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

Earth Interactions

Robert F. Adler¹  Dalia B. Kirschbaum²  George J. Huffman³  Thomas Stanley⁴ 

[1] Earth System Sciences Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland;Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland;Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland;Universities Space Research Association, and Goddard Earth Sciences Technology and Research, Columbia, and Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland

关键词: Central America;    Satellite observations;    Statistical techniques;    Model evaluation/performance;    Model output statistics;   

DOI  :  10.1175/EI-D-16-0025.1

学科分类：地球科学(综合)

来源: American Geophysical Union

【 摘 要 】

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.

【 授权许可】

Unknown