A Spatiotemporal Water Vapor–Deep Convection Correlation Metric Derived from the Amazon Dense GNSS Meteorological Network

dc.contributor.authorAdams, David K.
dc.contributor.authorBarbosa, H. M. J.
dc.contributor.authorRíos, Karen Patricia Gaitán De Los
dc.date.accessioned2024-06-28T18:10:05Z
dc.date.available2024-06-28T18:10:05Z
dc.date.issued2017-01-01
dc.description.abstractDeep atmospheric convection, which covers a large range of spatial scales during its evolution, continues to be a challenge for models to replicate, particularly over land in the tropics. Specifically, the shallow-to-deep convective transition and organization on the mesoscale are often not properly represented in coarse-resolution models. High-resolution models offer insights on physical mechanisms responsible for the shallow-to-deep transition. Model verification, however, at both coarse and high resolution requires validation and, hence, observational metrics, which are lacking in the tropics. Here a straightforward metric derived from the Amazon Dense GNSS Meteorological Network (~100 km × 100 km) is presented based on a spatial correlation decay time scale during convective evolution on the mesoscale. For the shallow-to-deep transition, the correlation decay time scale is shown to be around 3.5 h. This novel result provides a much needed metric from the deep tropics for numerical models to replicate.
dc.description.sponsorshipWe thank Ben Lintner and Yolande Serra for their comments on the manuscript. We also thank Alain Protat and two anonymous reviewers for their constructive criticisms. Financial support for the ADGMN came through Cooperation Project 0050.0045370.08.4 between PETROBRAS and INPE (Brazil) and from the INPA/LBA (Brazil) (Principle Investigator Icaro Vitorello). H. B. acknowledges the financial support of FAPESP Grant 2013/50510-5. Financial support for this paper was also provided by UNAM PAPIIT IA100916. We also thank the students of the graduate program Clima e Ambiente (UEA/INPA) for their contributions. Data are available upon request from the corresponding author
dc.description.urihttps://journals.ametsoc.org/view/journals/mwre/145/1/mwr-d-16-0140.1.xml
dc.format.extent10 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m2h3zk-uqdj
dc.identifier.citationAdams, David K., Henrique M. J. Barbosa, and Karen Patricia Gaitán De Los Ríos. “A Spatiotemporal Water Vapor–Deep Convection Correlation Metric Derived from the Amazon Dense GNSS Meteorological Network.” Monthly Weather Review 145, no. 1 (January 1, 2017): 279–88. https://doi.org/10.1175/MWR-D-16-0140.1.
dc.identifier.urihttps://doi.org/10.1175/MWR-D-16-0140.1
dc.identifier.urihttp://hdl.handle.net/11603/34771
dc.language.isoen_US
dc.publisherAMS
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Physics Department
dc.rights© Copyright [date of publication] American Meteorological Society (AMS). For permission to reuse any portion of this work, please contact permissions@ametsoc.org. 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 (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy).
dc.titleA Spatiotemporal Water Vapor–Deep Convection Correlation Metric Derived from the Amazon Dense GNSS Meteorological Network
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-4027-1855

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