全球产品能否捕捉Galápagos岛屿的降水变率?基于气候时间序列分量的评估

IF 2.5 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
María Lorena Orellana-Samaniego, Rolando Célleri, Jörg Bendix, Nazli Turini, Daniela Ballari
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引用次数: 0

摘要

像Galápagos岛屿这样的小岛屿极易受到可用水变化的影响,从而影响生态系统和社区。了解时间降水变率至关重要,但由于地面观测有限,具有挑战性。本研究在月尺度上评估了五种全球降水产品(卫星、再分析和多源产品),通过分析三个气候时间序列成分:季节性、异常和趋势,补充了基于地面观测的传统评估,这些成分捕捉了与气候应用相关的长期降水变率的不同方面。分析的重点是圣克鲁斯和圣Cristóbal群岛,那里有长期的地面数据,包括整个群岛的全球产品的空间比较。结果表明,再分析和多源产品(ERA5-Land、MSWEP、MSWX)总体优于卫星产品(CHIRPS、persansn - ccs - cdr)。例如,在凉爽的低地,再分析和多源产品的相关值在0.81到0.94之间,偏差在- 0.52%到- 40.3%之间,检测概率在0.76到0.96之间。这些产品在降水季节性、异常和趋势检测方面与地面数据具有较高和中等的一致性。相比之下,卫星产品的相关值较低,在0.52 ~ 0.86之间,低估偏差较大(- 10.86% ~ - 75.43%),探测概率较低(0.22 ~ 0.32),在降水异常和趋势方面与地面数据仅中等或不一致,在季节性方面也不一致。所有全球降水产品在代表高原降水季节性方面都表现出显著的局限性。基于组件的评估是对传统评估的补充,可以更深入地了解错误是如何随时间分布的。这种综合方法支持在数据匮乏的岛屿地区(如Galápagos)为气候分析和水资源管理更明智地选择降水产品。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Can Global Products Capture Precipitation Variability in the Galápagos Islands? An Assessment Based on Climatic Time-Series Components

Can Global Products Capture Precipitation Variability in the Galápagos Islands? An Assessment Based on Climatic Time-Series Components

Can Global Products Capture Precipitation Variability in the Galápagos Islands? An Assessment Based on Climatic Time-Series Components

Can Global Products Capture Precipitation Variability in the Galápagos Islands? An Assessment Based on Climatic Time-Series Components

Small islands such as the Galápagos Islands are highly vulnerable to changes in water availability, affecting ecosystems and communities. Understanding temporal precipitation variability is crucial but challenging due to limited ground-based observations. This study evaluates five global precipitation products (satellite, reanalysis and multi-source products) at a monthly scale, complementing conventional assessment against ground-based observations with the analysis of three climatic time-series components: seasonality, anomalies, and trends, which capture distinct aspects of long-term precipitation variability relevant to climate applications. The analysis focuses on Santa Cruz and San Cristóbal Islands, where long-term ground data are available, and includes a spatial comparison of global products across the entire archipelago. Results showed that reanalysis and multi-source products (ERA5-Land, MSWEP, MSWX) generally outperformed satellite-based products (CHIRPS, PERSIANN-CCS-CDR). For example, in the cool lowlands, reanalysis and multi-source products achieved correlation values between 0.81 and 0.94, bias ranging from −0.52% to −40.3%, and probability of detection between 0.76 and 0.96. These products showed high and medium agreement with ground data in precipitation seasonality, anomalies, and trend detection. In contrast, satellite-based products revealed lower correlation values between 0.52 and 0.86, a higher underestimation bias (−10.86% to −75.43%), a lower probability of detection (0.22–0.32), and only medium or no agreement with ground data in precipitation anomalies and trends, with no agreement in seasonality. All global precipitation products exhibited significant limitations in representing precipitation seasonality in the highlands. The component-based assessment complements conventional evaluation, offering deeper insight into how errors are distributed over time. This integrated approach supports a more informed selection of precipitation products for climate analysis and water resource management in data-scarce island regions like Galápagos.

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来源期刊
Meteorological Applications
Meteorological Applications 地学-气象与大气科学
CiteScore
5.70
自引率
3.70%
发文量
62
审稿时长
>12 weeks
期刊介绍: The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including: applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits; forecasting, warning and service delivery techniques and methods; weather hazards, their analysis and prediction; performance, verification and value of numerical models and forecasting services; practical applications of ocean and climate models; education and training.
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