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dc.contributor.authorStanley, Thomas
dc.contributor.authorKirschbaum, Dalia B.
dc.contributor.authorHuffman, George J.
dc.contributor.authorAdler, Robert F.
dc.date.accessioned2017-06-09T16:47:16Z
dc.date.available2017-06-09T16:47:16Z
dc.date.copyright2017/04/01
dc.date.issued2017
dc.identifier.otherams-74071.pdf
dc.identifier.urihttp://onlinelibrary.yabesh.ir/handle/yetl/4216255
dc.description.abstractong-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM?s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.
dc.publisherAmerican Meteorological Society
dc.titleApproximating Long-Term Statistics Early in the Global Precipitation Measurement Era
dc.typeJournal Paper
dc.journal.volume21
dc.journal.issue3
dc.journal.titleEarth Interactions
dc.identifier.doi10.1175/EI-D-16-0025.1
dc.journal.fristpage1
dc.journal.lastpage10
dc.treeEarth Interactions:;2017:;volume( 021 ):;issue: 003
dc.contenttypeFulltext


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