...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of the Atmospheric Sciences >Dynamical and microphysical retrievals from Doppler radar observations of a deep convective cloud
【24h】

Dynamical and microphysical retrievals from Doppler radar observations of a deep convective cloud

机译：从多普勒雷达对深对流云观测中进行动态和微物理反演

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A four-dimensional variational data assimilation system consisting of a three-dimensional time-dependent cloud model with both liquid and ice phase microphysics parameterization was used to assimilate radar data into a cloud model. Data of a severe thunderstorm observed during the Cooperative Huntsville Meteorological Experiment project were assimilated and results compared to a conventional analysis. The analysis system was able to retrieve all the prominent features of the storm, but differed in some of the details. However, the consistency of this retrieval dataset lent credence to the results. It was found that the algorithm was very sensitive to several coefficients in the microphysical and turbulence parameterizations. Simulations proved to be unable to reproduce the evolution of the observed storm even with parameterization coefficients set at values that produce reasonable storm evolutions. This result has implications for short-range forecasting of convective events. Such forecasts require initial fields that currently can only be derived from observations such as used in this study. The problems with assimilating radar observations point to additional work to design parameterizations that allow models to more accurately simulate actual observed storms. [References: 37]
机译：使用由三维时变云模型以及液相和冰相微物理学参数化组成的四维变分数据同化系统，将雷达数据同化为云模型。吸收了在合作的Huntsville气象实验项目中观察到的严重雷暴数据，并将结果与​​常规分析进行了比较。分析系统能够检索到风暴的所有主要特征，但是在某些细节上有所不同。但是，此检索数据集的一致性使结果可信。发现该算法对微物理和湍流参数化中的几个系数非常敏感。事实证明，即使将参数化系数设置为可产生合理风暴演变的值，模拟也无法重现观测到的风暴的演变。该结果对对流事件的短期预报有影响。此类预测需要初始字段，当前只能从本研究中使用的观察中得出。同化雷达观测的问题指出了设计参数化的额外工作，这些参数化使模型可以更准确地模拟实际观测到的风暴。 [参考：37]

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号