...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of hydrometeorology >Radar Vertical Profile of Reflectivity Correction with TRMM Observations Using a Neural Network Approach
【24h】

Radar Vertical Profile of Reflectivity Correction with TRMM Observations Using a Neural Network Approach

机译：使用神经网络方法进行TRMM观测的反射率校正的雷达垂直剖面

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

Complex terrain poses challenges to the ground-based radar quantitative precipitation estimation (QPE) because of partial or total blockages of radar beams in the lower tilts. Reflectivities from higher tilts are often used in the QPE under these circumstances and biases are then introduced due to vertical variations of reflectivity. The spaceborne Precipitation Radar (PR) on board the Tropical Rainfall Measuring Mission (TRMM) satellite can provide good measurements of the vertical structure of reflectivity even in complex terrain, but the poor temporal resolution of TRMM PR data limits their usefulness in real-time QPE. This study proposes a novel vertical profile of reflectivity (VPR) correction approach to enhance ground radar-based QPEs in complex terrain by integrating the spaceborne radar observations. In the current study, climatological relationships between VPRs from an S-band Doppler weather radar located on the east coast of Taiwan and the TRMM PR are developed using an artificial neural network (ANN). When a lower tilt of the ground radar is blocked, higher-tilt reflectivity data are corrected with the trained ANN and then applied in the rainfall estimation. The proposed algorithm was evaluated with three typhoon precipitation events, and its preliminary performance was evaluated and analyzed.
机译：复杂的地形由于较低倾斜下雷达波束的部分或全部阻塞，对地面雷达定量降水估计（QPE）构成了挑战。在这种情况下，QPE通常会使用较高倾斜度产生的反射率，然后由于反射率的垂直变化而引入偏差。热带降雨测量任务（TRMM）卫星上的星载降水雷达（PR）即使在复杂的地形中也可以提供反射率垂直结构的良好测量，但是TRMM PR数据的时间分辨率差限制了它们在实时QPE中的实用性。这项研究提出了一种新颖的反射率垂直剖面（VPR）校正方法，通过整合星载雷达观测来增强复杂地形中基于地面雷达的QPE。在当前的研究中，使用人工神经网络（ANN）开发了位于台湾东海岸的S波段多普勒天气雷达的VPR与TRMM PR之间的气候关系。当地面雷达的较低倾斜被阻止时，较高倾斜的反射率数据将通过训练的ANN进行校正，然后应用于降雨估算中。对该算法进行了3次台风降水事件的评估，并对其初步性能进行了分析。

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