A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors
Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with suffi...
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ftdoajarticles:oai:doaj.org/article:801620a1bc774acd8d1f5f0aa0c402bb 2023-05-15T15:10:39+02:00 A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors L. D. Riihimaki J. M. Comstock K. K. Anderson A. Holmes E. Luke 2016-06-01T00:00:00Z https://doi.org/10.5194/ascmo-2-49-2016 https://doaj.org/article/801620a1bc774acd8d1f5f0aa0c402bb EN eng Copernicus Publications http://www.adv-stat-clim-meteorol-oceanogr.net/2/49/2016/ascmo-2-49-2016.pdf https://doaj.org/toc/2364-3579 https://doaj.org/toc/2364-3587 2364-3579 2364-3587 doi:10.5194/ascmo-2-49-2016 https://doaj.org/article/801620a1bc774acd8d1f5f0aa0c402bb Advances in Statistical Climatology, Meteorology and Oceanography, Vol 2, Iss 1, Pp 49-62 (2016) Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 article 2016 ftdoajarticles https://doi.org/10.5194/ascmo-2-49-2016 2022-12-31T01:02:02Z Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty information on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Advances in Statistical Climatology, Meteorology and Oceanography 2 1 49 62 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 |
spellingShingle |
Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 L. D. Riihimaki J. M. Comstock K. K. Anderson A. Holmes E. Luke A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors |
topic_facet |
Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 |
description |
Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty information on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data. |
format |
Article in Journal/Newspaper |
author |
L. D. Riihimaki J. M. Comstock K. K. Anderson A. Holmes E. Luke |
author_facet |
L. D. Riihimaki J. M. Comstock K. K. Anderson A. Holmes E. Luke |
author_sort |
L. D. Riihimaki |
title |
A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors |
title_short |
A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors |
title_full |
A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors |
title_fullStr |
A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors |
title_full_unstemmed |
A path towards uncertainty assignment in an operational cloud-phase algorithm from ARM vertically pointing active sensors |
title_sort |
path towards uncertainty assignment in an operational cloud-phase algorithm from arm vertically pointing active sensors |
publisher |
Copernicus Publications |
publishDate |
2016 |
url |
https://doi.org/10.5194/ascmo-2-49-2016 https://doaj.org/article/801620a1bc774acd8d1f5f0aa0c402bb |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Advances in Statistical Climatology, Meteorology and Oceanography, Vol 2, Iss 1, Pp 49-62 (2016) |
op_relation |
http://www.adv-stat-clim-meteorol-oceanogr.net/2/49/2016/ascmo-2-49-2016.pdf https://doaj.org/toc/2364-3579 https://doaj.org/toc/2364-3587 2364-3579 2364-3587 doi:10.5194/ascmo-2-49-2016 https://doaj.org/article/801620a1bc774acd8d1f5f0aa0c402bb |
op_doi |
https://doi.org/10.5194/ascmo-2-49-2016 |
container_title |
Advances in Statistical Climatology, Meteorology and Oceanography |
container_volume |
2 |
container_issue |
1 |
container_start_page |
49 |
op_container_end_page |
62 |
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