基于卫星降水估算的厄瓜多尔气候遥相关模式的空间关联特征

D. Ballari, L. Campozano, E. Samaniego, D. Orellana
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引用次数: 1

摘要

气候遥相关显示了地球上遥远点之间遥远而大规模的关系。它们与降水的关系对于监测和预测它们可能在当地气候中产生的异常非常重要,例如影响农业、健康和水力发电的洪涝和干旱事件。与降水有关的气候遥相关已被广泛研究。然而,远距连接模式的空间关联(即具有远距连接的区域的空间描绘)一直未被关注。这种空间关联可以表征给定模式的潜在空间现象的稳定性(异质性/依赖性和统计显著性)。因此,我们的目标是利用探索性空间数据分析方法来表征与降水相关的气候遥相关模式的空间关联。基于TRMM卫星影像和气候指数,利用Moran’s I和LISA全球和局部空间关联指标检测遥相关的空间格局。Moran的I描述了不同气候指数的高度正空间关联,LISA描述了两种类型的遥相关模式。均匀型主要分布在沿海和亚马逊地区,分散型主要分布在高原地区。结果还显示,一些地区,虽然有中等到高度的遥相关影响,但具有随机的空间模式(即,非显著的空间关联)。其他区域同时表现出远连和显著的空间关联,但模式分散。这表明需要探索限制(非显著空间关联)或重申(分散模式)遥连模式的当地潜在特征(地形、方向、风和微气候)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial Association To Characterize The Climate Teleconnection Patterns In Ecuador Based On Satellite Precipitation Estimates
Climate teleconnections show remote and large-scale relationships between distant points on Earth. Their relations to precipitation are important to monitor and anticipate the anomalies that they can produce in the local climate, such as flood and drought events impacting agriculture, health, and hydropower generation. Climate teleconnections in relation to precipitation have been widely studied. Nevertheless, the spatial association of the teleconnection patterns (i.e., the spatial delineation of regions with teleconnections) has been unattended. Such spatial association allows to characterize how stable (heterogeneity/dependent and statistically significant) is the underlying spatial phenomena for a given pattern. Thus our objective was to characterize the spatial association of climate teleconnection patterns related to precipitation using an exploratory spatial data analysis approach. Global and local indicators of spatial association (Moran’s I and LISA) were used to detect spatial patterns of teleconnections based on TRMM satellite images and climate indices. Moran’s I depicted high positive spatial association for different climate indices, and LISA depicted two types of teleconnections patterns. The homogenous patterns were localized in the Coast and Amazonian regions, meanwhile the disperse patterns had a major presence in the Highlands. The results also showed some areas that, although with moderate to high teleconnection influences, had a random spatial patterns (i.e., non-significant spatial association). Other areas showed both teleconnections and significant spatial association, but with dispersed patterns. This pointed out the need to explore the local underlying features (topography, orientation, wind and micro-climates) that restrict (non-significant spatial association) or reaffirm (disperse patterns) the teleconnection patterns.
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