PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018
Hylke E. Beck (Princeton University)
Seth Westra (University of Adelaide)
Jackson Tan (Universities Space Research Association, NASA Goddard Space Flight Center)
Florian Pappenberger (European Centre for Medium Range Weather Forecasts)
George J. Huffman (NASA Goddard Space Flight Center)
Tim R. McVicar (CSIRO - Land and Water, Dutton Park, The ARC Centre of Excellence for Climate Extremes)
Gaby J. Gründemann (TU Delft - Water Resources)
Noemi Vergopolan (Princeton University)
Hayley J. Fowler (The University of Newcastle, Australia)
Elizabeth Lewis (The University of Newcastle, Australia)
Koen Verbist (UNESCO International Hydrological Programme)
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Abstract
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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