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

Approximating Long-Term Statistics Early in the Global Precipitation Measurement Era AbstractLong-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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Earth Interactions American Meteorological Society

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

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1087-3562
eISSN
1087-3562
D.O.I.
10.1175/EI-D-16-0025.1
Publisher site
See Article on Publisher Site

Abstract

AbstractLong-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.

Journal

Earth InteractionsAmerican Meteorological Society

Published: Apr 23, 2017

References

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