Benefits and limitations of regional multi-model ensembles for storm loss estimations
Benefits and limitations of regional multi-model ensembles for storm loss estimations
复制标题
用于风暴损失估计的区域多模式集合的优点和局限性
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
U. Ulbrich
中科院分区：
文献类型：
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作者：
M. Donat;G. Leckebusch;S. Wild;U. Ulbrich
近地面风速的空间格局和由此产生的损失潜力与严重的冬季风暴进行了研究，在多模式模拟与区域气候模式（RCMs），由ERA40再分析驱动。探讨了风暴损失计算中动态降尺度的益处和局限性，包括对多模式集合整体性能的量化，以及系统调查模式选择对集合结果的影响。对不同模式风场的比较表明，各模式的系统偏差和山区特定模式的异常都有。此外，将风暴损失模型应用于RCM风场，并根据德国观测到的年度保险损失数据验证计算的损失。一般来说，动态降尺度的明显优势是显而易见的。然而，所有RCM均未能真实模拟1起特定重大事件。如果不考虑这一特殊事件，几乎所有的模拟都显示出与观测损失的高度相关性（大于0.8），与直接从大规模再分析风场计算的损失相当。对于表现最好的模型，相当高的损失相关性高达0.95，这表明高分辨率的区域协调模型超过了同化的驱动数据的值考虑的区域。将各个RCM的计算损失组合到多模式集合中，集合平均值的性能与最佳单一模式的性能一样好。研究了所有可能的子系综，我们发现，一般来说，一个较高的最低性能得到了更大数量的系综成员，而最大性能几乎没有影响的系综大小。
Spatial patterns of near-surface wind speeds and resulting loss potentials associated with severe winter storms were investigated in multi-model simulations with regional climate mod- els (RCMs), driven by ERA40 re-analyses. The benefits and limitations of dynamical downscaling for windstorm loss calculations were explored, including a quantification of the performance of the multi-model ensemble as a whole and the systematic investigation of the influence of model selection on the ensemble results. A comparison of the wind fields in the different models revealed both sys- tematic biases in individual RCMs and model-specific anomalies over mountainous regions. Further, a storm loss model was applied to the RCM wind fields, and the calculated losses were validated against observed annual insurance loss data available for Germany. Generally, a distinct advantage from dynamical downscaling was obvious. However, all RCMs failed in realistically simulating 1 spe- cific major event. If this particular event was excluded from the considerations, almost all simulations revealed high correlations (above 0.8) with observed losses, comparable to losses calculated directly from the large-scale reanalysis wind field. For the best performing models, considerably higher loss correlations up to 0.95 were obtained, suggesting that the high-resolution RCMs exceeded the value of assimilation in the driving data for the area considered. Combining calculated losses from the indi- vidual RCMs into a multi-model ensemble, the performance of the ensemble mean was as good as the performance of the best single model. Examining all possible sub-ensembles, we found that gener- ally a higher minimum performance was obtained with a larger number of ensemble members, whereas the maximum performance was hardly affected by the ensemble size.