边界层参数化方案对一次西北地区沙尘天气过程影响的数值模拟研究

doi:10.13866/j.azr.2021.01.18

Abstract

Abstract:

In this study, WRF-Chem(version 3.4) model was used to compare the performance of different Planetary Boundary Layer (PBL) parameterization namely, the YSU(Yonsei University), MYJ(Mellor-Yamada-Janjic), QNSE(Quasi-Normal Scale Elimination), MYNN2.5(Mellor-Yamada-Nakanisfii and Niino 2.5) and BouLac PBL schemes, over the dust event in northwest china on 27 March 2007. Surface observations were used for comparisons and evaluating model performance for meteorological variables. It is shown that simulations with the five PBL schemes can successfully reproduce the evolution of the dust event. The YSU and BouLac schemes produced higher surface friction velocity, 10 m wind speed, 2 m air temperature and surface PM₁₀ concentration and lower 2-mrelative humidity, thus simulating stronger weather processes than those of the MYJ, QNSE and MYNN2.5 schemes. These results indicate that different boundary layer schemes affect the dust emission flux and PM₁₀ concentration through different simulation effects of friction velocity. The dust emission flux and PM₁₀ concentration tend to increase with higher friction velocity. Therefore, the dust event was enhanced due to the high friction velocity and the characteristics of high temperature as well as lower humidity in near-surface layer in the afternoon. As a result, the simulated dust event with the BouLac scheme was the strongest while the weakest by the QNSE scheme. Observations from Minqin meteorological station are used to validate the simulated results over Minqin region. Statistical analysis of the five simulations shows that the QNSE scheme simulated better PM₁₀ concentration, the BouLac scheme performs well for 10 m wind speed and the YSU scheme resulted in the best model performance for simulating air temperature and relative humidity at 2 m. Overall, the YSU scheme was concluded as the best PBL scheme for the dust storm and the QNSE scheme was the worst one.

Key words: WRF-Chem, sand and dust, planetary boundary layer, numerical simulation

WEI Qian,LONG Xiao,ZHAO Jianhua,HAN Zifei,WANG Siyi. Impact of boundary layer parameterization schemes on the simulation of a dust event over Northwest China[J].Arid Zone Research, 2021, 38(1): 163-177.

Figures/Tables 13

Fig. 1

Tab. 1

Tab. 2

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig.9

Tab. 3

Tab. 4

References 32

[1]	张强. 大气边界层气象学研究综述[J]. 干旱气象, 2003,21(3):74-78.
	[ Zhang Qiang. Review of atmospheric boundary layer meterology[J]. Arid Meteorology, 2003,21(3):74-78. ]
[2]	Banks R F, Tiana-Alsina J, Maria Baldasano J, et al. Sensitivity of boundary-layer variables to PBL schemes in the WRF model based on surface meteorological observations, lidar, radiosondes during the HygrA-CD campaign[J]. Atmospheric Research, 2016, 176-177:185-201. doi: 10.1016/j.atmosres.2016.02.024
[3]	Milovac J, Warrach-Sagi K, Behrendt A, et al. Investigation of PBL schemes combining the WRF model simulations with scanning water vapor differential absorption lidar measurements[J]. Journal of Geophysical Research: Atmospheres, 2016,121(2):624-649. doi: 10.1002/2015JD023927
[4]	Xie B, Fung J C H, Chan A, et al. Evaluation of nonlocal and local planetary boundary layer schemes in the WRF model[J]. Journal of Geophysical Research, 2012,117(D12103).
[5]	张碧辉, 刘树华, 马雁军, 等. MYJ和YSU方案对WRF边界层气象要素模拟的影响[J]. 地球物理学报, 2012,55(7):2239-2248.
	[ Zhang Bihui, Liu Shuhua, Ma Yanjun, et al. The effect of MYJ and YSU schemes on the simulation of boundary layer[J]. Chinese Journal of Geophysics, 2012,55(7):2239-2248. ]
[6]	黄文彦, 沈新勇, 王卫国, 等. 边界层参数化方案对边界层热力和动力结构特征影响的比较[J]. 地球物理学报, 2014,57(5):1399-1414.
	[ Huang Wenyan, Shen Xinyong, Wang Weiguo, et al. Comparison of the thermal and dynamic structural characteristics in boundary layer with different boundary layer parameterizations[J]. Chinese Journal of Geophysics, 2014,57(5):1399-1414. ]
[7]	王丽霞, 王颖, 赖锡柳, 等. WRF模式不同边界层参数化方案模拟兰州冬季边界层高度的研究[J]. 高原气象, 2017,36(1):162-172.
	[ Wang Lixia, Wang Ying, Lai Xiliu, et al. Study on the simulation of boundary layer height in Lanzhou in winter using WRF model with different boundary layer parameterization schemes[J]. Plateau Meteorology, 2017,36(1):162-172. ]
[8]	孟露, 赵天良, 杨兴华, 等. 塔克拉玛干沙漠腹地大气边界层参数化方案的模拟评估[J]. 气象科学, 2018,38(2):157-166.
	[ Meng Lu, Zhao Tianliang, Yang Xinghua, et al. An assessment of atmospheric boundary layer schemes over the Taklimakan Desert hinterland[J]. Journal of the Meteorological Sciences, 2018,38(2):157-166. ]
[9]	Gunwani P, Mohan M. Sensitivity of WRF model estimates to various PBL parameterizations in different climatic zones over India[J]. Atmospheric Research, 2017,194(9):43-65.
[10]	Mohan M, Gupta M. Sensitivity of PBL parameterizations on PM₁₀, and ozone simulation using chemical transport model WRF-Chem over a sub-tropical urban airshed in India[J]. Atmospheric Environment, 2018,185:53-63.
[11]	Banks R F, Baldasano J M. Impact of WRF model PBL schemes on air quality simulations over Catalonia, Spain[J]. Science of The Total Environment, 2016,572:98-113.
[12]	Sathyanadh A, Prabha T V, Balaji B, et al. Evaluation of WRF PBL parameterization schemes against direct observations during a dry event over the Ganges valley[J]. Atmospheric Research, 2017,193:125-141.
[13]	丁成慧, 李江南, 赵杨洁, 等. 边界层参数化方案对南海秋季台风“莎莉嘉”(2016)模拟的影响[J]. 热带气象学报, 2018,34(5):657-673.
	[ Ding Chenghui, Li Jiangnan, Zhao Yangjie, et al. The influence of boundary layer parameterization schemes on autumn typhoon SARIKA(2016) in south China Sea[J]. Journal of Tropical Meteorology, 2018,34(5):657-673. ]
[14]	温晓培, 隆霄, 张述文, 等. 边界层参数化方案对台风SANBA初生阶段影响的数值模拟研究[J]. 热带气象学报, 2016,32(3):346-357.
	[ Wen Xiaopei, Long Xiao, Zhang Shuwen, et al. Parameterization schemes on typhoon SANBA during its initial phase[J]. Journal of Tropical Meteorology, 2016,32(3):346-357. ]
[15]	Liu J, Zhang F, Pu Z. Numerical simulation of the rapid intensification of hurricane Katrina(2005): Sensitivity to boundary layer parameterization schemes[J]. Advances in Atmospheric Sciences, 2017,34(4):482-496.
[16]	崔驰潇, 包云轩, 袁成松, 等. 不同边界层参数化方案对江苏地区一次平流雾过程的模拟影响[J]. 大气科学, 2018,42(6):1344-1362.
	[ Cui Chixiao, Bao Yunxuan, Yuan Chengsong, et al. Influence of different boundary layer parameterization schemes on the simulation of an advection fog process in Jiangsu[J]. Chinese Journal of Atmospheric Sciences, 2018,42(6):1344-1362. ]
[17]	Mylonas M P, Nastos P T, Matsangouras I T. PBL parameterization schemes sensitivity analysis on WRF modeling of a tornadic event environment in Skala Lakonia in September 2015[J]. Atmospheric Research, 2018,208:116-131.
[18]	高笃鸣, 李跃清, 蒋兴文, 等. WRF模式多种边界层参数化方案对四川盆地不同量级降水影响的数值试验[J]. 大气科学, 2016,40(2):371-389.
	[ Gao Duming, Li Yueqing, Jiang Xingwen, et al. Influence of planetary boundary layer parameterization schemes on the prediction of rainfall with different magnitudes in the Sichuan Basin using the WRF model[J]. Chinese Journal of Atmospheric Sciences, 2016,40(2):371-389. ]
[19]	李斐, 邹捍, 周立波, 等. WRF模式中边界层参数化方案在藏东南复杂下垫面适用性研究[J]. 高原气象, 2017,36(2):340-357.
	[ Li Fei, Zou Han, Zhou Libo, et al. Study of boundary layer parameterization schemes’ applicability of WRF model over complex underlying surfaces in Southeast Tibet[J]. Plateau Meteorology, 2017,36(2):340-357. ]
[20]	Wang J K, Zhang B H, Zhang H D, et al. The impacts of three PBL schemes on the simulation of boundary layer meteorological factors and air pollutants in Northern China[J]. IOP Conference Series: Earth and Environmental Science, 2019,227(5):98-113.
[21]	刘筱冉, 王金艳, 邱继勇, 等. 起沙方案对西北地区沙尘过程模拟的影响[J]. 环境保护科学, 2018,44(4):69-76.
	[ Liu Xiaoran, Wang Jinyan, Qiu Jiyong, et al. Impact of dust emission schemes on the simulation of dust storms in the Northwest region[J]. Environmental Protection Science, 2018,44(4):69-76. ]
[22]	黄豪杰. 基于超大尺度结构模化的沙尘暴及沙墙形态数值研究[D]. 兰州: 兰州大学, 2018.
	[ Huang Haojie. Numerical Studies on Sand-Dust Storms and Sand Wall Morphology Based on the Modeling of the Very-Large-Scale Motions[D]. Lanzhou: Lanzhou University, 2018. ]
[23]	赵建华, 张峰, 梁芸, 等. 大气边界层湍流相干结构研究进展[J]. 干旱区研究, 2019,36(6):1419-1430.
	[ Zhao Jianhua, Zhang Feng, Liang Yun, et al. Research progress on turbulent coherent structure in atmospheric boundary layer[J]. Arid Zone Research, 2019,36(6):1419-1430. ]
[24]	Hong S Y. A new vertical diffusion package with an explicit treatment of entrainment processes[J]. Monthly Weather Review, 2006,134(9):2318-2341.
[25]	Janjić Z I. The Step-Mountain Eta Coordinate Model: Further developments of the convection, viscous sublayer, and turbulence closure schemes[J]. Monthly Weather Review, 1994,122(5):927-945.
[26]	Nakanishi M, Niino H. An improved Mellor-Yamada Level-3 Model: Its numerical stability and application to a regional prediction of advection fog[J]. Boundary-Layer Meteorology, 2006,119(2):397-407.
[27]	Sukoriansky S, Galperin B, Perov V. Application of a new spectral theory of stably stratified turbulence to the atmospheric boundary layer over sea ice[J]. Boundary-Layer Meteorology, 2005,117(2):231-257.
[28]	Bougeault P, Lacarrre P. Parametrization of orography-induced turbulence in a meso-beta model[J]. Monthly Weather Review, 1989,117:1872-1890.
[29]	Nabavi S O, Haimberger L, Samimi C. Sensitivity of WRF-chem predictions to dust source function specification in West Asia[J]. Aeolian Research, 2017,24:115-131.
[30]	隆霄, 赵建华, 黄建平. “07. 3”强沙尘暴天气过程的观测分析[J]. 兰州大学学报(自然科学版), 2012,48(1):69-74.
	[ Long Xiao, Zhao Jianhua, Huang Jianping. Observation and analysis of ‘07. 3’ severe sandstorm[J]. Journal of Lanzhou University (Natural Sciences), 2012,48(1):69-74. ]
[31]	魏倩, 隆霄, 田畅, 等. 民勤一次沙尘暴天气过程的近地层气象要素多尺度特征[J]. 干旱区研究, 2018,35(6):1352-1362.
	[ Wei Qian, Long Xiao, Tian Chang, et al. Multiscale meteorological characteristics during a sandstorm in Minqin[J]. Arid Zone Research, 2018,35(6):1352-1362. ]
[32]	魏倩. 一次沙尘天气过程发展演变的多尺度特征研究[D]. 兰州: 兰州大学, 2020.
	[ Wei Qian. Studies on Multiscale Characteristics during the Evolution of A Dust Event[D]. Lanzhou: Lanzhou University, 2020. ]

模拟域	D01	D02	D03
模拟时段	2007年3月27日00:00 UTC-28日00:00 UTC
模拟中心	103.08°E,38.63°N	103.08°E,38.63°N	103.08°E,38.63°N
水平分辨率	15 km	5 km	1.67 km
格点维数	151×111	271×211	391×331
垂直层数	30层	30层	30层
顶部气压	50 hPa	50 hPa	50 hPa
积分步长	60 s	20 s	6.7 s
输出时间分辨率	1 h	5 min	5 min
微物理过程	WSM5	WSM5	WSM5
短波辐射	RRTM	RRTM	RRTM
长波辐射	RRTM	RRTM	RRTM
积云参数化	Kain-Fritsch	-	-
陆面过程	Noah	Noah	Noah
边界层及近地层参数化	YSU(MM5 M-O)、MYJ(M-O)、QNSE(QNSE)、MYNN2.5(MYNN)和BouLac(MM5 M-O)
起沙参数化	GOCART-AFWA	GOCART-AFWA	GOCART-AFWA

边界层方案	闭合方法及阶数	主要特征	边界层顶
YSU^[24]	非局地1阶	增加反梯度项及卷夹通量项,增加热力诱导的湍流混合、降低动力强迫的湍流混合	理查逊数(Ri),不稳定层结,Ri=0.25,稳定,Ri=0
MYJ^[25]	局地1.5阶	反梯度扩散,用Mellor-Yamada闭合模型表示表面层以上的湍流	湍流动能廓线,TKE降至0.2 m²·s^-2时高度
QNSE^[27]	局地1.5阶	不稳定层结：采用引入波动项的MYJ方案湍流动能闭合方案;稳定层结：采用稳定条件下改进的湍流谱闭合模式扩散方案	湍流动能廓线,TKE降至0.01 m²·s^-2时高度
MYNN2.5^[26]	局地1.5阶	反梯度扩散,对气压相关项进行参数化处理,增加表征浮力与切变作用的一组闭合参数	湍流动能廓线,TKE降至1.0×10^-6m²·s^-2时高度
BouLac^[28]	局地1.5阶	能预报不同类型下垫面、湍流强度和具体位置,并持续预报湍流动能强度	湍流动能廓线,TKE降至0.005 m²·s^-2时高度

气象要素		平均时段	OBS	YSU	MYJ	QNSE	MYNN2.5	BouLac
10 m风速/(m·s^-1)	均值	07:00—12:00 UTC	10.6	10.4	11.2	10.3	11.2	11.3
10 m风速/(m·s^-1)	峰值	-	15	13.2	15	14	14.3	14.1
2 m温度/℃	均值	00:00—00:00 UTC	12.6	11.1	10.7	10.3	10.6	11
2 m温度/℃	峰值	-	21.8	20.5	21.4	20.7	20.7	21.2
2 m相对湿度/%	均值	00:00—00:00 UTC	26	25	29	32	29	27
2 m相对湿度/%	峰值	-	43	50	58	65	56	57
PM₁₀浓度/(μg·m^-3)	均值	07:00—12:00 UTC	4503	4787	3402	3301	5944	6713
PM₁₀浓度/(μg·m^-3)	峰值	-	9647	9658	6946	5462	12255	16129
沙尘排放通量/(μg·m^-2·s^-1)	均值	03:00—12:00 UTC	-	694	540	448	1016	861
沙尘排放通量/(μg·m^-2·s^-1)	峰值	-	-	2996	2548	1378	4497	4547
摩擦速度/(m·s^-1)	均值	03:00—12:00 UTC	-	0.90	0.86	0.83	0.90	0.90
摩擦速度/(m·s^-1)	峰值	-	-	1.17	1.14	1.08	1.25	1.25

边界层方案	10 m风速/(m·s^-1)		2 m温度(扰动2 m温度)/℃		2 m相对湿度(扰动2 m相对湿度)/%		PM₁₀浓度/(μg·m^-3)
边界层方案	R	RMSE	R	RMSE	R	RMSE	R	RMSE
YSU	0.86	2.33	0.98(0.24)	2.11	0.98(-0.13)	3.26	0.78	1386
MYJ	0.84	2.85	0.98(0.16)	2.75	0.97(-0.04)	6.57	0.80	1253
QNSE	0.79	2.79	0.98(0.13)	3.01	0.97(-0.01^*)	10.44	0.81	1220
MYNN2.5	0.89	2.21	0.98(0.15)	2.69	0.98(-0.06)	6.31	0.80	1725
BouLac	0.92	1.93	0.98(0.23)	2.28	0.96(-0.06)	5.11	0.78	2157

Impact of boundary layer parameterization schemes on the simulation of a dust event over Northwest China

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 13

References 32

Related Articles 11

Metrics

Comments

Recommended 0

[1]	YAN Qing, LI Juyan, YIN Zhongdong, LIU Jinmiao, LIU Hongcai. Numerical simulation of the influence of typical shrub types on wind-sand flow field [J]. Arid Zone Research, 2023, 40(5): 785-797.
[2]	ZHANG Tianyi, LIU Jie, YANG Zhiwei, WANG Bin, CHENG Qiulian. Numerical simulation of avalanche process in Aerxiangou, West Tianshan Mountains, based on air-ground cooperative investigation [J]. Arid Zone Research, 2023, 40(11): 1729-1743.
[3]	XUE Chengjie, ZHANG Kecun, AN Zhishan, ZHANG Hongxue, PAN Jiapeng. Influences of railway viaducts on local wind power: A case study of the Shashangou Bridge used by the Dunge Railway [J]. Arid Zone Research, 2023, 40(10): 1678-1686.
[4]	CAO Yiqing,LONG Xiao,LI Chao,WANG Siyi,ZHAO Jianhua. Numerical study on the effect of low-level jet on two rainstorms on the east side of the Helan Mountain [J]. Arid Zone Research, 2022, 39(6): 1739-1752.
[5]	LIU Jinmiao,LI Juyan,YIN Zhongdong,GUAN Hanxiao,ZHANG Jiawei. Numerical simulation study on the influence of dry Alhagi camelorum on the wind-sand flow field [J]. Arid Zone Research, 2022, 39(5): 1514-1525.
[6]	ZHOU Hong. A comparative study of ponded infiltration in a desert sandy soil based on multi-hydrological models [J]. Arid Zone Research, 2022, 39(1): 123-134.
[7]	ZHANG Xingxin,ZHANG Kai,SHI Boyuan,CUI Baohong,ZHAO Liming. Numerical simulation of wind-blown sand flow field and formation mechanism of sand damage on road surface in shifting dune area [J]. Arid Zone Research, 2021, 38(4): 1184-1191.
[8]	ZHANG Hailiang,LI Huoqing,Ali Mamtimin. Simulation characteristics of planetary boundary layer parameterizations: A case study in Xinjiang during summer [J]. Arid Zone Research, 2021, 38(1): 154-162.
[9]	. Dynamic Parameterization and Simulation of Surface Albedo in Maize Cropland Mulched by Plastic Film in Arid Region [J]. , 2018, 35(2): 461-470.
[10]	ZHU Qing-Liang, JIANG Hao, WANG Ke-Li, LUO Xin-Ping. Effects of Parameterized Physical Process of WRF Model on Simulation of Precipitation in the Heihe River Basin [J]. , 2013, 30(3): 462-469.
[11]	HUANG Jing, ZHANG Qiang. Mesoscale Atmospheric Numerical Simulation and Its Progress [J]. , 2012, 29(2): 273-283.