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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 02 Oct 2019

Submitted as: research article | 02 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Predictive skill of Atmospheric Rivers in western Iberian Peninsula

Alexandre M. Ramos1, Pedro M. Sousa1, Emanuel Dutra1, and Ricardo M. Trigo1,2 Alexandre M. Ramos et al.
  • 1Instituto Dom Luiz (IDL), Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
  • 2Departamento de Meteorologia, Instituto de Geociências, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 21941-916, Brazil

Abstract. It is now clear that a large fraction of extreme precipitation and flood events across Western Europe are triggered by Atmospheric Rivers (ARs). The association between ARs and extreme precipitation days over the Iberian Peninsula has been well documented for western river basins.

Since ARs are often associated with high impact weather, it is important to study their medium-range predictability. Here we perform such an assessment using the ECMWF ensemble forecasts up to 15 days for events that made landfall in western Iberian Peninsula during the winters spanning between 2012/2013 and 2015/16. IVT and precipitation from the 51 ensemble members of the ECMWF Integrated Forecasting System (IFS) ensemble (ENS) were processed over a domain including western Europe and contiguous North Atlantic Ocean.

Metrics concerning the ARs location, intensity and orientation were computed, in order to compare the predictive skill (for different prediction lead times) of IVT and precipitation analyses in the IFS. We considered several regional boxes over Western Iberia, where the presence of ARs is detected in analysis/forecasts, enabling the construction of contingency tables and probabilistic evaluation for further objective verification of forecast accuracy. Our results indicate that the ensemble forecasts have skill to detect upcoming ARs events, which can be particularly useful to better predict potential hydrometeorological extremes. We also characterized how the ENS dispersion and confidence curves change with increasing forecast lead times for each sub-domain. The probabilistic evaluation, using ROC analysis, shows that for short lead times precipitation forecasts are more accurate than IVT forecasts, while for longer lead times this reverses (~10 days). Furthermore, we show that this reversal occurs for shorter lead times in areas where the ARs contribution is more relevant for winter precipitation totals (e.g. northwestern Iberia).

Alexandre M. Ramos et al.
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Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Alexandre M. Ramos et al.
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