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基于有限区域中尺度暴雨预报模式AREM,利用常德、荆州、宜昌3部多普勒雷达及武汉数字化天气雷达联合反演的1h降水量资料和宜昌、荆州多普勒雷达反演的水平风场资料,采用3种不同的初值方案:Grapes-3DVAR同化方案、Barnes客观分析法及Barnes-3DVAR同化方案,对2002年7月22日20:00—23日20:00发生在长江流域的一次大暴雨过程进行了多普勒雷达估算降水和反演风场在不同初值方案下对降水预报影响的数值试验,结果表明:(1)采用相同资料源(探空 T213L31分析场),3种初值方案降水模拟效果差别显著。整体而言,融合了Barnes和3DVAR两种方案的Barnes-3DVAR初值方案降水模拟效果最好:雨带模拟完整,位置与实况完全一致;降水中心中尺度结构清晰,位置、强度与实况接近。(2)Barnes-3DVAR同化方案与Grapes-3DVAR同化方案都能同化雷达降水和风场资料,但两者降水预报结果相差很大。Barnes-3DVAR方案下的降水模拟结果明显好于Grapes-3DVAR同化方案。(3)Grapes-3DVAR同化方案下,在控制试验的基础上增加雷达降水资料的同化,24h降水模拟效果有所改善;增加雷达风场资料的同化,仅部分改善模拟效果,某些地方效果反而变差;同时增加雷达降水和风场资料的同化,降水模拟效果反而明显变差。Barnes-3DVAR同化方案则不然,无论雷达降水资料和风场资料单独同化还是两种资料同时同化,降水模拟效果都有不同程度的改进,特别是对雷达降水和风场资料同时同化的模拟结果是各种试验中效果最好的。(4)融合Barnes客观分析方法和Grapes-3DVAR同化方案的Barnes-3DVAR同化方案,是同化雷达降水和风场资料的一种新的、有效的初值方案。  相似文献   
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王丽荣  王立荣 《气象》2017,43(9):1152-1159
以石家庄2006—2008年77次降水过程为例,分析了海拔高度和距雷达站水平距离对定量估测降水的影响,给出了估测降水时反射率因子取值的最佳高度以及距离高度订正值。对比了经验公式法和最优化法分别在有无距离高度订正4种情况下的估测效果,依据估测效果,设计了雷达定量估测降水最佳方案:若反射率因子小于30 dBz,则不进行距离高度订正,仅采用最优化法估测降水;若反射率因子大于30 dBz,则首先对反射率因子进行距离高度订正,然后采用最优化法估测降水。实例检验表明,该方案对单站10 mm·h-1以上的强降水、过程降水量和区域降水量估测效果较好。  相似文献   
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On the basis of the joint estimated 1-h precipitation from Changde, Jingzhou, and Yichang Doppler radars as well as Wuhan digital radar, and the retrieved wind fields from Yichang and Jingzhou Doppler radars, a series of numerical experiments with an advanced regionalη-coordinate model (AREM) under different model initial schemes, i.e., Grapes-3DVAR, Barnes objective analysis, and Barnes-3DVAR, are carried out for a torrential rain process occurring along the Yangtze River in the 24-h period from 2000 BT 22 July 2002 to investigate the effects of the Doppler-radar estimated rainfall and retrieved winds on the rainfall forecast. The main results are as follows: (1) The simulations are obviously different under three initial schemes with the same data source (the radiosounding and T213L31 analysis). On the whole, Barnes-3DVAR, which combines the advantages of the Barnes objective analysis and the Grapes-3DVAR method, gives the best simulations: well-simulated rain band and clear mesoscale structures, as well as their location and intensity close to observations. (2) Both Barnes-3DVAR and Grapes-3DVAR schemes are able to assimilate the Doppler-radar estimated rainfall and retrieved winds, but differences in simulation results are very large, with Barnes-3DVAR's simulation much better than Grapes-3DVAR's. (3) Under Grapes-3DVAR scheme, the simulation of 24-h rainfall is improved obviously when assimilating the Doppler-radar estimated precipitation into the model in compared with the control experiment; but it becomes a little worse when assimilating the Doppler-radar retrieved winds into the model, and it becomes worse obviously when assimilating the Doppler-radar estimated precipitation as well as retrieved winds into the model. However, the simulation is different under Barnes-3DVAR scheme. The simulation is improved to a certain degree no matter assimilating the estimated precipitation or retrieved winds, or both of them. The result is the best when assimilating both of them into the model. And (4) Barnes-3DVAR is a new and efficient initial scheme for assimilating the radar estimated rainfall and retrieved winds.  相似文献   
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