A Probabilistic Forecast Experiment of Short-duration Heavy Rainfall in Beijing Based on CMA-BJ

Xing Nan; Zhong Jiqin; Lei Lei; Yang Yiya; Xu Luyang

doi:10.11898/1001-7313.20230601

Vol.34, NO.6, 2023

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Article Navigation > Journal of Applied Meteorological Science > 2023 > 34(6): 641-654

Xing Nan, Zhong Jiqin, Lei Lei, et al. A probabilistic forecast experiment of short-duration heavy rainfall in Beijing based on CMA-BJ. J Appl Meteor Sci, 2023, 34(6): 641-654. DOI: 10.11898/1001-7313.20230601.

Citation:

Xing Nan, Zhong Jiqin, Lei Lei, et al. A probabilistic forecast experiment of short-duration heavy rainfall in Beijing based on CMA-BJ. J Appl Meteor Sci, 2023, 34(6): 641-654. DOI: 10.11898/1001-7313.20230601.

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A Probabilistic Forecast Experiment of Short-duration Heavy Rainfall in Beijing Based on CMA-BJ

DOI: 10.11898/1001-7313.20230601

1.
Beijing Weather Forecast Center, Beijing 100089
2.
Institute of Urban Meteorology, CMA, Beijing 100089

Received Date: 2023-08-30
Rev Recd Date: 2023-10-10
Publish Date: 2023-11-27

Abstract

Abstract

Based on numerical prediction products from China Meteorological Administration Beijing model (CMA-BJ), precipitation observation of ground weather stations in Beijing and ECMWF ERA5 dataset, the hourly rainfall samples from April to September during 2019-2021 are divided into short-duration heavy rainfall (SDHR, greater than 20 mm·h^-1), ordinary rainfall (between 0.1 and 19.9 mm·h^-1) and no rainfall (less than 0.1 mm·h^-1). The probability density distribution characteristics of physical parameters are comparatively analyzed, including moisture conditions, thermal and energy conditions, and dynamic conditions for three categories. Monthly predictors are selected from these parameters by comparing their ability to discriminate among SDHR, ordinary rainfall and no rainfall weather. It is found that the distributions and thresholds of physical parameters differ between months to some extent. Among that, moisture conditions, thermal and energy conditions, and dynamic conditions are relatively stronger but with less discrimination degrees among SDHR, ordinary rainfall and no rainfall weather in July and August. The background circulation and the distributions of physical parameters show obvious monthly differences, so the forecast model is established for each different period. After that, forecast model of SDHR for 0-12 h at 1 h intervals in different periods is established by using the ingredients-based method and fuzzy logic algorithms. When probabilistic and composite reflectivity thresholds are 0.6 dBZ and 15 dBZ, threat score (TS) and bias of SDHR are 0.14 and 1.14 in Beijing from April to September during 2019-2021, showing relatively better forecasts. Therefore, the probabilistic and composite reflectivity thresholds corresponding to the optimal TS and bias for 2019-2021 are taken as the forecast probability and eliminating false thresholds of SDHR, and 0-12 h hourly forecast products of SDHR four times a day are tested from April to September of 2022. Results show that TS and bias of SDHR are 0.104 and 1.341, respectively, indicating that the probability prediction products are better than that of CMA-BJ. SDHR products achieve greater improvement, and balance hit rate and false alarm rate well in the piedmont and plain areas with high SDHR frequency. But performances in mountainous areas are not as good as that in plain areas, which may be related to less stations in the mountainous areas of the forecast model. In addition, the result based on case analysis show the predicted area of the products is relatively larger, but high probability area has a good indication for SDHR in Beijing.
- short-duration heavy rainfall;
- CMA-BJ;
- physical parameters;
- forecast model

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References(45)

Figures and Tables

Fig. 1 Spatial distribution of 193 weather stations in Beijing (the shaded denotes elevation)

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Fig. 2 Distribution of frequency(a) and ratio of monthly frequency to total frequency(b) of short-duration heavy rainfall in Beijing from Apr to Sep during 2019-2022

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Fig. 3 Monthly averaged 500 hPa geopotential height (the black line, unit:dagpm), 850 hPa wind (the barb), 850 hPa pseudo-equivalent potential temperature (the red line, unit:K), and 850 hPa relative humidity (the shaded) during 1991-2020

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Fig. 4 Box plots of physical parameters of short-duration heavy rainfall (the red), ordinary rainfall (the blue), and no rainfall (the black) in Beijing from Apr to Sep during 2019-2021

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Fig. 5 Threat score and bias with respect to different probability thresholds of short-duration heavy rainfall for probabilistic model forecast from Apr to Sep during 2019-2021

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Fig. 6 Threat score and bias of short-duration heavy rainfall for probabilistic model forecast from Jun to Sep in 2022

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Fig. 7 Threat score and bias of short-duration heavy rainfall for probabilistic model forecast and CMA-BJ model in Beijing from Apr to Sep in 2022

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Fig. 8 Short-duration heavy rainfall observation (the green dot), forecasts for CMA-BJ (the black contour, unit:mm·h^-1) and probabilistic model (the shaded) in Beijing from 1700 BT to 2000 BT on 4 Aug 2022

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Fig. 9 Precipitable water, K_I, I_SWEAT and V₇₀₀ at 1900 BT 4 Aug 2022

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Table 1 Overlappingsize (unit:%) of probability density distributions of moisture conditions, thermal and energy conditions, and dynamic conditions of short-duration heavy rainfall with ordinary rainfall(S₁) and with no rainfall(S₂) weather types

类别	物理参量	4月和5月		6月		7月		8月		9月
类别	物理参量	S₁	S₂	S₁	S₂	S₁	S₂	S₁	S₂	S₁	S₂
水汽条件	整层可降水量	47.6	22.8	72.1	39.8	81.7	52.7	78.7	42.3	59.3	27.7
	925 hPa比湿	41.7	24.6	57.8	40.9	79.1	69.8	71.8	53.4	51.5	35.6
	850 hPa比湿	41.6	22.0	49.0	36.7	76.8	64.8	73.0	48.4	53.6	31.3
	700 hPa比湿	64.1	24.8	70.9	54.5	85.5	57.8	76.7	48.4	76.2	30.0
	925 hPa相对湿度	61.7	38.2	72.1	49.0	89.1	69.6	85.7	57.3	79.2	43.3
	850 hPa相对湿度	67.1	36.4	76.5	43.7	90.8	68.0	86.9	52.2	81.0	35.0
	700 hPa相对湿度	53.9	36.4	63.8	61.1	88.4	56.5	89.0	46.9	59.9	36.0
	925 hPa水汽通量	66.2	49.6	71.4	57.9	85.3	69.8	76.9	61.8	80.2	72.5
	850 hPa水汽通量	67.7	47.2	72.6	57.2	83.9	69.6	79.3	64.4	76.2	64.9
	700 hPa水汽通量	65.2	45.4	74.6	55.6	85.5	61.6	78.8	54.8	78.4	60.2
热力和能量条件	对流有效位能	48.6	44.4	43.7	41.3	78.0	82.8	74.2	76.9	66.6	70.5
	对流抑制能量	57.5	48.3	65.9	56.8	82.5	82.1	79.2	80.5	75.2	80.1
	最优抬升指数	38.9	34.1	37.9	33.7	76.0	82.4	65.4	60.3	55.7	65.6
	沙氏指数	38.3	32.3	41.8	36.2	74.7	79.8	67.1	57.3	55.9	58.8
	总指数	48.1	50.7	46.3	46.1	81.6	82.4	72.1	77.5	62.3	71.1
	K指数	36.9	21.0	67.8	41.2	79.8	56.1	68.1	34.5	63.4	32.8
	强天气威胁指数	36.5	28.0	43.6	33.2	75.1	62.9	74.1	45.1	57.2	37.2
	850 hPa和500 hPa假相当位温差	40.0	37.5	43.6	49.5	79.2	80.8	69.6	87.5	60.1	71.4
	850 hPa和500 hPa温差	49.3	62.4	48.3	66.4	80.1	73.3	77.1	77.5	61.8	49.2
动力条件	925 hPa散度	72.1	69.9	74.8	67.6	87.2	74.3	79.3	67.4	81.2	80.1
	850 hPa散度	74.5	67.0	74.5	67.5	87.4	75.6	81.1	71.2	78.5	77.1
	300 hPa散度	68.4	68.3	63.1	55.3	83.3	66.1	76.9	63.2	78.4	75.7
	850 hPa经向风	56.6	60.5	64.0	60.7	85.2	71.4	82.8	57.7	67.6	66.0
	700 hPa经向风	63.5	42.0	72.1	56.7	86.7	57.4	80.1	50.5	74.4	49.4
	700 hPa垂直速度	68.4	67.7	68.4	58.4	82.0	63.7	73.7	58.7	86.5	80.7
	0~1 km垂直风切变	72.6	72.6	67.1	63.1	84.1	69.9	81.5	63.2	76.6	68.8
	0~3 km垂直风切变	60.5	57.0	78.2	70.0	87.4	71.9	82.8	61.9	73.0	72.4
	0~6 km垂直风切变	68.2	63.4	64.0	57.8	80.2	72.8	86.3	81.6	74.8	76.0
	3~6 km垂直风切变	69.4	71.8	61.6	57.8	89.0	86.2	86.5	84.3	70.4	79.2

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