动力降尺度方法在辐射雾大涡模拟中的应用

doi:10.13209/j.0479-8023.2017.011

摘要/Abstract

摘要：

针对大涡模拟(LES)计算精确但耗费大量计算机资源的特点, 采用动力降尺度的方法, 把传统的边界层(PBL)方案与LES模拟相结合, 利用WRFV3.7模式四重到五重嵌套网格, 以华北地区一次辐射雾为例进行模拟, 试图保留LES计算精确的特点, 并减少计算机时。结果表明, 动力降尺度方法能够准确地预报雾的发生, 与同样水平分辨率的LES模拟相比, 四层嵌套网格节省47.02%计算机时, 五层嵌套网格节省67.67%计算机时, 可以为未来高分辨率业务预报系统的LES模拟实现提供一种可能性。

关键词: 辐射雾, 大涡模拟, 边界层, 降尺度, 计算机时

Abstract:

The large eddy simulation (LES) has accurate calculation, but consumes a lot of computer resources. Based on the WRFV3.7 model with four to five nested domains, a radiation fog in North China is simulated through the dynamic downscaling method, to retain the advantages of the LES and reduce the computational time. The results show that, compared with LES scheme under the same horizontal grid resolution, dynamic downscaling method can predict formation of fog more successfully, and in addition, save 47.02% to 67.67% computational time. This study raise possibility for the LES scheme to be used in the operational forecast system in the future.

Key words: radiation fog, large eddy simulation, boundary layer, downscaling, computational time

林继配, 张庆红. 动力降尺度方法在辐射雾大涡模拟中的应用[J]. 北京大学学报自然科学版, 2017, 53(4): 607-616.

Jipei LIN, Qinghong ZHANG. Application of Dynamic Downscaling Method for the Large Eddy Simulation in a Radiation Fog Case[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2017, 53(4): 607-616.

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链接本文: https://xbna.pku.edu.cn/CN/10.13209/j.0479-8023.2017.011

https://xbna.pku.edu.cn/CN/Y2017/V53/I4/607

图/表 9

图1 高速公路站点与常规气象观测站点在 D03区域内的分布情况

Fig. 1 Distribution of freeway and meteorology observation stations in D03

图2 各实验的模拟区域

Fig. 2 Configurations of the nested model domains

表1 各实验的模式分辨率、水平格点数和边界层方案设置

Table 1 Configuration of resolution, horizontal grid points and boundary layer schemes of different experiments

实验名称		参数名称	参数设置
实验名称		参数名称	D01	D02	D03	D04	D05
PBL3.3km(3p)	分辨率		30 km	10 km	3.3 km	-	-
	水平格点		100×115	202×226	301×301	-	-
	边界层方案		QNSE	QNSE	QNSE	-	-
	feedback		-	1	1	-	-
PBL1.1km(4p)	分辨率		30 km	10 km	3.3 km	1.1 km	-
	水平格点		100×115	202×226	301×301	412×412	-
	边界层方案		QNSE	QNSE	QNSE	QNSE	-
	feedback		-	1	1	1	-
LES1.1km(4l)	分辨率		30 km	10 km	3.3 km	1.1 km	-
	水平格点		100×115	202×226	301×301	412×412	-
	边界层方案		QNSE	QNSE	QNSE	-	-
	feedback		-	1	1	1	-
PBL222m(5p)	分辨率		30 km	10 km	3.3 km	1.1 km	222 m
	水平格点		100×115	202×226	301×301	412×412	1201×1201
	边界层方案		QNSE	QNSE	QNSE	QNSE	QNSE
	feedback		-	1	1	1	1
LES222m(5l)	分辨率		30 km	10 km	3.3 km	1.1 km	222 m
	水平格点		100×115	202×226	301×301	412×412	1201×1201
	边界层方案		QNSE	QNSE	QNSE	QNSE	关闭
	feedback		-	1	1	1	1

图3 D03区域地面观测站与不同PBL实验雾区对比

Fig. 3 Comparison of observation and different PBL simulations in D03

图4 D03区域不同模拟实验的ETS和Bias评分

Fig. 4 ETS and Bias of different experiments in D03

图5 D05区域地面观测站与不同LES实验雾区对比

Fig. 5 Comparison of observation and different LES simulations in D05

图6 各实验所需的计算机时

Fig. 6 Computational time of different experiments

图7 D05区域地面观测站与不同ndown实验雾区对比

Fig. 7 Comparison of observation and different ndown simulations in D05

图8 D05区域LES实验与ndown实验的ETS和Bias评分

Fig. 8 ETS and Bias of different LES simulations and ndown simulations in D05

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