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

被引:23
作者
Adams, David K. [1 ]
Barbosa, Henrique M. J. [2 ]
Patricia Gaitan De Los Rios, Karen [1 ]
机构
[1] Univ Nacl Autonoma Mexico, Ctr Ciencias Atmosfera, Circuito Exterior S-N,Ciudad Univ, Mexico City 04510, DF, Mexico
[2] Univ Sao Paulo, Inst Fis, Sao Paulo, Brazil
基金
巴西圣保罗研究基金会;
关键词
HIGH-RESOLUTION SIMULATION; DIURNAL CYCLE; CLIMATE MODELS; TIME SCALES; LAND; TRANSITION; SHALLOW; PRECIPITATION; RAINFALL; CONGESTUS;
D O I
10.1175/MWR-D-16-0140.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
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.
引用
收藏
页码:279 / 288
页数:10
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