Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network
Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations inf...
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Online Access: | http://www.osti.gov/servlets/purl/1877060 https://www.osti.gov/biblio/1877060 https://doi.org/10.5194/amt-15-279-2022 |
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ftosti:oai:osti.gov:1877060 2023-07-30T04:02:09+02:00 Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network Kalesse-Los, Heike Schimmel, Willi Luke, Edward Seifert, Patric 2022-08-15 application/pdf http://www.osti.gov/servlets/purl/1877060 https://www.osti.gov/biblio/1877060 https://doi.org/10.5194/amt-15-279-2022 unknown http://www.osti.gov/servlets/purl/1877060 https://www.osti.gov/biblio/1877060 https://doi.org/10.5194/amt-15-279-2022 doi:10.5194/amt-15-279-2022 54 ENVIRONMENTAL SCIENCES 2022 ftosti https://doi.org/10.5194/amt-15-279-2022 2023-07-11T10:13:36Z Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection ... Other/Unknown Material Arctic SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic Luke ENVELOPE(-94.855,-94.855,56.296,56.296) Atmospheric Measurement Techniques 15 2 279 295 |
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Open Polar |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
op_collection_id |
ftosti |
language |
unknown |
topic |
54 ENVIRONMENTAL SCIENCES |
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54 ENVIRONMENTAL SCIENCES Kalesse-Los, Heike Schimmel, Willi Luke, Edward Seifert, Patric Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection ... |
author |
Kalesse-Los, Heike Schimmel, Willi Luke, Edward Seifert, Patric |
author_facet |
Kalesse-Los, Heike Schimmel, Willi Luke, Edward Seifert, Patric |
author_sort |
Kalesse-Los, Heike |
title |
Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network |
title_short |
Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network |
title_full |
Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network |
title_fullStr |
Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network |
title_full_unstemmed |
Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network |
title_sort |
evaluating cloud liquid detection against cloudnet using cloud radar doppler spectra in a pre-trained artificial neural network |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1877060 https://www.osti.gov/biblio/1877060 https://doi.org/10.5194/amt-15-279-2022 |
long_lat |
ENVELOPE(-94.855,-94.855,56.296,56.296) |
geographic |
Arctic Luke |
geographic_facet |
Arctic Luke |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
http://www.osti.gov/servlets/purl/1877060 https://www.osti.gov/biblio/1877060 https://doi.org/10.5194/amt-15-279-2022 doi:10.5194/amt-15-279-2022 |
op_doi |
https://doi.org/10.5194/amt-15-279-2022 |
container_title |
Atmospheric Measurement Techniques |
container_volume |
15 |
container_issue |
2 |
container_start_page |
279 |
op_container_end_page |
295 |
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1772812869925076992 |