PPDIST — Global maps OF the precipitation probability distribution
The Probability DISTribution (PPDIST) dataset represents a collection of global high-resolution (0.1°) observation-based climatologies (1979–2018) of the occurrence and peak intensity of precipitation at daily and 3‑hourly time scales.
The climatologies were produced using neural network ensembles and take advantage of the strengths of all three main precipitation data sources — satellites, reanalyses, and gauges — to obtain reliable estimates for the entire globe. For more information, see the following open-access paper:
- Beck, H. E., Westra, S., Tan, J., Pappenberger, F., Huffman, G. J., McVicar, T. R., Gründemann, G. J., Vergopolan, N., Fowler, H. J., Lewis, E., Verbist, K., and Wood, E. F.: PPDIST: global 0.1° daily and 3‑hourly precipitation probability distribution climatologies for 1979–2018, Scientific Data, in review.
The latest version of the PPDIST dataset can be downloaded here. If the dataset forms a key component of your research, we kindly ask that you give us the opportunity to comment on your results prior to publication. Please see the above-mentioned paper for details on the dataset. By using the dataset in any publication you agree to cite the above-mentioned paper.
The PPDIST dataset was developed by Hylke Beck (Princeton University and Princeton Climate Analytics, Inc.) in collaboration with Eric Wood, Seth Westra, Jackson Tan, Florian Pappenberger, George Huffman, Tim McVicar, Gaby Gründemann, Noemi Vergopolan, Hayley Fowler, Elizabeth Lewis, and Koen Verbist. The Water Center for Arid and Semi-Arid Zones in Latin America and the Caribbean (CAZALAC) and the Centro de Ciencia del Clima y la Resiliencia (CR) 2 (FONDAP 15110009) are thanked for sharing the Mexican and Chilean gauge data, respectively. We also acknowledge the gauge data providers in the Latin American Climate Assessment and Dataset (LACA&D) project: IDEAM (Colombia), INAMEH (Venezuela), INAMHI (Ecuador), SENAMHI (Peru), SENAMHI (Bolivia), and DMC (Chile). We further wish to thank Ali Alijanian and Piyush Jain for providing additional gauge data. We gratefully acknowledge the developers of the predictor datasets for producing and making available their datasets. Hylke Beck was supported in part by the U.S. Army Corps of Engineers’ International Center for Integrated Water Resources Management (ICIWaRM).