气候变化领域集成服务门户(CCPortal): Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina

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DOI	10.1016/j.atmosres.2021.105482
	Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina
	Hurtado S.I.; Zaninelli P.G.; Agosta E.A.; Ricetti L.
发表日期	2021
ISSN	0169-8095
卷号	254
英文摘要	Precipitation plays a crucial role from a social and economic perspective in Subtropical Argentina (STAr). Therefore, it renders the need for continuous and reliable precipitation records to develop serious climatological researches. However, precipitation records in this region are frequently inhomogeneous and scarce, which makes it necessary to deal with data filling methods. Choosing the best method to complete precipitation data series relies on rain gauge network density and on the complexity of orography, among other factors. Most comparative-method studies in the literature are focused on dense station networks while, contrastingly, the STAr's station network density is remarkably poor (between 10 and 1000 times lower). The research aims at assessing the performance of several interpolation methods in STAr. In this sense, the performance of a large number of interpolation methods was evaluated for dry and wet seasons, interpolating raw monthly data and their anomalies applied to different time-series subsets. In general, most methods performances improve when applied to anomalies in the seasonal time-series subset. Multiple Linear Regression (MLR) stands out as the method with the best performance for infilling precipitation records for most of the regions regardless of orography or season. Despite the bibliography invokes that kriging interpolation methods are the best ones, in this work the performance of kriging methods was similar to the one of the Inverse Distance Weighted method (IDW) and the Angular Distance Weighted method (ADW, the method used to generate CRU precipitation dataset). © 2021 Elsevier B.V.
英文关键词	Interpolation methods; Missing data; Monthly precipitation; Scarce data; Time series
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/236830
作者单位	Facultad de Ciencias Astronómicas y Geofísicas, Universidad de La Plata, La Plata, Buenos Aires, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina; Centro de Investigaciones del Mar y la Atmósfera (CIMA) CONICET-UBA, Universidad de Buenos Aires, Buenos Aires, Argentina; Instituto Franco-Argentino sobre Estudios de Clima y sus Impactos (UMI3351-IFAECI/CNRS-CONICET-UBA), Buenos Aires, Argentina
推荐引用方式 GB/T 7714	Hurtado S.I.,Zaninelli P.G.,Agosta E.A.,et al. Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina[J],2021,254.
APA	Hurtado S.I.,Zaninelli P.G.,Agosta E.A.,&Ricetti L..(2021).Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina.Atmospheric Research,254.
MLA	Hurtado S.I.,et al."Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina".Atmospheric Research 254(2021).