PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979-2018.
Creators
- 1. Princeton University
- 2. University of Adelaide
- 3. Universities Space Research Association
- 4. European Centre for Medium-Range Weather Forecasts
- 5. Goddard Space Flight Center
- 6. NASA
- 7. Commonwealth Scientific and Industrial Research Organisation
- 8. Delft University of Technology
- 9. Newcastle University
- 10. UNESCO International Hydrological Programme, 7, Place de Fontenoy, 75352, Paris, France.
Description
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
. Machine-accessible metadata file describing the reported data:
https://doi.org/10.6084/m9.figshare.12815585
Open Access
Licence Attribution (CC BY)
Publisher Website
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Publication Details
Journal article
Journal:
Scientific data
Publisher:
Springer Science and Business Media LLC
ISSN:
20524463
Volume:
7
Pages:
302-302
Persistent Identifiers
References
Ann N Y Acad Sci. 2020 Jul;1472(1):49-75
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