, Israel - Pennsylvania State University

Area of research:

Cloud Distributions/Characterizations

Journal Reference:

Silber I, J Verlinde, G Wen, and E Eloranta. "Can Embedded Liquid Cloud Layer Volumes Be Classified in Polar Clouds Using a Single-Frequency Zenith-Pointing Radar?" IEEE Geoscience and Remote Sensing Letters, , doi:10.1109/LGRS.2019.2918727. ONLINE.


Detection of embedded liquid-bearing layers in polar clouds using zenith-pointing radars could enhance our observational knowledge about polar cloud processes. By examining one year of radar data, we show that cloud-top liquid-bearing cloud layers can potentially be reliably detected at high percentages, but embedded liquid cloud layers are significantly more challenging to detect, and could be reliably separated from liquid-free cloud layers in bulk processing only in exceptional cases.


Our statistical analysis shows that a high percentage of embedded liquid-bearing cloud layers, which are common in deep precipitating systems, might be missed and mistakenly classified as ice, both in radar data gathered at the Arctic (e.g., North Slope of Alaska) and the Antarctic (e.g., AWARE). In addition, our study provides recommendations for the radar parameters that will likely be most useful in classification efforts, as well as distribution fit parameters for radar returns from liquid- and ice-containing air volumes.


Various attempts to classify ground-based radar observations use different techniques. In this study, we examine the potential of detecting air-volumes containing liquid water in polar clouds using the Ka-band ARM Zenith Pointing Radar (KAZR). We used measurements gathered at Barrow, Alaska in 2015, to produce comprehensive statistics about the Doppler-radar moments and the Doppler spectra. We find that the cloud-top liquid-bearing cloud layers (LBCLs) can potentially be reliably detected at high percentages with the KAZR when the signal is above the radar noise floor. However, embedded LBCLs are significantly more challenging to detect, and could potentially be reliably separated in bulk processing only in exceptional cases, which account for not more than a few tens of percent of these cloud layer occurrences. We also suggest that additional difficulties in separating the LBCLs from the ice cloud layers are expected in dryer and/or orographically forced regions -- for example, Eureka, Canada, and Ross Island, Antarctica. Finally, we also provide distribution fit parameters that may be useful for radar classification schemes in a single parameter or multivariate normal distribution form.