Precipitation Extrapolation Nowcasting in Beijing-Tianjin-Hebei Under Different Weather Backgrounds

Wang Yuhong; Bica Benedikt

doi:10.11898/1001-7313.20220302

Vol.33, NO.3, 2022

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Article Navigation > Journal of Applied Meteorological Science > 2022 > 33(3): 270-281

Wang Yuhong, Bica Benedikt. Precipitation extrapolation nowcasting in Beijing-Tianjin-Hebei under different weather backgrounds. J Appl Meteor Sci, 2022, 33(3): 270-281. DOI: 10.11898/1001-7313.20220302.

Citation:

Wang Yuhong, Bica Benedikt. Precipitation extrapolation nowcasting in Beijing-Tianjin-Hebei under different weather backgrounds. J Appl Meteor Sci, 2022, 33(3): 270-281. DOI: 10.11898/1001-7313.20220302.

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Precipitation Extrapolation Nowcasting in Beijing-Tianjin-Hebei Under Different Weather Backgrounds

DOI: 10.11898/1001-7313.20220302

Wang Yuhong^{1
,
,},
Bica Benedikt²

1.
Hebei Meteorological Observatory, Shijiazhuang 050021
2.
Central Institute for Meteorology and Geodynamics, Vienna 1190

Received Date: 2021-12-01
Rev Recd Date: 2022-03-18
Publish Date: 2022-05-31

Abstract

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.
- nowcasting;
- cross correlation;
- optical flow;
- RMAPS_IN

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References(27)

Figures and Tables

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)

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Fig. 2 Euclidean distance and Cosine distance of two motion vectors derived by cross correlation and optical flow under different weather backgrounds

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Fig. 3 TS(a), FAR(b), MR(c) for low trough cold front precipitation

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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

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Fig. 5 Euclidean distance(a) and Cosine distance(b) of two motion vectors derived by cross correlation and optical flow on 9 Aug 2020

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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

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Fig. 7 Euclidean distance(a) and Cosine distance(b) of two motion vectors derived by cross correlation and optical flow on 11 Aug 2019

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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， 2020-07-17，2020-07-30，2020-08-18，2020-08-23	2108
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， 2020-08-12，2020-09-14	1448
台风类	2019-07-28，2019-08-01，2019-08-09，2019-08-10， 2019-08-11，2019-08-15，2020-08-05	1058
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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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
预报时效/min	降水类型	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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