气候变化领域集成服务门户(CCPortal): RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements

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DOI	10.1016/j.rse.2019.111606
	RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements
	Baez-Villanueva O.M.; Zambrano-Bigiarini M.; Beck H.E.; McNamara I.; Ribbe L.; Nauditt A.; Birkel C.; Verbist K.; Giraldo-Osorio J.D.; Xuan Thinh N.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	The accurate representation of spatio-temporal patterns of precipitation is an essential input for numerous environmental applications. However, the estimation of precipitation patterns derived solely from rain gauges is subject to large uncertainties. We present the Random Forest based MErging Procedure (RF-MEP), which combines information from ground-based measurements, state-of-the-art precipitation products, and topography-related features to improve the representation of the spatio-temporal distribution of precipitation, especially in data-scarce regions. RF-MEP is applied over Chile for 2000—2016, using daily measurements from 258 rain gauges for model training and 111 stations for validation. Two merged datasets were computed: RF-MEP3P (based on PERSIANN-CDR, ERA-Interim, and CHIRPSv2) and RF-MEP5P (which additionally includes CMORPHv1 and TRMM 3B42v7). The performances of the two merged products and those used in their computation were compared against MSWEPv2.2, which is a state-of-the-art global merged product. A validation using ground-based measurements was applied at different temporal scales using both continuous and categorical indices of performance. RF-MEP3P and RF-MEP5P outperformed all the precipitation datasets used in their computation, the products derived using other merging techniques, and generally outperformed MSWEPv2.2. The merged P products showed improvements in the linear correlation, bias, and variability of precipitation at different temporal scales, as well as in the probability of detection, the false alarm ratio, the frequency bias, and the critical success index for different precipitation intensities. RF-MEP performed well even when the training dataset was reduced to 10% of the available rain gauges. Our results suggest that RF-MEP could be successfully applied to any other region and to correct other climatological variables, assuming that ground-based data are available. An R package to implement RF-MEP is freely available online at https://github.com/hzambran/RFmerge. © 2019 Elsevier Inc.
英文关键词	Bias correction; Merging; Precipitation; Precipitation products; Random Forest; RF-MEP
语种	英语
scopus关键词	Decision trees; Merging; Precipitation (chemical); Rain gages; Topography; Bias correction; Environmental applications; Ground based measurement; Precipitation products; Probability of detection; Random forests; RF-MEP; Spatiotemporal distributions; Rain; algorithm; correction; data set; ground-based measurement; measurement method; model validation; numerical method; precipitation (climatology); raingauge; topography; TRMM; uncertainty analysis; Chile
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179464
作者单位	Institute for Technology and Resources Management in the Tropics and Subtropics (ITT), TH Köln, Cologne, Germany; Faculty of Spatial Planning, TU Dortmund University, Dortmund, Germany; Department of Civil Engineering, Universidad de la Frontera, Temuco, Chile; Center for Climate and Resilience Research, Universidad de Chile, Santiago, Chile; Department of Civil and Environmental Engineering, Princeton University, Princeton, United States; Geography Department, University of Costa Rica, San José, Costa Rica; Northern Rivers Institute, University of Aberdeen, Aberdeen, United Kingdom; UNESCO International Hydrological Programme, Paris, France; Pontificia Universidad Javeriana, Bogotá, Colombia
推荐引用方式 GB/T 7714	Baez-Villanueva O.M.,Zambrano-Bigiarini M.,Beck H.E.,et al. RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements[J],2020,239.
APA	Baez-Villanueva O.M..,Zambrano-Bigiarini M..,Beck H.E..,McNamara I..,Ribbe L..,...&Xuan Thinh N..(2020).RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements.Remote Sensing of Environment,239.
MLA	Baez-Villanueva O.M.,et al."RF-MEP: A novel Random Forest method for merging gridded precipitation products and ground-based measurements".Remote Sensing of Environment 239(2020).