Mapping TRMM TMPA into average recurrence interval for monitoring extreme precipitation events

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[15588432 - Journal of Applied Meteorology and Climatology] Mapping TRMM TMPA into Average Recurrence Interval for Monitoring Extreme Precipitation Events.pdf (7.92 MB)

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https://journals.ametsoc.org/view/journals/apme/54/5/jamc-d-14-0269.1.xml

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https://doi.org/10.1175/JAMC-D-14-0269.1
 http://hdl.handle.net/11603/25097

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UMBC Joint Center for Earth Systems Technology (JCET)

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https://orcid.org/0000-0002-7812-851X

Date

2015-05-01

Type of Work

journal articles
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Citation of Original Publication

Zhou, Yaping, William K. M. Lau, and George J. Huffman. "Mapping TRMM TMPA into Average Recurrence Interval for Monitoring Extreme Precipitation Events", Journal of Applied Meteorology and Climatology 54, 5 (2015): 979-995, accessed Jun 22, 2022, https://doi.org/10.1175/JAMC-D-14-0269.1

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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

prototype online extreme precipitation monitoring system is developed from the TRMM TMPA near-real-time precipitation product. The system utilizes estimated equivalent average recurrence interval (ARI) for up-to-date precipitation accumulations from the past 1, 2, 3, 5, 7, and 10 days to locate locally severe events. The mapping of precipitation accumulations into ARI is based on local statistics fitted into generalized extreme value (GEV) distribution functions. Initial evaluation shows that the system captures historic extreme precipitation events quite well. The system provides additional rarity information for ongoing precipitation events based on local climatology that could be used by the general public and decision makers for various hazard management applications. Limitations of the TRMM ARI due to short record length and data accuracy are assessed through comparison with long-term high-resolution gauge-based rainfall datasets from the NOAA Climate Prediction Center and the Asian Precipitation–Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) project. TMPA-based extreme climatology captures extreme distribution patterns from gauge data, but a strong tendency to overestimate from TMPA over regimes of complex orography exists.