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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 7
Hydrol. Earth Syst. Sci., 18, 2657–2667, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Drought forecasting and warning

Hydrol. Earth Syst. Sci., 18, 2657–2667, 2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 24 Jul 2014

Research article | 24 Jul 2014

Global meteorological drought – Part 1: Probabilistic monitoring

E. Dutra1, F. Wetterhall1, F. Di Giuseppe1, G. Naumann2, P. Barbosa2, J. Vogt2, W. Pozzi3, and F. Pappenberger1 E. Dutra et al.
  • 1European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 2European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
  • 3Group on Earth Observations, Geneva, Switzerland

Abstract. Near-real-time drought monitoring can provide decision-makers with valuable information for use in several areas, such as water resources management, or international aid. One of the main constrains of assessing the current drought situation is associated with the lack of reliable sources of observed precipitation on a global scale available in near-real time. Furthermore, monitoring systems also need a long record of past observations to provide mean climatological conditions. To address these problems, a novel probabilistic drought monitoring methodology based on ECMWF probabilistic forecasts is presented, where probabilistic monthly means of precipitation were derived from short-range forecasts and merged with the long-term climatology of the Global Precipitation Climatology Centre (GPCC) data set. From the merged data set, the standardised precipitation index (SPI) was estimated. This methodology was compared with the GPCC first guess precipitation product as well as SPI calculations using the ECMWF ERA-Interim reanalysis and Tropical Rainfall Measuring Mission (TRMM) precipitation data sets. ECMWF probabilistic forecasts for near-real-time monitoring are similar to GPCC and TRMM in terms of correlation and root mean square errors, with the added value of including an estimate of the uncertainty given by the ensemble spread. The real-time availability of this product and its stability (i.e. that it does not directly depend on local rain gauges or single satellite products) are also beneficial in the light of an operational implementation.

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