Approximating Long-Term Statistics Early in the Global Precipitation Measurement Era

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https://doi.org/10.1175/EI-D-16-0025.1
 http://hdl.handle.net/11603/33370

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https://orcid.org/0000-0003-2288-0363

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2017-04-01

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journal articles
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Citation of Original Publication

Stanley, Thomas, Dalia B. Kirschbaum, George J. Huffman, and Robert F. Adler. “Approximating Long-Term Statistics Early in the Global Precipitation Measurement Era.” Earth Interactions 21, no. 3 (April 1, 2017): 1–10. https://doi.org/10.1175/EI-D-16-0025.1.

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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract

Abstract Long-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.