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dc.contributor.authorTurco, Marco
dc.contributor.authorJerez, Sonia
dc.contributor.authorDonat, Markus
dc.contributor.authorToreti, Andrea
dc.contributor.authorVicente-Serrano, Sergio M.
dc.contributor.authorDoblas-Reyes, Francisco
dc.contributor.otherBarcelona Supercomputing Center
dc.identifier.citationTurco, M. [et al.]. A global probabilistic dataset for monitoring meteorological droughts. "Bulletin of the American Meteorological Society", 2020, vol. 101, núm. 10, p. E1628-E1644.
dc.description.abstractDROP is a global land dataset to monitor meteorological drought that gathers an ensemble of observation-based datasets providing near-real time estimates with associated uncertainty using a probabilistic approach. Accurate and timely drought information is essential to move from post-crisis to pre-impact drought-risk management. A number of drought datasets is already available. They cover the last three decades and provide data in near-real time (using different sources), but they are all ”deterministic” (i.e. single realisation), and input and output data partly differ between them. Here we first evaluate the quality of long-term and continuous climate data for timely meteorological drought monitoring considering the Standardized Precipitation Index. Then, by applying an ensemble approach, mimicking weather/climate prediction studies, we develop DROP (DROught Probabilistic), a new global land gridded dataset, in which an ensemble of observations-based datasets is used to obtain the best near-real time estimate together with its associated uncertainty. This approach makes the most of the available information and brings it to the end-users. The high-quality and probabilistic information provided by DROP is useful for monitoring applications, and may help to develop global policy decisions on adaptation priorities in alleviating drought impacts, especially in countries where meteorological monitoring is still challenging.
dc.description.sponsorshipM.T. has received funding from the European Union’s Horizon 2020 Research And Innovation Programme under the Marie Skłodowska-Curie Grant Agreement 740073 (CLIM4CROP project) and from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00), which is cofinanced with the European Regional Development Fund (ERDF/FEDER). S.J. was supported by the Spanish Ministry of Science, Innovation and Universities through the project EASE (RTI2018-100870-A-I00), the Fundación Séneca—Regional Agency for Science and Technology of Murcia through the CLIMAX project (20642/JLI/18) and by the Plan Propio de Investigación of the University of Murcia (Grant UMU-2017-10604). M.G.D. acknowledges funding by the Spanish Ministry of Science, Innovation and Universities Ramón y Cajal Grant Reference RYC-2017-22964. The authors thank the data providers listed in Table 1 for providing access to these datasets. Special thanks to Dr. Meng Zhao to provide the GRACE data and Dr. Hong Xuan Do for providing R scripts to read and process the GSIM data.
dc.format.extent17 p.
dc.publisherAmerican Meteorological Society
dc.rightsAttribution 3.0 Spain
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.subjectÀrees temàtiques de la UPC::Enginyeria agroalimentària::Ciències de la terra i de la vida
dc.subject.lcshDrought forecasting
dc.subject.otherDROP (DROught Probabilis-tic)
dc.subject.otherGridded dataset
dc.subject.otherMeteorological drought
dc.subject.otherProbabilistic information
dc.titleA global probabilistic dataset for monitoring meteorological droughts
dc.subject.lemacBases de dades
dc.subject.lemacProbabilitats -- Models matemàtics
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/740073/EU/Climate monitoring and seasonal forecast for global crop production/CLIM4CROP
local.citation.publicationNameBulletin of the American Meteorological Society

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Attribution 3.0 Spain
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