| 多普勒天气雷达资料同化对江淮暴雨模拟的影响 |
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| 引用本文： | 张新忠,陈军明,赵平.多普勒天气雷达资料同化对江淮暴雨模拟的影响[J].应用气象学报,2015,26(5):555-566. |
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| 作者姓名： | 张新忠 陈军明 赵平 |
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| 作者单位： | 1.中国气象科学研究院灾害天气重点实验室, 北京 100081 |
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| 基金项目： | 公益性行业（气象）科研专项(GYHY201406001)，中国气象科学研究院基本科研业务费重点项目（2014Z005，2013Z004） |
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| 摘 要： | 利用GSI同化系统 (Gridpoint Statistical Interpolation System) 对我国多普勒天气雷达资料进行直接循环同化分析，并采用WRF-ARW 3.5.1中尺度模式对2013年我国夏季江淮流域典型暴雨过程进行模拟试验。结果表明:经过质量控制的雷达径向风、反射率因子资料经GSI同化系统同化后，可形成合理的分析增量。仅同化径向风，模拟的风场与实况更接近，模拟的降雨落区与观测雨带位置更加接近。仅同化反射率因子，对水平风场的直接调整比较小，通过水凝物含量调整，对水平和垂直风场进行调整，对降水的落区影响较小，主要影响模拟的降水强度。同时同化两种资料，能更好地反映风场特征，并改善强降水的落区和强度的模拟。模拟改善最明显是在积分12~36 h时段内，该时段有效雷达资料量较多，表明同化雷达资料对暴雨模拟确实具有正效果。
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| 关 键 词： | 中尺度模式 多普勒天气雷达 GSI同化系统 云分析 暴雨 |
| 收稿时间： | 2015-04-16 |
Impacts of Doppler Radar Data Assimilation on the Simulation of Severe Heavy Rainfall Events |
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| Zhang Xinzhong,Chen Junming and Zhao Ping.Impacts of Doppler Radar Data Assimilation on the Simulation of Severe Heavy Rainfall Events[J].Quarterly Journal of Applied Meteorology,2015,26(5):555-566. |
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| Authors: | Zhang Xinzhong Chen Junming and Zhao Ping |
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| Institution: | 1.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 1000812.Chinese Academy of Meteorological Sciences, Beijing 100081 |
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| Abstract: | The impact of Doppler weather radar (DWR) data on the simulation of a heavy rainfall event is examined. The quality control algorithm of DWR developed by Center for Analysis and Prediction of Storms is applied and the threshold for the raw S-band DWR radial velocity is decided. Several commonly seen non-meteorological returns can be removed effectively. The DWR reflectivity data are processed and the regional three-dimensional mosaic is generated using the CINRAD 3D Digital Mosaic System developed by State Key Laboratory of Severe Weather. Retrieval results match well with the observation. The Gridpoint Statistical Interpolation System (GSI) and the Weather Research and forecasting Model version 3.5.1 (WRF) are used to assimilate 46 S-band DWR data to simulate the severe heavy rain cases that occurred in Jun 2013. Numerical experiment results show that about 90% of the radial velocity data after quality control can be assimilated and generate reasonable analysis increments. Results also show that the assimilation of DWR data has a positive impact on the simulation of heavy rainfall. Assimilating radial velocity can enhance the information of mesoscale weather system in initial field and the simulated field, making the simulated wind fields and rainfall location more similar to the observation. Radar reflectivity data are used primarily in a cloud analysis that retrieves the amount of hydrometeors and adjusts in-cloud temperature and moisture. Assimilating radial velocity affects the zonal and vertical winds by adjusting the amount of hydrometers and moisture which have directly influence on generating precipitation. It changes the simulated rainfall intensity. Assimilating radial velocity and reflectivity at the same time can not only reflect the wind filed more reasonably, but also improve the simulation of rainfall intensity and area. In addition, improvements of the precipitation are most notable in the 12-36 h simulation when more effective radar data are available. Both ETS and HSS of experiment assimilating radar data are proved higher than CTRL experiment which only assimilates conventional data. |
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