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Ensemble approach for projections of return periods of extreme water levels in Estonian waters

机译:爱沙尼亚水域极端水位返回期的集合方法

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The contribution of various drivers to the water level in the eastern Baltic Sea and the presence of outliers in the time series of observed and hindcast water level lead to large spreading of projections of future extreme water levels. We explore the options for using an ensemble of projections to more reliably evaluate return periods of extreme water levels. An example of such an ensemble is constructed by means of fitting several sets of block maxima (annual maxima and stormy season maxima) with a Generalised Extreme Value, Gumbel and Weibull distribution. The ensemble involves projections based on two data sets (resolution of 6 h and 1 h) hindcast by the Rossby Centre Ocean model (RCO; Swedish Meteorological and Hydrological Institute) and observed data from four representative sites along the Estonian coast. The observed data are transferred into the grid cells of the RCO model using the HIROMB model and a linear regression. For coastal segments where the observations represent the offshore water level well, the overall appearance of the ensembles signals that the errors of single projections are randomly distributed and that the median of the ensemble provides a sensible projection. For locations where the observed water level involves local effects (e.g. wave set-up) the block maxima are split into clearly separated populations. The resulting ensemble consists of two distinct clusters, the difference between which can be interpreted as a measure of the impact of local features on the water level observations. (C) 2014 Elsevier Ltd. All rights reserved.
机译:各种驱动因素对波罗的海东部水位的贡献以及观测和后预报水位的时间序列中存在异常值,导致对未来极端水位的预测的广泛传播。我们探索了使用整体投影来更可靠地评估极端水位返回期的选项。通过拟合几组具有极大极值,Gumbel和Weibull分布的块状最大值(年最大值和暴风雨季节最大值)来构造这样的合奏。该集合涉及基于罗斯比中心海洋模型(RCO;瑞典气象水文研究所)后预报的两个数据集(6小时和1小时的分辨率)的投影,以及从爱沙尼亚海岸的四个代表性地点观察到的数据。使用HIROMB模型和线性回归将观察到的数据转移到RCO模型的网格单元中。对于观测值代表海上水位良好的沿海地区,集合体的整体外观表明单个投影的误差是随机分布的,并且集合的中值提供了合理的预测。对于所观察到的水位涉及局部影响(例如波浪形成)的位置,区块最大值被分为明显分开的种群。最终的集合由两个不同的簇组成,它们之间的差异可以解释为衡量局部特征对水位观测值的影响的量度。 (C)2014 Elsevier Ltd.保留所有权利。

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