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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Autor(es):
Adams, David K. ; Barbosa, Henrique M. J. ; Patricia Gaitan De Los Rios, Karen
Número total de Autores: 3
Tipo de documento: Artigo Científico
Fonte: MONTHLY WEATHER REVIEW; v. 145, n. 1, p. 279-288, JAN 2017.
Citações Web of Science: 3
Resumo

Deep 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 (similar to 100 km x 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. (AU)

Processo FAPESP: 13/50510-5 - Pesquisa colaborativa Brasil-EUA: modificações causadas pela poluição antrópica na química da atmosfera e na microfísica de partículas da floresta tropical durante as campanhas intensivas do GoAmazon
Beneficiário:Henrique de Melo Jorge Barbosa
Linha de fomento: Auxílio à Pesquisa - Programa de Pesquisa sobre Mudanças Climáticas Globais - Regular