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首页> 外文期刊>International Journal of Climatology: A Journal of the Royal Meteorological Society >Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall
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Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall

机译:印度夏季风季后雨季节预报的DEMETER性能评估

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摘要

On the basis of the observed all-India rainfall (AIR), Climate Prediction Center (CPC) merged analysis of precipitation (CMAP), and the multimodel rainfall simulated in the Development of a European multimodel ensemble system for seasonal to interannual prediction (DEMETER) project, the model performance in terms of forecast quality of the seasonal mean Indian summer monsoon rainfall (ISMR) was evaluated. The approach used included the individual model (single-model ensemble) comparison as well as its comparison with the multimodel ensemble (MME). The results obtained reveal that systematic biases are the main source of the low skill in predicting the ISMR, while improved reproduction of interannual variability may increase the overall forecast quality for the ISMR. The MME shows superior performance in reproducing AIR features than the single-model ensembles. This improved forecast quality achieved by the MME with respect to the ISMR variations primarily depends on model performance and secondly on ensemble size. The MME has improved capability of simulating the ISMR variations during 1972-1990 decades, whereas it exhibits a lower performance in the 1990s. The reason of this decadal varying forecast quality clearly deserves more investigation. In summary, these results suggest that the MME's increase in skill absolutely depends on enhancing the individual model quality.
机译:根据观测到的全印度降雨量(AIR),气候预测中心(CPC)合并了降水分析(CMAP),并在开发欧洲多模式集合系统以模拟季节至年际预报(DEMETER)中模拟了多模型降雨在项目中,评估了印度夏季风季平均降雨量(ISMR)的预测质量方面的模型性能。所使用的方法包括单个模型(单模型集合)比较以及与多模型集合(MME)的比较。获得的结果表明,系统偏差是预测ISMR的低技能的主要来源,而改进的年际变化再现可能会提高ISMR的总体预测质量。与单模型组合相比,MME在再现AIR功能方面显示出卓越的性能。 MME针对ISMR变化实现的这种改进的预测质量主要取决于模型性能,其次取决于集合大小。 MME改进了模拟1972-1990年ISMR变化的能力,而在1990年代表现较低。这种年代际变化的预报质量的原因显然值得进一步研究。总之,这些结果表明,MME技能的提高完全取决于提高单个模型的质量。

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