The european flood alert system EFAS - Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts
The european flood alert system EFAS - Part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts
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DOI：
10.5194/hess-13-141-2009
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发表时间：
2009-01-01
影响因子：
6.3
通讯作者：
Gentilini, S.
中科院分区：
文献类型：
--
作者：
Bartholmes, J. C.;Thielen, J.;Gentilini, S.
自2005年以来，欧洲洪水警报系统(EFAS)一直在欧洲委员会联合研究中心(JRC)的预运行模式下制作概率水文预报。EFAS旨在通过提前3至10天向国家水文气象服务提供中期确定性和概率性洪水预报信息来提高对跨国欧洲河流流域洪水的准备。本文是EFA发展和技能评估研究的第二部分。第一部分介绍了系统开发所采用的科学方法，以及系统开发的基本原理和预测产品。在这篇文章中，统计评估了现有的两年运行的EFAS预报，并用几个技能分数分析了EFAS预报的技巧。这一分析是基于对代理观测和预测流量之间的阈值超越进行比较的。技能的评估既考虑了连续预报期间预报信号的持续性，也不考虑预报信号的持续性。斯基尔评估方法大多来自气象学，分析还比较了EFA的概率和确定性方面。此外，还讨论了不同技能分数的效用，并说明了它们的优点和不足。分析表明，将过去的预测纳入中期预测的概率分析中是有好处的，这有效地提高了预测的技能。
Since 2005 the European Flood Alert System (EFAS) has been producing probabilistic hydrological forecasts in pre-operational mode at the Joint Research Centre (JRC) of the European Commission. EFAS aims at increasing preparedness for floods in trans-national European river basins by providing medium-range deterministic and probabilistic flood forecasting information, from 3 to 10 days in advance, to national hydro-meteorological services.This paper is Part 2 of a study presenting the development and skill assessment of EFAS. In Part 1, the scientific approach adopted in the development of the system has been presented, as well as its basic principles and forecast products. In the present article, two years of existing operational EFAS forecasts are statistically assessed and the skill of EFAS forecasts is analysed with several skill scores. The analysis is based on the comparison of threshold exceedances between proxy-observed and forecasted discharges. Skill is assessed both with and without taking into account the persistence of the forecasted signal during consecutive forecasts.Skill assessment approaches are mostly adopted from meteorology and the analysis also compares probabilistic and deterministic aspects of EFAS. Furthermore, the utility of different skill scores is discussed and their strengths and shortcomings illustrated. The analysis shows the benefit of incorporating past forecasts in the probability analysis, for medium-range forecasts, which effectively increases the skill of the forecasts.