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Lookup NU author(s): Professor Hayley Fowler, Dr Elizabeth Lewis
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2020, The Author(s).We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) observation-based climatologies (1979–2018) of the occurrence and peak intensity of precipitation (P) at daily and 3-hourly time-scales. The climatologies were produced using neural networks trained with daily P observations from 93,138 gauges and hourly P observations (resampled to 3-hourly) from 11,881 gauges worldwide. Mean validation coefficient of determination (R2) values ranged from 0.76 to 0.80 for the daily P occurrence indices, and from 0.44 to 0.84 for the daily peak P intensity indices. The neural networks performed significantly better than current state-of-the-art reanalysis (ERA5) and satellite (IMERG) products for all P indices. Using a 0.1 mm 3 h−1 threshold, P was estimated to occur 12.2%, 7.4%, and 14.3% of the time, on average, over the global, land, and ocean domains, respectively. The highest P intensities were found over parts of Central America, India, and Southeast Asia, along the western equatorial coast of Africa, and in the intertropical convergence zone. The PPDIST dataset is available via www.gloh2o.org/ppdist.
Author(s): Beck HE, Westra S, Tan J, Pappenberger F, Huffman GJ, McVicar TR, Grundemann GJ, Vergopolan N, Fowler HJ, Lewis E, Verbist K, Wood EF
Publication type: Article
Publication status: Published
Journal: Scientific Data
Year: 2020
Volume: 7
Issue: 1
Print publication date: 01/12/2020
Online publication date: 11/09/2020
Acceptance date: 03/08/2020
Date deposited: 18/12/2020
ISSN (electronic): 2052-4463
Publisher: Nature Publishing Group
URL: https://doi.org/10.1038/s41597-020-00631-x
DOI: 10.1038/s41597-020-00631-x
PubMed id: 32917890
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