The prediction of supercooled large drops by a microphysics and a machine learning model for the ICICLE field campaign

Jensen, Anders A.; Weeks, Courtney; Xu, Mei; Landolt, Scott; Korolev, Alexei; Wolde, Mengistu; DiVito, Stephanie

doi:https://open-science.canada.ca/handle/123456789/2998

The prediction of supercooled large drops by a microphysics and a machine learning model for the ICICLE field campaign

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dc.contributor.author: Jensen, Anders A.; Weeks, Courtney; Xu, Mei; Landolt, Scott; Korolev, Alexei; Wolde, Mengistu; DiVito, Stephanie
dc.date.accepted: 2023-04-17
dc.date.accessioned: 2024-10-03T15:11:30Z
dc.date.available: 2024-10-03T15:11:30Z
dc.date.issued: 2023-07-01
dc.date.submitted: 2022-06-11
dc.description.abstract - en: The prediction of supercooled large drops (SLD) from the Thompson–Eidhammer (TE) microphysics scheme—run as part of the High-Resolution Rapid Refresh (HRRR) model—is evaluated with observations from the In-Cloud Icing and Large drop Experiment (ICICLE) field campaign. These observations are also used to train a random forest machine learning (ML) model, which is then used to predict SLD from several variables derived from HRRR model output. Results provide insight on the limitations and benefits of each model. Generally, the ML model results in an increase in the probability of detection (POD) and false alarm rate (FAR) of SLD compared to prediction from TE microphysics. Additionally, the POD of SLD increases with increasing forecast lead time for both models, likely since clouds and precipitation have more time to develop as forecast length increases. Since SLD take time to develop in TE microphysics and may be poorly represented in short-term (<3 h) forecasts, the ML model can provide improved short-term guidance on supercooled large-drop icing conditions. Results also show that TE microphysics predicts a frequency of SLD in cold (<−10°C) or high ice water content (IWC) environments that is too low compared to observations, whereas the ML model better captures the relative frequency of SLD in these environments.
dc.description.fosrcfull - en: Copyright 2023 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact <a href = "mailto: permissions@ametsoc.org">permissions@ametsoc.org</a>. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (<a href="https://www.copyright.com">https://www.copyright.com</a>). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (<a href="https://www.ametsoc.org/PUBSCopyrightPolicy">https://www.ametsoc.org/PUBSCopyrightPolicy</a>)
dc.description.fosrcfull-fosrctranslation - fr: Droit d'auteur 2023 American Meteorological Society (AMS). Pour obtenir l'autorisation de réutiliser toute partie de cette œuvre, veuillez contacter <a href = "mailto: permissions@ametsoc.org">permissions@ametsoc.org</a>. Toute utilisation d'un élément de cette œuvre considérée comme une "utilisation équitable" par l'article 107 de la loi américaine sur le droit d'auteur (U.S. Copyright Act, 17 U.S. Code § 107) ou satisfaisant aux conditions énoncées à l'article 108 de la loi américaine sur le droit d'auteur (U.S. Copyright Act, 17 U.S. Code § 108) ne requiert pas l'autorisation de l'AMS. La republication, la reproduction systématique, l'affichage sous forme électronique, par exemple sur un site web ou dans une base de données interrogeable, ou toute autre utilisation de ce matériel, à l'exception de ce qui est exempté par la déclaration ci-dessus, nécessite une autorisation écrite ou une licence de la part de l'AMS. Toutes les revues et monographies de l'AMS sont enregistrées auprès du Copyright Clearance Center (<a href="https://www.copyright.com">https://www.copyright.com</a>). Des détails supplémentaires sont prévu dans la déclaration de politique de droit d'auteur de l'AMS, disponible sur le site web de l'AMS (<a href="https://www.ametsoc.org/PUBSCopyrightPolicy">https://www.ametsoc.org/PUBSCopyrightPolicy</a>)
dc.identifier.doi: https://doi.org/10.1175/WAF-D-22-0105.1
dc.identifier.issn: 0882-8156; 1520-0434
dc.identifier.uri: https://open-science.canada.ca/handle/123456789/2998
dc.language.iso: en
dc.publisher: American Meteorological Society
dc.rights.openaccesslevel - en: Green
dc.rights.openaccesslevel - fr: Vert
dc.subject - en: Nature and environment; Science and technology
dc.subject - fr: Nature et environnement; Sciences et technologie
dc.subject.en - en: Nature and environment; Science and technology
dc.subject.fr - fr: Nature et environnement; Sciences et technologie
dc.title - en: The prediction of supercooled large drops by a microphysics and a machine learning model for the ICICLE field campaign
dc.type - en: Article
dc.type - fr: Article
local.article.journalissue: 7
local.article.journaltitle: Weather and Forecasting
local.article.journalvolume: 383
local.pagination: 1107–1124
local.peerreview - en: Yes
local.peerreview - fr: Oui
local.requestdoi: No

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