利用2007—2015年济南市区及历城区自动气象观测站的逐小时降水量资料,以及常规高空、地面观测资料,统计了198次短时强降水过程的范围和强度特征,年际、月际变化特征,按照短时强降水发生时的天气形势和影响系统,分为切变线型、低槽冷锋...利用2007—2015年济南市区及历城区自动气象观测站的逐小时降水量资料,以及常规高空、地面观测资料,统计了198次短时强降水过程的范围和强度特征,年际、月际变化特征,按照短时强降水发生时的天气形势和影响系统,分为切变线型、低槽冷锋型、西风槽型、冷涡型、台风外围型及无系统型6类,并分析了不同类型和不同范围短时强降水的关键环境参量。研究表明:短时强降水的强度与范围有较好的相关性,7月中旬—8月中旬出现强降水的次数最多;切变线型短时强降水发生范围与强度分布最广,7、8月的低槽冷锋型过程极易造成大范围高强度降水;地面露点(Td)、850 h Pa假相当位温(θse)、对流有效位能(CAPE)以及暖云层厚度能较好地区分不同范围的短时强降水过程。在天气分型的基础上,结合不同降水范围和不同降水类型环境参量箱线图与阈值表,可为济南市区短时强降水的预报提供有价值的参考。展开更多
Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is propose...Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is proposed. To this end, the coupling of the artificial neural network （ANN） with the Xin＇anjiang conceptual model with a view to enhance the quality of its flow forecast is presented. The approach uses the latest observations and residuals in runoff/discharge forecasts from the Xin＇anjiang model. The two complementary models （Xin＇anjiang ＆ ANN） are used in such a way that residuals of the Xin＇anjiang model are forecasted by a neural network model so that flow forecasts can be improved as new observations come in. For the complementary neural network, the input data were presented in a patterned format to conform to the calibration regime of the Xin＇anjiang conceptual model, using differing variants of the neural network scheme. The results show that there is a substantial improvement in the accuracy of the forecasts when the complementary model was operated on top of the Xin＇anjiang conceptual model as compared with the results of the Xin＇anjiang model alone.展开更多
文摘利用2007—2015年济南市区及历城区自动气象观测站的逐小时降水量资料,以及常规高空、地面观测资料,统计了198次短时强降水过程的范围和强度特征,年际、月际变化特征,按照短时强降水发生时的天气形势和影响系统,分为切变线型、低槽冷锋型、西风槽型、冷涡型、台风外围型及无系统型6类,并分析了不同类型和不同范围短时强降水的关键环境参量。研究表明:短时强降水的强度与范围有较好的相关性,7月中旬—8月中旬出现强降水的次数最多;切变线型短时强降水发生范围与强度分布最广,7、8月的低槽冷锋型过程极易造成大范围高强度降水;地面露点(Td)、850 h Pa假相当位温(θse)、对流有效位能(CAPE)以及暖云层厚度能较好地区分不同范围的短时强降水过程。在天气分型的基础上,结合不同降水范围和不同降水类型环境参量箱线图与阈值表,可为济南市区短时强降水的预报提供有价值的参考。
文摘Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is proposed. To this end, the coupling of the artificial neural network （ANN） with the Xin＇anjiang conceptual model with a view to enhance the quality of its flow forecast is presented. The approach uses the latest observations and residuals in runoff/discharge forecasts from the Xin＇anjiang model. The two complementary models （Xin＇anjiang ＆ ANN） are used in such a way that residuals of the Xin＇anjiang model are forecasted by a neural network model so that flow forecasts can be improved as new observations come in. For the complementary neural network, the input data were presented in a patterned format to conform to the calibration regime of the Xin＇anjiang conceptual model, using differing variants of the neural network scheme. The results show that there is a substantial improvement in the accuracy of the forecasts when the complementary model was operated on top of the Xin＇anjiang conceptual model as compared with the results of the Xin＇anjiang model alone.
