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Impacts of Doppler Radar Data Assimilation on the Simulation of Severe Heavy Rainfall Events
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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。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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图 1 模式区域 (方框内) 和雷达站点分布
Fig. 1 Mode domain (square frame) and the distribution of radar sites
图 2 2013年6月两次强降水过程累积降水量分布
(a)6月5日00：00—8日00：00，(b)6月26日00：00—28日12：00
Fig. 2 The observed accumulated precipitation from 0000 UTC 5 Jun to 0000 UTC 8 Jun in 2013(a) and from 0000 UTC 26 Jun to 1200 UTC 28 Jun in 2013(b)
图 3 2013年6月26日00:00各物理量场和同化雷达资料后的增量场
(a) 同化前700 hPa流场，(b) 同化后700 hPa流场，(c)500 hPa温度场 (等值线，单位：K) 和增量场 (填色)，(d)850 hPa水汽混合比 (等值线，单位：g/kg) 和增量场 (填色)
Fig. 3 Variables of background and incremental ground at 0000 UTC 26 Jun in 2013
(a)700 hPa streamlines before assimilating radar data, (b)700 hPa streamlines after assimilating radar data, (c)500 hPa temperature (the contour, unit:K) with the increment (the shaded), (d)850 hPa water vapor mixing ratio (the contour, unit:g/kg) with the increment (the shaded)
图 4 h累积降水量模拟 (等值线) 和实况 (阴影)(单位：mm)
(a) 2013年6月6日00：00—7日00：00，(b) 2013年6月7日00：00—8日00：00, (c)2013年6月26日00：00—27日00：00，(d)2013年6月27日00：00—28日00：00
Fig. 4 24 h simulated (the contour) and obseverd (the shaded) accumlated precipitation (unit: mm)
(a) from 0000 UTC 6 Jun to 0000 UTC 7 Jun in 2013, (b) from 0000 UTC 7 Jun to 0000 UTC 8 Jun in 2013, (c) from 0000 UTC 26 Jun to 0000 UTC 27 Jun in 2013, (d) from 0000 UTC 27 Jun to 0000 UTC 28 Jun in 2013
图 5 降水过程1 24 h降水模拟评分
(a) ETS评分, (b) HSS评分, (c) BS评分
Fig. 5 The verification of 24 h simulated precipitation for case 1
(a) ETS, (b) HSS, (c) BS
图 6 24 h累积降水模拟 (等值线) 和实况 (阴影) 分布 (单位：mm)
Fig. 6 24 h simulated (the contour) and obseverd (the shaded) accumlated precipitation (unit: mm)
图 7 降水过程2 24 h累积降水量模拟评分
(a) ETS评分，(b) HSS评分
Fig. 7 The verification of 24 h simulated precipitation for case 2
(a) ETS, (b) HSS
图 8 3 h累积降水量实况和模拟以及RADVAR-V试验与CTRL试验的差值
(a) 实况，(b) 模拟，(c)850 hPa风场 (矢量，单位：m/s) 和散度 (填色) 差值，(d) 降水量差值
Fig. 8 3 h obseverd and simulated accumlated precipitation and the difference between RADVAR-V and CTRL
(a) observation, (b) simulated precipitation, (c) the difference of 850 hPa wind (the vector, unit:m/s) and divergence (the shaded), (d) the difference of precipitation
图 9 RADVAR-R试验和CTRL试验差值图
(a)850 hPa风场 (矢量线，单位：m/s) 和散度 (填色)，(b) 降水量，(c) 垂直速度 (单位：10-1m/s)，(d) 雨水含量 (单位：g/kg)
Fig. 9 The difference between RADVAR-R and CTRL
(a)850 hPa wind (the vector, unit:m/s) and divergence (the shaded), (b) precipitation, (c)w(unit:10-1m/s), (d) rain water mixing ratio (unit:g/kg)
图 10 RADVAR-VR试验和CTRL试验的差值
(a)850 hPa风场 (矢量线，单位：m/s) 和散度 (填色)，(b) 垂直速度 (单位：10-1m/s)，(c) 雨水含量 (单位：g/kg)，(d) 降水量 (填色) 和850 hPa垂直速度 (等值线，单位：10-1m/s)
Fig. 10 The difference between RADVAR-VR and CTRL
(a)850 hPa wind (the vector, unit:m/s) and divergence (the shaded), (b)w(unit: 10-1m/s), (c) rain water mixing ratio (unit: g/kg), (d) precipitation (the shaded) and 850 hPa w(the contour, unit:10-1m/s)
表 1 试验方案
Table 1 The configuration of experiments
试验名称 控制试验 (CTRL) 敏感性试验 RADAR-V RADAR-R RADAR-VR 常规资料 有 有 有 有 雷达径向风 无 有 无 有 雷达反射率因子 无 无 有 有 
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