气候变化领域集成服务门户(CCPortal): A probabilistic gridded product for daily precipitation extremes over the United States

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DOI	10.1007/s00382-019-04636-0
	A probabilistic gridded product for daily precipitation extremes over the United States
	Risser M.D.; Paciorek C.J.; Wehner M.F.; O’Brien T.A.; Collins W.D.
发表日期	2019
ISSN	0930-7575
起始页码	2517
结束页码	2538
卷号	53 期号:2020-05-06
英文摘要	Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new “probabilistic” gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj). © 2019, The Author(s).
英文关键词	Extreme value analysis; Gaussian processes; Global Historical Climatology Network; Gridded daily precipitation; Nonparametric bootstrap; Precipitation; Spatial statistics
语种	英语
scopus关键词	bootstrapping; extreme event; Gaussian method; historical perspective; precipitation assessment; precipitation intensity; probability; spatiotemporal analysis; United States
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/146045
作者单位	Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, United States; University of California, Berkeley, CA, United States
推荐引用方式 GB/T 7714	Risser M.D.,Paciorek C.J.,Wehner M.F.,et al. A probabilistic gridded product for daily precipitation extremes over the United States[J],2019,53(2020-05-06).
APA	Risser M.D.,Paciorek C.J.,Wehner M.F.,O’Brien T.A.,&Collins W.D..(2019).A probabilistic gridded product for daily precipitation extremes over the United States.Climate Dynamics,53(2020-05-06).
MLA	Risser M.D.,et al."A probabilistic gridded product for daily precipitation extremes over the United States".Climate Dynamics 53.2020-05-06(2019).