Approximating Long-Term Statistics Early in the Global Precipitation Measurement EraSource: Earth Interactions:;2017:;volume( 021 ):;issue: 003::page 1DOI: 10.1175/EI-D-16-0025.1Publisher: American Meteorological Society
Abstract: ong-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.
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contributor author | Stanley, Thomas | |
contributor author | Kirschbaum, Dalia B. | |
contributor author | Huffman, George J. | |
contributor author | Adler, Robert F. | |
date accessioned | 2017-06-09T16:47:16Z | |
date available | 2017-06-09T16:47:16Z | |
date copyright | 2017/04/01 | |
date issued | 2017 | |
identifier other | ams-74071.pdf | |
identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4216255 | |
description abstract | ong-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. | |
publisher | American Meteorological Society | |
title | Approximating Long-Term Statistics Early in the Global Precipitation Measurement Era | |
type | Journal Paper | |
journal volume | 21 | |
journal issue | 3 | |
journal title | Earth Interactions | |
identifier doi | 10.1175/EI-D-16-0025.1 | |
journal fristpage | 1 | |
journal lastpage | 10 | |
tree | Earth Interactions:;2017:;volume( 021 ):;issue: 003 | |
contenttype | Fulltext |