| 物理滤波初始化四维变分在临近预报中的应用 |
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| 引用本文： | 姜文静,梁旭东.物理滤波初始化四维变分在临近预报中的应用[J].应用气象学报,2020,31(5):543-555. |
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| 作者姓名： | 姜文静 梁旭东 |
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| 作者单位： | 中国气象科学研究院灾害天气国家重点实验室, 北京 100081 |
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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。
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| 关 键 词： | 资料同化 临近预报 四维变分 降水预报 |
| 收稿时间： | 2020-06-16 |
Application of PFI-4DVar Data Assimilation Technique to Nowcasting of Numerical Model |
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| Institution: | State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081 |
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| 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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