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

Beck, Hylke E; Westra, Seth; Tan, Jackson; Pappenberger, Florian; Huffman, George J; McVicar, Tim R; Gründemann, Gaby J; Vergopolan, Noemi; Fowler, Hayley J; Lewis, Elizabeth; Verbist, Koen; Wood, Eric F

Beck, Hylke E; Westra, Seth; Tan, Jackson; Pappenberger, Florian; Huffman, George J; McVicar, Tim R; Gründemann, Gaby J; Vergopolan, Noemi; Fowler, Hayley J; Lewis, Elizabeth; Verbist, Koen; Wood, Eric F.

Afiliación

Beck HE; Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA. hylke.beck@gmail.com.
Westra S; School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, Australia.
Tan J; Universities Space Research Association, Columbia, Maryland, USA.
Pappenberger F; NASA Goddard Space Flight Center, Greenbelt, Maryland, USA.
Huffman GJ; Forecast Department, European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK.
McVicar TR; Mesoscale Atmospheric Processes Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA.
Gründemann GJ; CSIRO Land and Water, Black Mountain, Canberra, Australia.
Vergopolan N; Australian Research Council Centre of Excellence for Climate Extremes, Canberra, Australia.
Fowler HJ; Delft University of Technology, Water Management, Delft, Netherlands.
Lewis E; Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA.
Verbist K; School of Engineering, Newcastle University, New castle upon Tyne, UK.
Wood EF; School of Engineering, Newcastle University, New castle upon Tyne, UK.

Sci Data ; 7(1): 302, 2020 09 11.

Article en En | MEDLINE | ID: mdl-32917890

RESUMEN

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 .

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Data Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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