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Application of multimodel ensemble techniques for real time district level rainfall forecasts in short range time scale over Indian region

机译:多模型集成技术在印度地区短时间尺度上实时地区水平降雨预报中的应用

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During the summer monsoon (1 June to 30 September) 2007, real-time district level rainfall forecasts in short-range time scale were generated for Indian region applying multimodel ensemble technique. The pre-assigned grid point weights on the basis of correlation coefficients (CC) between the observed values and forecast values are determined for each constituent model at the resolution of 0.5p c 0.5p utilizing two seasons datasets (1 June to 30 September, 2005 and 2006), and the multimodel ensemble forecasts (day 1 and day 2 forecasts) are generated at the same resolution on a real-time basis. The ensemble forecast fields are then used to prepare forecasts for each district taking the average value of all grid points falling in a particular district. In this paper we examined the performance skill of the multimodel ensemble-based real-time district level short-range forecast of rainfall. It has clearly emerged from the results that the multimodel ensemble technique reported in this study is superior to each ensemble member. District wise performance of the ensemble rainfall forecast reveals that the technique, in general, is capable of providing reasonably good forecast skill over most districts of the country, particularly over the districts where the monsoon systems are dominant. Though the procedure shows appreciable skill to predict occurrence or non-occurrence of rainfall at the district level, it always underestimates rainfall amount, particularly in heavy rainfall events. Possible reasons of this failure may be due to model bias and poor data assimilation procedure.
机译:在2007年夏季风(6月1日至9月30日)期间,采用多模式合奏技术为印度地区生成了短时标范围内的实时区域水平降雨预报。利用两个季节数据集(2005年6月1日至9月30日,以观测值与预测值之间的相关系数(CC)为基础,以0.5%0.5p的分辨率为每个组成模型确定预先分配的网格点权重) (2006年),并以相同的分辨率实时生成多模型集合预测(第1天和第2天预测)。然后,使用集合预测字段为每个区域准备预测,并采用落入特定区域的所有网格点的平均值。在本文中,我们研究了基于多模型集成的实时地区水平降雨短期预报的性能技巧。从结果中可以明显看出,本研究报告的多模型集成技术要优于每个集成成员。整体降雨预报的区域明智表现表明,该技术通常能够在全国大部分地区,尤其是在季风系统占主导的地区,提供相当好的预报技能。尽管该程序显示了在地区级别预测降雨发生与否的明显技巧,但它总是低估了降雨量,特别是在强降雨事件中。失败的可能原因可能是由于模型偏差和不良的数据同化程序。

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