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Precipitation Extrapolation Nowcasting in Beijing-Tianjin-Hebei Under Different Weather Backgrounds
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摘要: 基于2019—2020年京津冀地区不同天气系统影响下的降水过程，采用交叉相关法和光流法对快速更新多尺度分析和预报综合集成系统（Rapid-refresh Multi-Scale Analysis and Prediction System-Integration，RMAPS_IN）的降水分析产品进行0~2 h临近外推预报的批量试验。结果表明：由交叉相关法和光流法计算的两种外推矢量在大小和方向上存在一定差异，直接差异与影响降水的天气系统位置有明显的对应关系，而方向差异受地理位置的影响更明显，台风类降水呈弧形带状分布，低槽冷锋类、低涡类、气旋类、暖切变线类等几类降水均呈西北大东南小的特点；预报效果方面，总体上交叉相关法优于光流法，尤其是预报时效超过30 min以后，各种降水类型的批量检验结果显示交叉相关法的预报评分优于光流法，且预报时效越长、优势越明显，但预报时效为10 min时，光流法在低涡类、台风类、暖切变线类的空报率上优于交叉相关法。此外，基于外推的临近预报方法对京津冀地区台风类降水的预报效果最好，其次为暖切变线类、低涡类、低槽冷锋类、气旋类。Abstract:
Rapid-refresh Multi-Scale Analysis and Prediction System-Integration (RMAPS_IN) is an important tool for Beijing, Hebei and other meteorological departments to make rapid-updated and refined precipitation nowcasting. The precipitation analysis products of the system are based on automatic station observation and radar quantitative precipitation estimation data, while 0-2 h forecast products are obtained by extrapolation based on the analysis products. To study the applicability of different extrapolation methods in RMAPS_IN, the precipitation events of different weather systems from 2019 to 2020 are analyzed, using cross correlation method and optical flow method to conduct a 0-2 h extrapolation nowcasting test based on the RMAPS_IN precipitation analysis products. The cross correlation method uses classic optimal correlation coefficient calculation scheme, while the optical flow method employs the Farneback dense optical flow calculation scheme in the OpenCV function library. According to the characteristics of the regional weather systems, the precipitation events are divided into five types: Low trough cold front precipitation, low vortex precipitation, typhoon precipitation, cyclone precipitation, and warm shear line precipitation. The sample size of each precipitation type is 2108, 1448, 1058, 260, and 140, respectively. The batch test results show that the extrapolated vectors by the cross correlation method and optical flow method have a certain difference in magnitude and direction. The direct difference has a clear correspondence with the position of the weather system that affects precipitation, and is more obviously affected by the geographical location. For typhoon precipitation, the difference in direction is distributed in an arc band, while for other 4 types of precipitation, the difference is large in the northwest and small in the southeast. In terms of forecasting effect, the cross correlation method is generally better than the optical flow method, especially when the forecast time exceeds 30 minutes, and the longer the lead time is, the more obvious the advantage is. But when the forecast time is 10 min, the optical flow method is better in the false alarm rate of low vortex precipitation, typhoon precipitation and warm shear line precipitation. In addition, the nowcasting method based on extrapolation has the best prediction effects on typhoon precipitation in Beijing-Tianjin-Hebei region, followed by warm shear line precipitation, low vortex precipitation, low trough cold front precipitation, and cyclone precipitation. It should be noted that in Beijing-Tianjin-Hebei region, cyclone precipitation and warm shear line precipitation rarely occurred in recent years, and the sample size of these two types of precipitation is significantly smaller than that of other types, so the relevant results are less representative.
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Key words:
- nowcasting;
- cross correlation;
- optical flow;
- RMAPS_IN
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图 1 站点分布
(黑色圆点为RMAPS_IN分析场所用站点，红色三角为检验预报效果所用站点，蓝色方框和箭头为计算移动矢量的矩形设计)
Fig. 1 Distribution of stations
(black dots are stations used for RMAPS_IN analyses, red triangles are stations for verification, blue boxes and arrow are the rectangular design when calculating motion vectors)
图 2 不同天气背景下两种外推矢量对比
Fig. 2 Euclidean distance and Cosine distance of two motion vectors derived by cross correlation and optical flow under different weather backgrounds
图 3 低槽冷锋类降水的TS评分(a)、空报率(b)、漏报率(c)
Fig. 3 TS(a), FAR(b), MR(c) for low trough cold front precipitation
图 4 2020年8月9日23：00低涡降水过程(填色为降水强度)
(a)起报时刻23：00的降水分析产品与移动矢量(红色箭头为交叉相关法移动矢量，黑色箭头为光流法移动矢量)，(b)8月10日00：00的降水分析产品，(c)交叉相关法外推未来60 min的预报，(d)光流法外推未来60 min的预报
Fig. 4 Low vortex precipitation case at 2300 BT 9 Aug 2020(the shaded denotes precipitation intensity)
(a)precipitation analysis and motion field based at 2300 BT 9 Aug 2020(the red vector denotes motion field derived by cross correlation, the black vector denotes motion field derived by optical flow), (b)precipitation analysis at 0000 BT 10 Aug, (c)forecast of cross correlation at 60 min from the base time, (d)forecast of optical flow at 60 min from the base time
图 5 2020年8月9日降水过程两种外推矢量的对比(a)欧氏距离，(b)余弦距离
Fig. 5 Euclidean distance(a) and Cosine distance(b) of two motion vectors derived by cross correlation and optical flow on 9 Aug 2020
图 6 2019年8月11日台风降水过程(填色为降水强度)
(a)起报时刻12：00的降水分析产品与移动矢量(红色箭头为交叉相关法移动矢量，黑色箭头为光流法移动矢量)，(b)13：00的降水分析产品，(c)交叉相关法外推未来60 min的预报，(d)光流法外推未来60 min的预报
Fig. 6 Typhoon precipitation case on 11 Aug 2019(the shaded denotes precipitation intensity)
(a)precipitation analysis and motion field based at 1200 BT(the red vector denotes motion field derived by cross correlation, the black vector denotes motion field derived by optical flow), (b)precipitation analysis at 1300 BT 11 Aug, (c)forecast of cross correlation at 60 min from the base time, (d)forecast of optical flow at 60 min from the base time
图 7 2019年8月11日降水过程两种外推矢量的对比(a)欧氏距离，(b)余弦距离
Fig. 7 Euclidean distance(a) and Cosine distance(b) of two motion vectors derived by cross correlation and optical flow on 11 Aug 2019
表 1 不同天气背景下降水过程的日期和样本量
Table 1 Dates and sample number of precipitation events under different weather backgrounds
降水类型 日期 样本量 低槽冷锋类 2019-05-17，2019-05-18，2019-07-05，2019-07-22，
2019-07-29，2019-08-04，2019-09-09，2019-10-03，
2020-05-30，2020-06-24，2020-07-04, 2020-07-05，
2020-07-17，2020-07-30，2020-08-18，2020-08-232108 低涡类 2019-07-06，2020-05-07，2020-05-21，2020-06-25，
2020-06-28，2020-06-29，2020-07-01，2020-07-08，
2020-07-26，2020-07-28，2020-08-01，2020-08-09，
2020-08-12，2020-09-141448 台风类 2019-07-28，2019-08-01，2019-08-09，2019-08-10，
2019-08-11，2019-08-15，2020-08-051058 气旋类 2019-05-25，2020-07-12 260 暖切变线类 2020-08-15，2020-08-16 140 
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表 2 不同天气背景下交叉相关法和光流法的预报评分一览表
Table 2 Scores of nowcastings with different lead-times forecasted by cross correlation and optical flow under different weather backgrounds
预报时效/min 降水类型 TS评分 空报率 漏报率 交叉相关法 光流法 交叉相关法 光流法 交叉相关法 光流法 10 低槽冷锋类 0.41* 0.41* 0.28* 0.28* 0.51* 0.51* 低涡类 0.49 0.48 0.27** 0.26** 0.42 0.43 台风类 0.53 0.52 0.28** 0.27** 0.34 0.35 气旋类 0.41* 0.41* 0.28* 0.28* 0.51* 0.51* 暖切变线类 0.51 0.50 0.28** 0.27** 0.36 0.38 30 低槽冷锋类 0.36 0.35 0.35 0.36 0.55 0.57 低涡类 0.44 0.42 0.32 0.33 0.46 0.49 台风类 0.47 0.45 0.34 0.35 0.38 0.41 气旋类 0.35 0.34 0.34 0.36 0.57 0.58 暖切变线类 0.46 0.44 0.35* 0.35* 0.41 0.43 60 低槽冷锋类 0.31 0.29 0.42 0.45 0.61 0.64 低涡类 0.38 0.35 0.40 0.42 0.52 0.56 台风类 0.39 0.35 0.43 0.45 0.45 0.51 气旋类 0.29 0.27 0.41 0.44 0.63 0.66 暖切变线类 0.39 0.37 0.42 0.43 0.46 0.49 90 低槽冷锋类 0.27 0.24 0.48 0.51 0.66 0.69 低涡类 0.33 0.30 0.45 0.48 0.56 0.62 台风类 0.33 0.30 0.50 0.52 0.51 0.57 气旋类 0.24 0.22 0.48 0.50 0.69 0.71 暖切变线类 0.35 0.31 0.47 0.50 0.49 0.54 120 低槽冷锋类 0.23 0.21 0.53 0.56 0.70 0.73 低涡类 0.30 0.26 0.50 0.53 0.61 0.66 台风类 0.29 0.25 0.55 0.57 0.55 0.63 气旋类 0.20 0.19 0.54 0.56 0.74 0.75 暖切变线类 0.32 0.28 0.52 0.56 0.52 0.58 注：*表示两种方法评分一致，**表示光流法评分更优，无*号表示交叉相关法评分更优。
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[1] 陈明轩, 俞小鼎, 谭晓光, 等. 对流天气临近预报技术的发展与研究进展. 应用气象学报, 2004, 15(6): 754-766. doi: 10.3969/j.issn.1001-7313.2004.06.015Chen M X, Yu X D, Tan X G, et al. A brief review on the development of nowcasting for convective storms. J Appl Meteor Sci, 2004, 15(6): 754-766. doi: 10.3969/j.issn.1001-7313.2004.06.015 [2] 郑永光, 张小玲, 周庆亮, 等. 强对流天气短时临近预报业务技术进展与挑战. 气象, 2010, 36(7): 33-42. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201007009.htmZheng Y G, Zhang X L, Zhou Q L, et al. Review on severe convective weather short-term forecasting and nowcasting. Meteor Mon, 2010, 36(7): 33-42. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201007009.htm [3] 柳士俊, 张蕾. 光流法及其在气象领域里的应用. 气象科技进展, 2015, 5(4): 16-21. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201504005.htmLiu S J, Zhang L. Optical flow method and its application in the field of meteorology. Adv Meteor Sci Tech, 2015, 5(4): 16-21. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201504005.htm [4] 韩丰, 龙明盛, 李月安, 等. 循环神经网络在雷达临近预报中的应用. 应用气象学报, 2019, 30(1): 61-69. doi: 10.11898/1001-7313.20190106Han F, Long M S, Li Y A, et al. The application of recurrent neural network to nowcasting. J Appl Meteor Sci, 2019, 30(1): 61-69. doi: 10.11898/1001-7313.20190106 [5] 孙健, 曹卓, 李恒, 等. 人工智能技术在数值天气预报中的应用. 应用气象学报, 2021, 32(1): 1-11. doi: 10.11898/1001-7313.20210101Sun J, Cao Z, Li H, et al. Application of artificial intelligence technology to numerical weather prediction. J Appl Meteor Sci, 2021, 32(1): 1-11. doi: 10.11898/1001-7313.20210101 [6] 金子琪, 王新敏, 鲍艳松, 等. 基于卷积神经网络的飑线识别算法. 应用气象学报, 2021, 32(5): 580-591. doi: 10.11898/1001-7313.20210506Jin Z Q, Wang X M, Bao Y S, et al. Squall line identification method based on convolution neural network. J Appl Meteor Sci, 2021, 32(5): 580-591. doi: 10.11898/1001-7313.20210506 [7] 韩丰, 杨璐, 周楚炫, 等. 基于探空数据集成学习的短时强降水预报试验. 应用气象学报, 2021, 32(2): 188-199. doi: 10.11898/1001-7313.20210205Han F, Yang L, Zhou C X, et al. An experimental study of the short-time heavy rainfall event forecast based on ensemble learning and sounding data. J Appl Meteor Sci, 2021, 32(2): 188-199. doi: 10.11898/1001-7313.20210205 [8] Haiden T, Kann A, Wittmann C, et al. The integrated nowcasting through comprehensive analysis(INCA) system and its validation over the Eastern Alpine region. Wea Forecasting, 2011, 26(2): 166-183. doi: 10.1175/2010WAF2222451.1 [9] Haiden T, Kann A, Pistotnik G. Nowcasting with INCA during SNOW-V10. Pure Appl Geophys, 2014, 171: 231-242. doi: 10.1007/s00024-012-0547-8 [10] 程丛兰, 陈敏, 陈明轩, 等. 临近预报的两种高时空分辨率定量降水预报融合算法的对比试验. 气象学报, 2019, 77(4): 701-714. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201904008.htmCheng C L, Chen M, Chen M X, et al. Comparative experiments on two high spatiotemporal resolution blending algorithm for quantitative precipitation nowcasting. Acta Meteor Sinica, 2019, 77(4): 701-714. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201904008.htm [11] Woo W, Wong W. Operational application of optical flow techniques to radar-based rainfall nowcasting. Atmosphere, 2017, 8: 48. doi: 10.3390/atmos8030048 [12] Liu Y, Xi D, Li Z, et al. A new methodology for pixel-quantitative precipitation nowcasting using a pyramid Lucas Kanade optical flow approach. J Hydrol, 2015, 529: 354-364. doi: 10.1016/j.jhydrol.2015.07.042 [13] Chen Y Z, Lan X P, Chen X L, et al. A nowcasting technique based on application of the particle filter blending algorithm. J Meteor Res, 2017, 31(5): 931-945. doi: 10.1007/s13351-017-6557-9 [14] 韩雷, 王洪庆, 林隐静. 光流法在强对流天气临近预报中的应用. 北京大学学报(自然科学版), 2018, 44(5): 751-755. https://www.cnki.com.cn/Article/CJFDTOTAL-BJDZ200805019.htmHan L, Wang H Q, Lin Y J. Application of optical flow method to nowcasting convective weather. Acta Scientiarum Naturalium Universitatis Pekinensis, 2018, 44(5): 751-755. https://www.cnki.com.cn/Article/CJFDTOTAL-BJDZ200805019.htm [15] 张蕾, 魏鸣, 李南, 等. 改进的光流法在回波外推预报中的应用. 科学技术与工程, 2014, 14(32): 133-137. https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS201432029.htmZhang L, Wei M, Li N, et al. Improved optical flow method application to extrapolate radar echo. Sci Tech Engrg, 2014, 14(32): 133-137. https://www.cnki.com.cn/Article/CJFDTOTAL-KXJS201432029.htm [16] 曹春燕, 陈元昭, 刘东华, 等. 光流法及其在临近预报中的应用. 气象学报, 2015, 73(3): 471-480. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201503005.htmCao C Y, Chen Y Z, Liu D H, et al. The optical flow method and its application to nowcasting. Acta Meteor Sinica, 2015, 73(3): 471-480. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201503005.htm [17] Horn B K P, Schunck B G. Determining optical flow. Artif Intell, 1981, 17: 185-204. doi: 10.1016/0004-3702(81)90024-2 [18] Lucas B D, Kanade T. An Iterative Image Registration Technique with an Application to Stereo Vision//Proc 7th Int Joint Conf on Artif Intell, 1981: 674-679. [19] Farneback G. Very High Accuracy Velocity Estimation Using Orientation Tensors, Parametric Motion, and Simultaneous Segmentation of the Motion Field//Proc 8th IEEE Int Conf on IEEE, 2002: 171-177. [20] 安晶晶, 刘高平, 朱佳宁. Farneback光流法在短临预报中的应用. 软件, 2018, 39(10): 18-25. https://www.cnki.com.cn/Article/CJFDTOTAL-RJZZ201810006.htmAn J J, Liu G P, Zhu J N. Application of Farneback optical flow method in nowcasting. Comput Engineer & Soft, 2018, 39(10): 18-25. https://www.cnki.com.cn/Article/CJFDTOTAL-RJZZ201810006.htm [21] 曹伟华, 陈明轩, 高峰, 等. 雷暴区域追踪矢量与雷暴单体追踪矢量融合临近预报研究. 气象学报, 2019, 77(6): 1015-1027. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201906004.htmCao W H, Chen M X, Gao F, et al. A vector blending study based on object-based tracking vectors and cross correlation tracking vectors. Acta Meteor Sinica, 2019, 77(6): 1015-1027. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201906004.htm [22] 宋林烨, 陈明轩, 程丛兰, 等. 京津冀夏季雷达定量降水估测的误差统计及定量气候校准. 气象学报, 2019, 77(3): 497-515. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201903009.htmSong L Y, Chen M X, Cheng C L, et al. Characteristics of summer QPE error and a climatological correction method over Beijing-Tianjin-Hebei region. Acta Meteor Sinica, 2019, 77(3): 497-515. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201903009.htm [23] 冯蕾, 王晓峰, 何晓凤, 等. 基于INCA和METRo的江苏省路面高温精细化预报. 应用气象学报, 2017, 28(1): 109-118. doi: 10.11898/1001-7313.20170110Feng L, Wang X F, He X F, et al. Fine forecast of high road temperature along Jiangsu highways based on INCA system and METRo Model. J Appl Meteor Sci, 2017, 28(1): 109-118. doi: 10.11898/1001-7313.20170110 [24] 韦青, 李伟, 彭颂, 等. 国家级天气预报检验分析系统建设与应用. 应用气象学报, 2019, 30(2): 245-256. doi: 10.11898/1001-7313.20190211Wei Q, Li W, Peng S, et al. Development and application of national verification system in CMA. J Appl Meteor Sci, 2019, 30(2): 245-256. doi: 10.11898/1001-7313.20190211 [25] 宋善允, 彭军, 连志鸾, 等. 河北省天气预报手册. 北京: 气象出版社, 2017: 65-73.Song S Y, Peng J, Lian Z L, et al. Weather Forecast Manual of Hebei. Beijing: China Meteorological Press, 2017: 65-73. [26] 刘涛, 端义宏, 冯佳宁, 等. 台风利奇马(1909)双眼墙特征及长时间维持机制. 应用气象学报, 2021, 32(3): 289-301. doi: 10.11898/1001-7313.20210303Liu T, Duan Y H, Feng J N, et al. Characteristics and mechanisms of long-lived concentric eyewalls in Typhoon Lekima in 2019. J Appl Meteor Sci, 2021, 32(3): 289-301. doi: 10.11898/1001-7313.20210303 [27] 郑倩, 毛程燕, 丁丽华, 等. 台风利奇马(1909)与台风摩羯(1814)云特征对比. 应用气象学报, 2022, 33(1): 43-55. doi: 10.11898/1001-7313.20220104Zheng Q, Mao C Y, Ding L H, et al. Comparison of cloud characteristics between Typhoon Lekima(1909) and Typhoon Yagi(1814). J Appl Meteor Sci, 2022, 33(1): 43-55. doi: 10.11898/1001-7313.20220104 -
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