Unlocking potential: A case study on reducing shortwave radiation bias in the Southern Ocean through improved cloud phase retrievals based on machine learning
There are significant gaps in both experimental and theoretical understanding of mixed-phase clouds, their impacts on the hydrological cycle as well as their effects on atmospheric radiation. Accurately identifying liquid water layers in mixed-phase clouds is crucial for estimating cloud radiative e...
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2024
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Online Access: | http://dx.doi.org/10.22541/essoar.171328617.72402908/v1 |
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crwinnower:10.22541/essoar.171328617.72402908/v1 2024-06-02T08:14:48+00:00 Unlocking potential: A case study on reducing shortwave radiation bias in the Southern Ocean through improved cloud phase retrievals based on machine learning Schimmel, Willi Velasco, Carola Barrientos Witthuhn, Jonas Radenz, Martin González, Boris Barja Kalesse-Los, Heike 2024 http://dx.doi.org/10.22541/essoar.171328617.72402908/v1 unknown Authorea, Inc. posted-content 2024 crwinnower https://doi.org/10.22541/essoar.171328617.72402908/v1 2024-05-07T14:19:22Z There are significant gaps in both experimental and theoretical understanding of mixed-phase clouds, their impacts on the hydrological cycle as well as their effects on atmospheric radiation. Accurately identifying liquid water layers in mixed-phase clouds is crucial for estimating cloud radiative effects. A proof-of-concept study utilizing a machine-learning-based liquid-layer detection method called VOODOO is presented. This method was applied alongside a single-column radiative transfer model to compare downwelling shortwave fluxes of mixed-phase clouds detected by the standard Cloudnet processing chain and VOODOO to ground-based pyranometer observations. Our findings reveal that VOODOO creates more realistic liquid water content distributions and significantly influences profiles of heating rates. Moreover, our study demonstrates a substantial enhancement in the estimation of shortwave cloud radiative effects of VOODOO compared to conventional method Cloudnet. Specifically, we observe a remarkable reduction in the mean absolute error of simulated shortwave radiation at the surface of 70\%, particularly in homogeneous cloud conditions. The mean percentage error of SW cloud radiative effects between Cloudnet and pyranometer observations is 44\%, while VOODOO+Cloudnet reduces this error to 8\%. Overall, our results underscore the potential of VOODOO to provide new insights into deep mixed-phase clouds, which were previously inaccessible using traditional lidar-based remote sensing techniques. Other/Unknown Material Southern Ocean The Winnower Southern Ocean |
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There are significant gaps in both experimental and theoretical understanding of mixed-phase clouds, their impacts on the hydrological cycle as well as their effects on atmospheric radiation. Accurately identifying liquid water layers in mixed-phase clouds is crucial for estimating cloud radiative effects. A proof-of-concept study utilizing a machine-learning-based liquid-layer detection method called VOODOO is presented. This method was applied alongside a single-column radiative transfer model to compare downwelling shortwave fluxes of mixed-phase clouds detected by the standard Cloudnet processing chain and VOODOO to ground-based pyranometer observations. Our findings reveal that VOODOO creates more realistic liquid water content distributions and significantly influences profiles of heating rates. Moreover, our study demonstrates a substantial enhancement in the estimation of shortwave cloud radiative effects of VOODOO compared to conventional method Cloudnet. Specifically, we observe a remarkable reduction in the mean absolute error of simulated shortwave radiation at the surface of 70\%, particularly in homogeneous cloud conditions. The mean percentage error of SW cloud radiative effects between Cloudnet and pyranometer observations is 44\%, while VOODOO+Cloudnet reduces this error to 8\%. Overall, our results underscore the potential of VOODOO to provide new insights into deep mixed-phase clouds, which were previously inaccessible using traditional lidar-based remote sensing techniques. |
format |
Other/Unknown Material |
author |
Schimmel, Willi Velasco, Carola Barrientos Witthuhn, Jonas Radenz, Martin González, Boris Barja Kalesse-Los, Heike |
spellingShingle |
Schimmel, Willi Velasco, Carola Barrientos Witthuhn, Jonas Radenz, Martin González, Boris Barja Kalesse-Los, Heike Unlocking potential: A case study on reducing shortwave radiation bias in the Southern Ocean through improved cloud phase retrievals based on machine learning |
author_facet |
Schimmel, Willi Velasco, Carola Barrientos Witthuhn, Jonas Radenz, Martin González, Boris Barja Kalesse-Los, Heike |
author_sort |
Schimmel, Willi |
title |
Unlocking potential: A case study on reducing shortwave radiation bias in the Southern Ocean through improved cloud phase retrievals based on machine learning |
title_short |
Unlocking potential: A case study on reducing shortwave radiation bias in the Southern Ocean through improved cloud phase retrievals based on machine learning |
title_full |
Unlocking potential: A case study on reducing shortwave radiation bias in the Southern Ocean through improved cloud phase retrievals based on machine learning |
title_fullStr |
Unlocking potential: A case study on reducing shortwave radiation bias in the Southern Ocean through improved cloud phase retrievals based on machine learning |
title_full_unstemmed |
Unlocking potential: A case study on reducing shortwave radiation bias in the Southern Ocean through improved cloud phase retrievals based on machine learning |
title_sort |
unlocking potential: a case study on reducing shortwave radiation bias in the southern ocean through improved cloud phase retrievals based on machine learning |
publisher |
Authorea, Inc. |
publishDate |
2024 |
url |
http://dx.doi.org/10.22541/essoar.171328617.72402908/v1 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_doi |
https://doi.org/10.22541/essoar.171328617.72402908/v1 |
_version_ |
1800738780716466176 |