A global probabilistic dataset for monitoring meteorological droughts

This paper presents DROP, a new global probabilistic precipitation-based dataset for monitoring and early warning of meteorological drought events. DROP is operationally updated every monthly and provides probabilistic information in near–real time, that is, up to the previous month. DROP could become an important tool to inform end users across a range of socioeconomic sectors (e.g., energy and water management, insurance, agriculture, fire risk).

Accurate and timely drought information is essential to move from postcrisis to preimpact drought-risk management. A number of drought datasets are 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 realization), and input and output data partly differ between them.

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.

DROP is publicly available online. Users can retrieve the estimated SPI indices of the ensemble mean of DROP and drought confidence levels. All codes used in the production of DROP are also freely available, via the DROP archive, which ensures adherence to the Enabling Findable, Accessible, Interoperable and Reusable (FAIR) Data Project for Earth-science research.