Articles | Volume 19, issue 7
Research article
28 Jul 2015
Research article |  | 28 Jul 2015

Early warning of drought in Europe using the monthly ensemble system from ECMWF

C. Lavaysse, J. Vogt, and F. Pappenberger

Abstract. Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from the European Centre for Medium-range Weather Forecasts (ECMWF) in predicting droughts over the European continent. The Standardized Precipitation Index (SPI-1) is used to quantify the onset or likely evolution of ongoing droughts for the next month.

It can be shown that on average the extended range forecast has greater skill than the seasonal forecast, whilst both outperform climatology. No significant spatial or temporal patterns can be observed, but the scores are improved when focussing on large-scale droughts. In a second step we then analyse several different methods to convert the probabilistic forecasts of SPI into a Boolean drought warning. It can be demonstrated that methodologies which convert low percentiles of the forecasted SPI cumulative distribution function into warnings are superior in comparison with alternatives such as the mean or the median of the ensemble. The paper demonstrates that up to 40 % of droughts are correctly forecasted one month in advance. Nevertheless, during false alarms or misses, we did not find significant differences in the distribution of the ensemble members that would allow for a quantitative assessment of the uncertainty.

Short summary
This paper assesses the predictability of meteorological droughts over Europe 1 month in advance using ensemble prediction systems. It has been shown that, on average and using the most relevant method, 40 % of droughts in Europe are correctly forecasted, with less than 25 % false alarms. This study is a reference for other studies that are motivated to improving the drought forecasting.