Application of PFI-4DVar Data Assimilation Technique to Nowcasting of Numerical Model
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摘要: 运用WRF模式(Weather Research and Forecasting Model,天气研究和预报模式)和WRFDA同化(WRF Data Assimilation,WRF资料同化)系统,探究采用物理滤波初始化四维变分同化方法提高数值预报在临近预报时效的预报能力的可能性。通过采用12 min同化窗,在不显著增加计算量的情况下,得到更协调的模式初始场,从而提高模式预报能力。选取2018年8月华北地区17个降水个例进行研究,结果表明:采用物理滤波初始化四维变分同化技术能够明显改进模式短时临近降水预报能力,明显提高对大量级降水预报的ETS评分,6 h累积降水大于25.0 mm量级的ETS评分由0.125提高到0.190,且6 h累积降水大于60.0 mm量级的ETS评分由0.016提高到0.081。研究还表明:同化雷达风场通过改进初始动力场使次网格尺度降水过程(积云参数化)快速响应,可提高短时临近时段的降水预报能力。Abstract: Nowcasting is mainly based on radar echo or satellite image extrapolation method. However, the prediction ability of extrapolation method decreases with time, because this method cannot describe the physical mechanism during the occurrence, development and extinction of severe convective weather systems. Considering that the prediction ability of numerical model improves with time, the nowcasting system should be based on numerical forecast model. And appropriate data assimilation technology can be used to produce a more accurate initial field, making the integral forecast results closer to the reality. The PFI-4DVar assimilation method (four-dimensional variational technology under physical filter initialization) can filter in the process of assimilation rather than model integration, thus shortening the model spin-up time and getting a more dynamic and physically coordinated analysis field. Therefore, PFI-4DVar assimilation method not only improves model prediction results, but also makes initial field closer to observations, which is very suitable for nowcasting.Using WRF model and WRFDA assimilation system, effects of PFI-4DVar on prediction ability of numerical nowcasting are explored. Through the precipitation case in North China on 11 August 2018, prediction results in control and assimilation tests are discussed. According to ETS scores, the precipitation prediction of assimilation test is closer to the observation compared with control test. The water vapor in assimilated ground and sounding data, the dynamic field in assimilated radar radial wind data and the appropriate cumulus parameterization scheme make the amplitude of divergence in high-level and convergence in low-level in analysis field of assimilation test much stronger than those in background field, thus creating vertical motion. Moreover, the precipitation of assimilation test is mainly caused by process of cumulus.A batch test is carried out on 17 precipitation cases of North China in August 2018. It shows that PFI-4DVar can significantly improve the prediction ability for short precipitation (especially large order precipitation) and timely predict the fall area of heavy rain or rainstorm. After assimilation, ETS scores of 6-hour accumulated precipitation (greater than 25.0 mm) in batch test increase from 0.125 to 0.190, and ETS scores of 6-hour accumulated precipitation (greater than 60.0 mm) increase from 0.016 to 0.081. PFI-4DVar significantly improves the precipitation nowcasting. Calculations are reduced by selecting 12-minute assimilation time window, which greatly saves computational resources. And the time of assimilation test is shortened, ensuring the time efficiency of 6-hour forecast. Therefore, PFI-4DVar can improve and enhance the prediction ability of precipitation nowcasting.
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图 7 2018年8月17个降水个例批量试验观测与未同化(CTL)、同化(PFI)试验的第1~3小时的逐小时平均累积降水中的积云对流降水(RAINC)和格点可分辨降水(RAINNC)分布
Fig. 7 Averaged convective parameterization(RAINC) and averaged grid-resolvable(RAINNC) precipitation accumulated within one-hour from the 1st to the 3rd hour in observation, CTL(without data assimilation), and PFI experiment(with data assimilation) of seventeen cases in Aug 2018
表 1 2018年8月华北地区17个降水个例
Table 1 Seventeen selected precipitation cases in North China in Aug 2018
日期 起始时刻 6 h最大降水量/mm 有降水产生的站点总数 6 h累积降水量不大于60 mm的站点数 2018-08-03 00:00 116.6 3536 67 2018-08-03 12:00 131.3 3837 37 2018-08-07 00:00 66.4 3642 2 2018-08-08 00:00 137.0 3825 65 2018-08-09 00:00 61.5 2479 1 2018-08-11 12:00 137.3 3522 71 2018-08-12 12:00 166.8 6616 112 2018-08-13 00:00 115.1 7608 22 2018-08-13 12:00 191.5 6624 268 2018-08-14 00:00 172.0 4614 194 2018-08-14 12:00 255.7 2572 67 2018-08-16 00:00 97.0 5095 12 2018-08-17 12:00 231.3 7707 219 2018-08-18 00:00 258.0 7528 315 2018-08-19 00:00 283.8 6946 174 2018-08-19 12:00 161.0 6097 177 2018-08-30 00:00 108.5 4473 14 表 2 2018年8月17个降水个例批量试验的逐小时累积降水ETS评分
Table 2 ETS scores of one-hour accumulated precipitation forecasts of seventeen cases in Aug 2018
预报时间 试验 ETS评分 0.1 mm 1.5 mm 7.0 mm 13.0 mm 40.0 mm 第1小时 CTL 0.204 0.087 0.008 0 0 3DV 0.203 0.081 0.007 0 0 PFI 0.183 0.136 0.073 0.027 0 第2小时 CTL 0.240 0.215 0.085 0.007 0 3DV 0.242 0.201 0.082 0.009 0 PFI 0.210 0.211 0.133 0.076 0.010 第3小时 CTL 0.216 0.195 0.056 0.004 0 3DV 0.221 0.188 0.069 0.009 0 PFI 0.206 0.215 0.131 0.039 0.010 表 3 2018年8月17个降水个例批量试验的6 h累积降水ETS评分
Table 3 ETS scores of six-hour accumulated precipitation forecasts of seventeen cases in Aug 2018
试验 ETS评分 0.1 mm 4.0 mm 13.0 mm 25.0 mm 60.0 mm CTL 0.202 0.245 0.187 0.125 0.016 3DV 0.202 0.222 0.163 0.108 0.017 PFI 0.181 0.241 0.210 0.190 0.081 表 4 2018年8月11日个例1~3 h逐小时累积降水预报ETS评分
Table 4 ETS scores of one-hour accumulated precipitation forecasts from the 1st to the 3rd hour on 11 Aug 2018
预报时间 ETS评分 0.1 mm 1.5 mm 7.0 mm CTL PFI CTL PFI CTL PFI 第1小时 -0.025 0.049 -0.004 0.008 0.0 0.0 第2小时 -0.024 0.024 -0.038 0.015 -0.015 0.014 第3小时 0.002 0.089 -0.024 0.053 -0.014 0.042 -
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