Characteristics and Application of Background Errors in GRAPES_Meso

Zhuang Zhaorong; Wang Ruichun; Wang Jincheng; Gong Jiandong

doi:10.11898/1001-7313.20190306

Vol.30, NO.3, 2019

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Article Navigation > Journal of Applied Meteorological Science > 2019 > 30(3): 316-331

Zhuang Zhaorong, Wang Ruichun, Wang Jincheng, et al. Characteristics and application of background errors in GRAPES_Meso. J Appl Meteor Sci, 2019, 30(3): 316-331. DOI: 10.11898/1001-7313.20190306.

Citation:

Zhuang Zhaorong, Wang Ruichun, Wang Jincheng, et al. Characteristics and application of background errors in GRAPES_Meso. J Appl Meteor Sci, 2019, 30(3): 316-331. DOI: 10.11898/1001-7313.20190306.

Zhuang Zhaorong, Wang Ruichun, Wang Jincheng, et al. Characteristics and application of background errors in GRAPES_Meso. J Appl Meteor Sci, 2019, 30(3): 316-331. DOI: 10.11898/1001-7313.20190306.

Citation:

Zhuang Zhaorong, Wang Ruichun, Wang Jincheng, et al. Characteristics and application of background errors in GRAPES_Meso. J Appl Meteor Sci, 2019, 30(3): 316-331. DOI: 10.11898/1001-7313.20190306.

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Characteristics and Application of Background Errors in GRAPES_Meso

DOI: 10.11898/1001-7313.20190306

1.
National Meteorological Center, Beijing 100081
2.
Numerical Weather Prediction Center of CMA, Beijing 100081

Received Date: 2018-12-18
Rev Recd Date: 2019-02-11
Publish Date: 2019-05-31

Abstract

Abstract

The statistic structure of background covariance is studied by NMC method of USA based on GRAPES regional model forecast data spanning one year from June 2015 to May 2016. The horizontal correlation length scale is estimated with Gauss function linear fitting method. Characteristics of background error and horizontal correlation length scale with the latitude, height and season are investigated. Results show that background error and horizontal correlation characteristic scale obviously change with height and latitude, and the unbalanced non-dimensional pressure and humidity are closely related to the season. Background errors of four control variables are nonhomogeneous, among which background errors of stream function and unbalanced velocity potential mainly change with latitude and height, background errors of unbalanced non-dimensional pressure and humidity show local and seasonal characteristics. The biggest background errors of the unbalanced non-dimensional pressure occur in the Tibetan Plateau, and they are larger in winter while smaller in summer. The biggest background errors of humidity happen in low latitude of tropical monsoon region, and they are larger in summer while smaller in winter. The horizontal correlation length scales of four control variables with Gauss function fitting are reasonable except that correlation coefficients of the unbalanced non-dimensional pressure are overestimated in close distance and underestimated in far distance. Horizontal correlation length scales of steam, unbalanced velocity potential and humidity obviously change with height are largest in tropopause. The length scale of unbalanced non-dimensional pressure obviously changes with latitude and is larger in low latitude of middle tropospheric. The horizontal correlation length scale of the unbalanced non-dimensional pressure and humidity both are larger in winter and smaller in summer. The horizontal correlation length scales changing with height are used in GRAPES-3DVar system instead of single parameter, and then the analysis and forecast experiment results of one month indicate that, qualities of 6-hour geopotential height forecast in troposphere are improved. Analysis and 12-hour forecast of wind in stratosphere are greatly improved; all levels of 24-hour accumulated precipitation forecast are obviously improved; the false prediction of 24-hour accumulated precipitation of light rain, moderate rain and heavy rain are improved; 12-24-hour accumulated precipitation of extra torrential rain in control test fails to be reported, but the experiment with changing horizontal correlation length scales improves forecasts of positions and values for extra torrential rain.
- background error;
- horizontal correlation length scale;
- NMC method of USA;
- 3DVar;
- GRAPES_Meso

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References(34)

Figures and Tables

图 1 第10层流函数(a)、非平衡势函数(b)、非平衡Exner气压(c)、比湿(d)背景误差水平分布

Fig. 1 Background errors for stream function(a), unbalanced velocity potential(b), unbalanced non-dimensional pressure(c) and specific humidity(d) at level 10

DownLoad: Full-Size Img PowerPoint

图 2 流函数(单位: 10⁵ m²·s^-1)(a)、非平衡势函数(单位:10⁵ m²·s^-1)(b)、非平衡Exner气压(单位：10^-4)(c)、比湿(单位:g·kg^-1)(d)的背景误差随纬度和高度变化

Fig. 2 The same as in Fig. 1, but for changes with latitude and height

DownLoad: Full-Size Img PowerPoint

图 3 流函数(a)、非平衡势函数(b)、非平衡Exner气压(c)、比湿(d)背景误差随季节变化

Fig. 3 The same as in Fig. 1, but for changes with seasons

DownLoad: Full-Size Img PowerPoint

图 4 沿35°N的第5层非平衡Exner气压背景误差随季节变化

(黑色粗线表示地形高度)

Fig. 4 Background errors for unbalanced non-dimensional pressure at level 5 along 35°N

(black thick line denotes terrain)

DownLoad: Full-Size Img PowerPoint

图 5 统计样本的水平相关系数随距离的变化(灰虚线)及拟合的高斯函数(黑粗线)

(a)流函数, (b)非平衡势函数, (c)非平衡Exner气压, (d)比湿

Fig. 5 Horizontal correlation for statistical samples(grey dashed line) and fitting curve of Gauss function(black thick line)

(a)stream function, (b)unbalanced velocity potential, (c)unbalanced non-dimensional pressure, (d)specific humidity

DownLoad: Full-Size Img PowerPoint

图 6 流函数(a)、非平衡势函数(b)、非平衡Exner气压(c)、比湿(d)的水平相关尺度(单位:10² km)随高度和纬度变化

Fig. 6 Horizontal correlation length changes with height and altitude(unit:10² km) (a)stream function, (b)unbalanced velocity potential, (c)unbalanced non-dimensional pressure, (d)specific humidity

DownLoad: Full-Size Img PowerPoint

图 7 流函数(a)、非平衡势函数(b)、非平衡Exner气压(c)、比湿(d)的水平相关尺度随季节变化

Fig. 7 The same as in Fig. 6, but for changes with seasons

DownLoad: Full-Size Img PowerPoint

图 8 流函数、非平衡势函数、非平衡Exner气压和比湿水平相关尺度随高度变化

Fig. 8 The same as in Fig. 6, but for changes with height

DownLoad: Full-Size Img PowerPoint

图 9 GRAPES_Meso位势高度(a)、温度(b)、纬向风(c)和经向风(d)分析场与NCEP FNL平均偏差和均方根误差

Fig. 9 The bias and root mean square error of geopotential height(a), temperature(b), u-component(c) and v-component(d) between GRAPES_Meso analysis and NCEP FNL analysis

DownLoad: Full-Size Img PowerPoint

图 10 GRAPES_Meso位势高度(a)、温度(b)、纬向风(c)和经向风(d) 12 h预报与NCEP FNL平均偏差和均方根误差

Fig. 10 The same as in Fig. 9, but for GRAPES_Meso 12-hour forecast

DownLoad: Full-Size Img PowerPoint

图 11 2016年8月1—31日全国降水预报月平均ETS评分

(a)0~6 h预报, (b)6~12 h预报, (c)12~18 h预报, (d)18~24 h预报

Fig. 11 Averaged ETS of precipitation forecast in China from 1 Aug to 31 Aug in 2016

(a)0-6 hours, (b)6-12 hours, (c)12-18 hours, (d)18-24 hours

DownLoad: Full-Size Img PowerPoint

图 12 2016年8月1—31日全国降水预报月平均降水偏差评分

(a)0~6 h预报, (b)6~12 h预报, (c)12~18 h预报, (d)18~24 h预报

Fig. 12 Averaged bias score of precipitation forecast in China from 1 Aug to 31 Aug in 2016

(a)0-6 hours, (b)6-12 hours, (c)12-18 hours, (d)18-24 hours

DownLoad: Full-Size Img PowerPoint

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