Can Embedded Liquid Cloud Layer Volumes Be Classified in Polar Clouds Using a Single- Frequency Zenith-Pointing Radar?
Abstract
Observational knowledge about polar cloud processes requires information about the hydrometeor phase structure of the clouds, preferentially at high resolutions. Therefore, there are various attempts to classify ground-based radar observations using different techniques. Here, we examine the potential of detecting air-volumes containing liquid water in polar clouds using the Ka-band zenith-pointing radar (KAZR). We utilized the 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.
- Authors:
-
[1];
[1];
- Pennsylvania State Univ., University Park, PA (United States)
- Chinese Academy of Sciences (CAS), Beijing (China)
- Univ. of Wisconsin, Madison, WI (United States)
- Publication Date:
- Research Org.:
- Pennsylvania State Univ., University Park, PA (United States)
- Sponsoring Org.:
- USDOE; National Science Foundation (NSF); National Natural Science Foundation of China (NSFC)
- OSTI Identifier:
- 1779348
- Grant/Contract Number:
- SC0017981; PLR-1443495; 41605019
- Resource Type:
- Accepted Manuscript
- Journal Name:
- IEEE Geoscience and Remote Sensing Letters
- Additional Journal Information:
- Journal Volume: 17; Journal Issue: 2; Journal ID: ISSN 1545-598X
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; Radar remote sensing; Doppler spectra; Doppler radar moments; mixed-phase clouds; embedded liquid; phase classification
Citation Formats
Silber, Israel, Verlinde, Johannes, Wen, Guang, and Eloranta, Edwin W. Can Embedded Liquid Cloud Layer Volumes Be Classified in Polar Clouds Using a Single- Frequency Zenith-Pointing Radar?. United States: N. p., 2019.
Web. doi:10.1109/lgrs.2019.2918727.
Silber, Israel, Verlinde, Johannes, Wen, Guang, & Eloranta, Edwin W. Can Embedded Liquid Cloud Layer Volumes Be Classified in Polar Clouds Using a Single- Frequency Zenith-Pointing Radar?. United States. https://doi.org/10.1109/lgrs.2019.2918727
Silber, Israel, Verlinde, Johannes, Wen, Guang, and Eloranta, Edwin W. Wed .
"Can Embedded Liquid Cloud Layer Volumes Be Classified in Polar Clouds Using a Single- Frequency Zenith-Pointing Radar?". United States. https://doi.org/10.1109/lgrs.2019.2918727. https://www.osti.gov/servlets/purl/1779348.
@article{osti_1779348,
title = {Can Embedded Liquid Cloud Layer Volumes Be Classified in Polar Clouds Using a Single- Frequency Zenith-Pointing Radar?},
author = {Silber, Israel and Verlinde, Johannes and Wen, Guang and Eloranta, Edwin W.},
abstractNote = {Observational knowledge about polar cloud processes requires information about the hydrometeor phase structure of the clouds, preferentially at high resolutions. Therefore, there are various attempts to classify ground-based radar observations using different techniques. Here, we examine the potential of detecting air-volumes containing liquid water in polar clouds using the Ka-band zenith-pointing radar (KAZR). We utilized the 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.},
doi = {10.1109/lgrs.2019.2918727},
journal = {IEEE Geoscience and Remote Sensing Letters},
number = 2,
volume = 17,
place = {United States},
year = {Wed Jun 19 00:00:00 EDT 2019},
month = {Wed Jun 19 00:00:00 EDT 2019}
}
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