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PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018

Lookup NU author(s): Professor Hayley Fowler, Dr Elizabeth Lewis

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 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.


Publication metadata

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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