Real-Time Background-Dependent Indirect Assimilation of Radar Reflectivity Factor and Experiments for Multi Heavy Rainfall Cases
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摘要: 为避免直接同化时反射率非线性观测算子线性化带来的线性近似误差问题，目前许多研究和业务中还常采用间接同化方式来同化雷达反射率因子，其通过背景场温度判定水凝物类型及比例。基于一种实时天气背景依赖的雷达反射率因子间接同化方案，进行了4次暴雨过程（2次强对流，2次锋面）的循环同化及预报试验。结果表明：对于强对流暴雨个例，相对于传统温度判定方案，天气背景依赖方案的温度预报误差更小、降水预报评分更高，而对于锋面过程区别不明显；进一步机理分析表明，对于强对流暴雨个例，由于背景依赖方案在同化反射率因子时引入了实时天气背景信息，使得分析场水凝物结构能够更好表征实际对流特征且与其它模式变量更为协调，进而改善了模式预报的热、动力及水汽条件，从而改善了降雨预报效果；而锋面暴雨由浅对流过程占主导，水凝物以低层的雨水为主导，冰相水凝物对于该过程的影响较小，由于两种方案反演的雨水结构和量级均相似，因此降雨预报差异较小。Abstract: In present operating systems, indirect assimilation is frequently used to assimilate the radar reflectivity factor to avoid the problems caused by the linearization of the observation operator. Based on a real-time background-dependent radar reflectivity factor indirect assimilation scheme, cycling assimilation and forecasting experiments of four heavy rainfall processes (two convective and two frontal) were carried out. The results show that compared with the traditional temperature-determination scheme, the background-dependent scheme has smaller temperature forecast errors and higher precipitation forecast scores for the severe convective rainfall cases, but the difference in frontal process is not obvious. Further analysis shows that for severe convective rainfall, the background-dependent scheme introduced real-time background information when assimilating the reflectivity factor, allowing the hydrometeor structure of the analysis field to characterize the actual convective characteristics better and be more coordinated with other model variables, thereby improving the thermal, dynamic, and humidity conditions of the model forecast, thus improving precipitation forecasting. For heavy frontal rainfall, the hydrometeor structural difference in the analysis field of the two schemes is not obvious; thus, the difference in precipitation forecast is small.
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Key words:
- Heavy rainfall /
- Radar data assimilation /
- Reflectivity factor /
- Indirect assimilation
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图 1 4次暴雨过程的6 h累积降水量（单位：mm）：（a） 2019年7月6日06～12时（协调世界时，下同）；（b）2019年7月12日18～00时；（c）2018年7月5日00～06时；（d）2018年7月26日09～15时
Figure 1. 6-h accumulative precipitation (units: mm) of four heavy rainfall processes (a) 0600–1200 UTC on July 6, 2019; (b) 1800–0000 UTC on July 12, 2019; (c) 0000–0600 UTC on July 5, 2018; (d) 0900–1500 UTC on July 26, 2018
图 2 研究区域及同化观测站点分布。全部同化观测站点分布（左），雷达观测站点分布（右）
Figure 2. Research area and distribution of assimilation observation stations. Distribution of all assimilation observation stations (left), distribution of radar observation stations (right)
图 3 循环同化预报流程。GTS代表常规观测，Radar代表雷达观测，包括雷达径向风和由雷达反射率反演的水凝物。GTS同化间隔为3小时，Radar同化间隔为15分钟，每隔1小时进行一次3小时预报
Figure 3. Cycling assimilation and forecast process. GTS represents conventional observations, and Radar stands for radar observations, which include radial velocity and hydrometeors retrieved from radar reflectivity. The GTS assimilation interval is 3 h, and the radar assimilation interval is 15 min, with a 3 h forecast carried out every hour
图 4 4个个例两组试验的逐小时平均FSS降水评分：（a，d，g，j）0～1 h预报；（b，e，h，k）1～2 h预报；（c，f，i，l）2～3 h预报
Figure 4. Averaged FSS (Fraction Skill Scores) of the hourly-accumulated precipitation forecasts for two experiments of four cases: (a, d, g, j) 0–1 h; (b, e, h, k) 1–2 h; (c, f, i, l) 2–3 h
图 5 4个个例两组试验3 h预报场的平均RMSE，评估变量分别为风速U、V（单位：m s−1），温度T（单位：°C）以及相对湿度（RH）。黑色线代表Exp-ZT试验，红色线代表Exp-BG试验，阴影为95%置信区间
Figure 5. Averaged root mean square error (RMSE) of 3-h forecast field for the two experiments of four cases. The evaluation variables are wind speed U, V (units: m s−1), temperature T (units: °C), and relative humidity (RH). The black line represents Exp-ZT, the red line represents the Exp-BG, and the shadow represents the 95% confidence interval
图 6 强对流暴雨个例（Case 1）的观测（左）与试验Exp-ZT（中）和Exp-BG（右）模拟的3 h逐小时累积降水量（阴影，单位：mm）对比（起报时间为2019年7月6日08时）：（a，b，c）0～1 h；（d，e，f）1～2 h；（g，h，j）2～3 h
Figure 6. Hourly precipitation (units: mm) of the observation (left), the Exp-ZT (middle), and the Exp-BG (right) for the convective rainfall case (Case 1) (forecast from 0800 UTC on July 6), 2019: (a, b, c) 0–1 h; (d, e, f) 1–2 h; (g, h, j) 2–3 h
图 7 锋面暴雨个例（Case 2）的观测（左）与试验Exp-ZT（中）和Exp-BG（右）模拟的3 h逐小时累积降水量（阴影，单位：mm）对比（起报时间为2019年7月12日20时）：（a，b，c）0～1 h；（d，e，f）1～2 h；（g，h，j）2～3 h
Figure 7. Hourly precipitation (units: mm) of the observation (left), the Exp-ZT (middle), and the Exp-BG (right) for the frontal process (Case 2) (forecast at 2000 UTC on July 12, 2019): (a, b, c) 0–1 h; (d, e, f) 1–2 h; (g, h, j) 2–3 h
图 8 2019年7月（a，b）6日08时沿 33°N～34°N和（c，d）12日20时沿 30°N～31°N平均水凝物混合比最大值反演场垂直剖面：（a，c）Exp-ZT试验；（b，d）Exp-BG试验。绿色等值线：Qrain（单位：g kg−1，等值线分别为0.1，0.5，1.0，2.0）；蓝色线等值线：Qsnow（单位：g kg−1，等值线分别为0.01，1.0，2.5，3.5）；阴影：Qgraup，黑色虚线：0°C等温线
Figure 8. Vertical cross sections of the maximum retrieved hydrometeor mixing ratio (units: g kg−1) averaged along 33°N–34°N at (a, b) 0800 UTC on July 6, 2019 and along 30°N–31°N at (c, d) 0000 UTC on July 12, 2019: (a, c) Exp-ZT; (b, d) Exp-BG. The contour values of Qrain (green lines) are 0.1, 0.5, 1.0 and 2.0. The contour values of Qsnow (blue lines) are 0.01, 1.0, 2.5, and 3.5. The shade is Qgraup, and the dotted black line represents the 0°C line
图 9 2019年7月6日08时（上）和12日20时（下）水凝物（Qsnow、Qgraup）混合比分析增量（阴影，单位：g kg−1）：（a，c，e，g）Exp-ZT试验；（b，d，f，h）Exp-BG试验
Figure 9. Hydrometeor analysis increment mixing ratio (shaded, units: g kg−1) at 0800 UTC on July 6, 2019 (top) and at 2000 UTC on July 12, 2019 (bottom): (a, c, e, g) Exp-ZT; (b, d, f, h) Exp-BG
图 10 2019年7月（a，b）6日09时（08时起报）沿着AB（见图6）和（c，d）12日23时（20时起报）沿着CD（见图7）的相当位温预报场垂直剖面（阴影，单位：K）：（a，c）试验Exp-ZT；（b，d）试验Exp-BG
Figure 10. Vertical cross sections of equivalent potential temperature forecast field (shaded, units: K) along AB (in Fig.6) (a, b) at 0900 UTC (forecast from 0800 UTC) on July 6, 2019 and along CD (in Fig.7) (c, d) at 2300 UTC (forecast from 2000 UTC) on July 12, 2019: (a, c) Exp-ZT; (b, d) Exp-BG
图 11 2019年7月（a，b）6日09时（08时起报）沿着AB和（c，d）12日23时（20时起报）沿着CD的相对湿度（阴影）及风场的预报场（矢量，单位：m s−1）：（a，c）试验Exp-ZT；（b，d）试验Exp-BG
Figure 11. Relative humidity (shaded) and wind (vector, units: m s−1) forecast fields along AB (a, b) at 0900 UTC (forecast from 0800 UTC) on July 6, 2019 and along CD (c, d) at 2300 UTC (forecast from 2000 UTC) on July 12, 2019: (a, c) Exp-ZT; (b, d) Exp-BG
图 12 2019年7月（a，b）6日09时（08时起报）沿着AB和（c，d）12日23时（20时起报）沿着CD的散度（填色，单位：10−4s−1）及风场的预报场（矢量，单位：m s−1）：（a，c）试验Exp-ZT；（b，d） 试验Exp-BG
Figure 12. Divergence (shaded, units: 10−4s−1) and wind (vector, units: m s−1) forecast fields along AB (a, b) at 0900 UTC (forecast from 0800 UTC) on July 6, 2019 and along CD (c, d) at 2300 UTC (forecast from 2000 UTC) on July 12, 2019: (a, c) Exp-ZT; (b, d) Exp-BG
表 1 研究选取的四次暴雨过程
Table 1. Four heavy rainfall cases selected in the study
个例名称 生命史 个例类型 Case 1 2019年7月6日06～12时 局地强对流 Case 2 2019年7月12日18时至13日03时 锋面降水 Case 3 2018年7月5日00～06时 锋面降水 Case 4 2018年7月26日09～15时 多孤立单体强对流 
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表 2 试验设计
Table 2. Experimental design
试验名称 同化资料 反射率因子同化方案 Exp-ZT 常规观测、径向风和反射率 温度判定方案 Exp-BG 常规观测、径向风和反射率 背景依赖方案
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