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| Titel |
Global meteorological drought – Part 1: Probabilistic monitoring |
| VerfasserIn |
E. Dutra, F. Wetterhall, F. Giuseppe, G. Naumann, P. Barbosa, J. Vogt, W. Pozzi, F. Pappenberger |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 18, no. 7 ; Nr. 18, no. 7 (2014-07-24), S.2657-2667 |
| Datensatznummer |
250120413
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| Publikation (Nr.) |
 copernicus.org/hess-18-2657-2014.pdf |
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| Zusammenfassung |
| 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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