Ensemble Forecast Experiments of PM<sub>2.5</sub> Based on Multiple Boundary Layer Schemes in Tianjin

Cai Ziying; Yao Qing; Han Suqin; Hao Tianyi; Liu Jingle

doi:10.11898/1001-7313.20170509

Vol.28, NO.5, 2017

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Article Navigation > Journal of Applied Meteorological Science > 2017 > 28(5): 611-620

Cai Ziying, Yao Qing, Han Suqin, et al. Ensemble forecast experiments of PM2.5 based on multiple boundary layer schemes in Tianjin. J Appl Meteor Sci, 2017, 28(5): 611-620. DOI: 10.11898/1001-7313.20170509.

Citation:

Cai Ziying, Yao Qing, Han Suqin, et al. Ensemble forecast experiments of PM_2.5 based on multiple boundary layer schemes in Tianjin. J Appl Meteor Sci, 2017, 28(5): 611-620. DOI: 10.11898/1001-7313.20170509.

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Ensemble Forecast Experiments of PM_2.5 Based on Multiple Boundary Layer Schemes in Tianjin

DOI: 10.11898/1001-7313.20170509

1.
Tianjin Environmental Meteorological Center, Tianjin 300074
2.
Tianjin Institute of Meteorology, Tianjin 300074

Received Date: 2017-01-20
Rev Recd Date: 2017-07-07
Publish Date: 2017-09-30

Abstract

Abstract

Based on the atmospheric chemical model WRF/Chem, four kinds of boundary layer schemes (YSU, BL, MYJ and MYN3) are used to simulate the evolution of PM_2.5 mass concentration of Tianjin in 2015. Effects of different boundary layer schemes on the simulation and prediction of PM_2.5 mass concentration are analyzed, and a set of prediction products with various boundary layer schemes are constructed to improve forecast effects. Results show that the best boundary layer scheme for the near surface temperature simulation is BL scheme, the best boundary layer scheme for relative humidity simulation is MYN scheme and YSU scheme, the best boundary layer scheme for wind speed simulation is YSU scheme, and four boundary layer schemes of atmospheric chemical model have good applicability in simulation of air quality. The correlation coefficient between the simulated value and the actual value can reach 0.76, and the relative error is between 31.7% and 33%. Among four boundary layer schemes, MYN scheme leads to highest simulated boundary layer height, and the simulated boundary layer height of BL scheme is the lowest. As for the correlation coefficient between boundary layer height and PM_2.5 mass concentration, BL scheme is the highest (0.64), comparing to 0.62 with YSU scheme and MYJ scheme, and only 0.5 with MYN scheme. No single scheme has significant advantages. BL scheme is better in sunny and windy weather, while YSU and MYJ schemes perform better in cloudy and breeze weather. The simulated PM_2.5 mass concentration in Tianjin shows a significant disturbance characteristic with different boundary layer schemes. The standard deviation of daily average PM_2.5 concentration is about 5.2 μg·m^-3, accounting for 8% of the mean, and its maximum can reach 23 μg·m^-3. The hourly standard deviation reaches 11.8%, which is greater than the daily standard deviation, especially in the process of mutual transformation between stable boundary layer and unstable boundary layer. To overcome these problems, air quality ensemble prediction tests of multiple boundary layer schemes are carried out in Tianjin. Based on the analysis of forecast value in 2015, the ensemble prediction of multiple boundary layer schemes and disturbance of multiple aerosol mechanisms can decrease the relative error and root mean square error of PM_2.5 mass concentration prediction by about 25%. It can also reduce the false negative rate of heavy pollution weather from 44% to 30% and improve the forecasting capabilities of air quality level by 3%-6%. When the computing resources are sufficient, it is also an effective means to enhance the forecast ability of PM_2.5 mass concentration.
- WRF/Chem;
- boundary layer scheme;
- meteorological tower;
- Tianjin

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References(30)

Figures and Tables

Fig. 1 The standard deviation percentage of mean value based on different boundary layer scheme of Tianjin

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Fig. 2 The standard deviation percentage of mean value based on different boundary layer scheme about PM_2.5 of Tianjin

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Fig. 3 The simulated PM_2.5 based on different boundary layer scheme and the observed PM_2.5 of Tianjin

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Fig. 4 The simulated air temperature based on different boundary layer scheme and the observed air temperature of Tianjin

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Fig. 5 Variation of boundary layer height based on different boundary layer scheme of Tianjin

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Fig. 6 Standard deviation percentage

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Fig. 7 PM_2.5 mass concentration based on MOSAIC and MADE mechanisms

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Fig. 8 Ensemble prediction of three heavily polluted weather forecasts about PM_2.5 mass concentration of Tianjin in 2015

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Table 1 Comparison of the simulated and the observed values based on different boundary layer schemes

分类	边界层方案	相关系数	相对误差/%	命中率/%
气温	YSU	0.996	6.9	92.27
	MYN	0.993	14.2	86.19
	MYJ	0.996	7.8	89.23
	BL	0.996	5.5	95.03
相对湿度	YSU	0.90	10.8	80.11
	MYN	0.80	15.4	67.40
	MYJ	0.89	10.5	80.39
	BL	0.90	12.9	72.38
地面风速	YSU	0.87	72.4	74.59
	MYN	0.70	77.2	69.61
	MYJ	0.89	97.3	53.59
	BL	0.89	89.5	57.77
PM_2.5质量浓度	YSU	0.76	31.7	83.70
	MYN	0.75	33.4	80.11
	MYJ	0.76	33.2	79.56
	BL	0.75	33.0	81.77
注：命中率为模拟值与观测值差小于某一标准的数据所占百分比，本文规定气温差值为2 K，风速差值为2 m·s^-1，相对湿度差值为10%，PM_2.5质量浓度差值为35 μg·m^-3。

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Table 2 The relative error of PM_2.5 mass concentration simulation based on different boundary layer scheme under different weather conditions and meteorological conditions(unit: %)

类别	划分依据	边界层方案				平均值
类别	划分依据	YSU	MYJ	MYN	BL	平均值
云量划分/成	[0, 1]	30.98	32.80	32.99	29.92	31.67
	(1, 3]	26.45	26.21	27.96	26.81	26.86
	(3, 7]	31.05	33.18	33.13	32.68	32.51
	(7, 10]	34.51	35.83	35.86	36.37	35.64
地面太阳辐射/(W·m^-2)	[0, 30]	29.83	29.34	32.95	30.32	30.61
	(30, 100]	35.82	35.63	37.53	37.41	36.60
	(100, 200]	31.13	32.21	30.57	31.04	31.24
	(200, +∞)	26.41	32.02	32.03	29.86	30.08
天气形势	低压型	28.98	30.60	30.36	31.16	30.28
	高压型	35.47	35.87	37.13	35.25	35.93
	冷空气影响型	42.51	47.55	44.17	42.86	44.27
	均压型	25.41	26.71	27.22	27.88	26.81
	平直型	32.28	31.32	34.82	33.76	33.05

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Table 3 The forecast effect of PM_2.5 mass concentration about different model

	预报来源	平均偏差/(μg·m^-3)	相对误差/%	归一化均方根误差
全样本	天津单一模式	1.87	32.99	0.49
	CUACE模式	34.14	60.32	0.81
	BREMPS	-12.34	37.23	0.57
	模式平均值(天津5组方案)	2.97	32.03	0.49
检验样本	天津单一模式	0.35	34.08	0.45
	CUACE模式	33.27	62.33	0.77
	BREMPS	-12.29	37.53	0.53
	独立模式集成	1.07	42.28	0.54
	多边界层方案集合	0.99	31.88	0.41
注：独立模式集成指中国气象局CUACE与区域中心BREMPS集成产品，多边界层方案集合指MOSAIC气溶胶机制下4组边界层参数方案成员和MADE气溶胶机制YSU边界层方案成员的神经网络集合产品。

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