采用不同样本集合同化地面观测对一次飑线过程的影响

李少英; 张述文; 毛伏平; 李彦霖

doi:10.3878/j.issn.1006-9895.1606.15298

采用不同样本集合同化地面观测对一次飑线过程的影响

doi: 10.3878/j.issn.1006-9895.1606.15298

1.
兰州大学大气科学学院/甘肃省干旱气候变化与减灾重点实验室, 兰州 730000
2.
中国人民解放军 94923部队, 福建省武夷山 354300

基金项目:

国家重点基础研究发展计划 (973计划) 项目 2013CB430102

国家自然科学基金项目 41575098

高等学校博士学科点专项科研基金项目 20120211110019

详细信息

作者简介:
李少英, 女, 1990年出生, 博士研究生, 从事资料同化和强对流天气数值模拟的研究。E-mail:lishyi2009@lzu.edu.cn

通讯作者:
张述文, E-mail:zhangsw@lzu.edu.cn

中图分类号: P435
计量
- 文章访问数: 1528
- HTML全文浏览量: 1
- PDF下载量: 1200
- 被引次数: 0
出版历程
- 收稿日期: 2015-11-11
- 网络出版日期: 2016-06-20
- 刊出日期: 2017-03-15

Influence of Assimilating Surface Observations on a Squall Line with Different Ensembles

1.
Key Laboratory of Arid Climate Change and Reducing Disaster of Gansu Province/College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000
2.
Unit 93868, People's Liberation Army, Wuyishan, Fujian 354300

Funds:

National Basic Research Program of China 2013CB430102

National Natural Science Foundation of China 41575098

Specialized Research Fund for the Doctoral Program of Higher Education 20120211110019

摘要

摘要: 针对夏季黄淮地区一次飑线过程，利用WRF (Weather Research and Forecasting) 模式及其Hybrid ETKF-3DVAR同化系统，考察不同生成方案的样本对同化地面观测的影响。集合样本创建方式包括3类：扰动初始背景场的方案 (RCV)、使用不同的物理参数化方案 (PPMP) 以及前两者集成方案 (BLE)。基于增量场分析，同化地面观测主要调整850 hPa以下水平风和水汽混合比的空间结构，其中RCV方案侧重于改变水平风的空间分布，PPMP方案侧重于改变水汽混合比的空间结构，BLE方案兼具二者特征。同化地面观测可以间接改善6 h降水预报，其中PPMP试验的降水预报最好，尤其是对降水位置和强度的预报。对比雷达回波观测，RCV试验和BLE试验对弓状回波模拟得较好，BLE试验的模拟较多体现RCV特征。PPMP试验和RCV试验还可改变冷池的位置和强度，同时影响飑线出现和消亡时间，相对而言，PPMP试验影响更大。
- 样本生成方案 /
- 地面观测 /
- 混合同化 /
- 中尺度模式WRF /
- 飑线
Abstract: The Weather Research and Forecasting (WRF) model with the hybrid ETKF-3DVAR (ensemble transform Kalman filter-three-dimensional variational data assimilation) data assimilation system is used to investigate the impact of different ensemble generation schemes on a squall line forecast in the Huanghe-Huaihe region in the summer by assimilating the surface observations. Ensembles are created in three different ways-by using the different initial ensemble samples (RCV), by using different model physical process schemes (PPMP), and by combining the first two ensembles (BLE). Based on the increments of model states, assimilating the surface observations mainly adjusts the spatial structures of wind and water vapor mixing ratio below the level of 850 hPa; the RCV scheme mainly updates the wind distribution, the PPMP scheme updates the water vapor mixing ratio, and the BLE scheme has the characteristics of both RCV and PPMP. Assimilating surface observations can also improve 6-h precipitation forecasts, and the PPMP scheme can give a relatively better performance compared to PPMP and BLE, especially for the prediction of rainfall location and intensity. RCV and BLE schemes present a better simulation for the bow echo, and the performance with BLE is similar to that with RCV. PPMP and RCV schemes can adjust the position and intensity of the cold pool, and also influence the times of appearance and disappearance of the squall line. Generally, PPMP scheme has a greater impact on the squall line than RCV and BLE.
- Ensemble generation scheme /
- Surface data /
- Hybrid data assimilation /
- Mesoscale model WRF /
- Squall line

HTML全文

图 1 2009年6月5日08:00 500 hPa高度场 (实线，单位：dagpm) 和温度场 (虚线，单位：K)

Figure 1. Distributions of geopotential height (solid lines, units: dagpm) and temperature (dashed lines, units: K) at 500 hPa at 0800 BJT (Beijing time) 5 June 2009

下载: 全尺寸图片幻灯片

图 2 2009年6月5日雷达组合反射率因子：(a) 15:00；(b) 16:24；(c) 17:12

Figure 2. Radar composite reflectivity factors at (a) 1500 BJT, (b) 1624 BJT, and (c) 1712 BJT on 5 June 2009

下载: 全尺寸图片幻灯片

图 3 模拟区域及地面观测站点分布，空心圆表示剔除站点，黑色三角形表示被同化站点

Figure 3. The simulation domain and distribution of surface observation stations, the hollow circles denote rejected stations, the black triangles denote assimilated stations

下载: 全尺寸图片幻灯片

图 4 (a) RCV试验、(b) PPMP试验、(c) BLE试验对应的最底层的水汽混合比增量 (阴影，单位：g kg^-1) 和不小于3 m s^-1的水平风速增量 (箭头) 在D3区水平分布。黑线表示图 5中剖面位置

Figure 4. Analysis increments of water vapor mixing ratio (shaded, units: g kg^-1) in (a) RCV (the different initial ensemble samples) experiment, (b) PPMP (the different model physical process schemes) experiment, and (c) BLE (combining the first two ensembles) experiment at the lowest model level in region D3. Arrows indicate horizontal wind speed increments larger than 3 m s^-1, the black line represents the location of cross section in Fig. 5

下载: 全尺寸图片幻灯片

图 5 (a) RCV试验、(b) PPMP试验、(c) BLE试验导出的水汽混合比 (阴影，单位：g kg^-1) 和水平风速 (等值线，间隔：2 m s^-1) 分析增量沿图 4黑线的剖面

Figure 5. Cross sections (along black lines in Fig. 4) of analysis increments of water vapor mixing ratio (shaded, units: g kg^-1) and horizontal wind speed (contoured interval 2 m s^-1) in (a) RCV experiment, (b) PPMP experiment, and (c) BLE experiment

下载: 全尺寸图片幻灯片

图 6 (a) RCV试验、(b) PPMP试验、(c) BLE试验同化前模式第1层水汽混合比样本的离散度 (单位：g kg^-1) 在D3区分布

Figure 6. Ensemble spreads for water vapor mixing ratio (units: g kg^-1) at the lowest model level in region D3 before data assimilation: (a) RCV experiment; (b) PPMP experiment; (c) BLE experiment

下载: 全尺寸图片幻灯片

图 7 4组试验 (CTRL试验、RCV试验、PPMP试验、BLE试验) 的SAL评分

Figure 7. SAL (structure, amplitude, location) values in CTRL, RCV, PPMP, and BLE experiments

下载: 全尺寸图片幻灯片

图 8 6月5日6 h (14:00至20:00) 累计降水量 (单位：mm) 比较：(a) CTRL试验；(b) RCV试验；(c) PPMP试验；(d) BLE试验；(e) 实况

Figure 8. 6-h (1400 BJT to 2000 BJT) accumulated precipitation on 5 June from (a) CTRL experiment, (b) RCV experiment, (c) PPMP experiment, (d) BLE experiment, and (e) observations

下载: 全尺寸图片幻灯片

图 9 2009年6月5日15:30(a) 观测以及 (b) CTRL试验、(c) RCV试验、(d) PPMP试验、(e) BLE试验预报的组合反射率因子

Figure 9. (a) The observed radar composite reflectivity factor and the reflectivity factor forecasted by (b) CTRL experiment, (c) RCV experiment, (d) PPMP experiment, (e) BLE experiment at 1530 BJT 5 June 2009

下载: 全尺寸图片幻灯片

图 10 同图 9，但为19:30

Figure 10. As in Fig. 9, but for 1930 BJT 5 June 2009

下载: 全尺寸图片幻灯片

图 11 6月5日18:00相当位温 (等值线，间隔2 K) 和水平风场 (箭头，单位：m s^-1) 在D3区最底层的水平分布：(a) CTRL试验；(b) RCV试验；(c) PPMP试验。黑线为图 12中剖面位置

Figure 11. Equivalent potential temperature θ (contours, interval 2 K) and horizontal wind (arrows, units: m s^-1) at 1800 BJT on 5 June from (a) CTRL experiment, (b) RCV experiment, and (c) PPMP experiment at the lowest level in region D3. The black lines represent the location of cross section in Fig. 12

下载: 全尺寸图片幻灯片

图 12 6月5日17:30相当位温 (等值线，间隔3 K) 和垂直速度w (阴影，单位：m s^-1) 沿图 11中黑线的剖面：(a) CTRL试验；(b) RCV试验；(c) PPMP试验

Figure 12. Cross sections (along black lines in Fig. 11) of equivalent potential temperature θ (contours, interval 3 K) and w (shaded, units: m s^-1) at 1730 BJT 5 June: (a) CTRL experiment; (b) RCV experiment; (c) PPMP experiment

下载: 全尺寸图片幻灯片

表 1 不同样本集合间的比较

Table 1. Comparison among different ensembles

同化试验名称	样本生成方案	6月5日14:00是否同化地面观测	集合样本数
RCV试验	Random_CV扰动方法	是	40
PPMP试验	不同物理参数化方案的组合	是	40
BLE试验	RCV试验成员+PPMP试验成员	是	80

下载: 导出CSV

表 2 6月5日18:00 3组试验在江苏省南部飑线中心处的垂直风切变

Table 2. Vertical wind shears near the squall line center in southern Jiangsu Province in CTRL, RCV, and PPMP experiments at 1800 BJT 5 June

气压范围/hPa	垂直风切变/s^-1
气压范围/hPa	CTRL试验	RCV试验	PPMP试验
地面~900	0.023	0.021	0.034
850~700	0.008	0.003	0.003
600~350	0.008	0.008	0.006

下载: 导出CSV

参考文献(45)

[1]	Ancell B C, Mass C F, Cook K, et al. 2014. Comparison of surface wind and temperature analyses from an ensemble Kalman filter and the NWS real-time mesoscale analysis system[J]. Wea. Forecasting, 29 (4):1058-1075, doi: 10.1175/WAF-D-13-00139.1.
[2]	Barker D M, Huang W, Guo Y R, et al. 2004. A three-dimensional variational data assimilation system for MM5:Implementation and initial results[J]. Mon. Wea. Rev., 132 (4):897-914, doi:10.1175/1520-0493(2004)132<0897:ATVDAS>2.0.CO; 2.
[3]	Barker D M. 2005. Southern high-latitude ensemble data assimilation in the Antarctic mesoscale prediction system[J]. Mon. Wea. Rev., 133 (12):3431-3449, doi: 10.1175/MWR3042.1.
[4]	Bryan G H, Parker M D. 2010. Observations of a squall line and its near environment using high-frequency rawinsonde launches during VORTEX2[J]. Mon. Wea. Rev., 138 (11):4076-4097, doi: 10.1175/2010MWR3359.1.
[5]	曹倩, 张述文, 曹帮军, 等. 2016.超强不稳定和弱切变环境下一次飑线过程的雷达资料同化与分析[J].热带气象学报, 32 (5):645-655. doi: 10.16032/j.issn.1004-4965.2016.05.00 Cao Qian, Zhang Shuwen, Cao Bangjun, et al. 2016. The radar data assimilation and analysis of a squall line under ultrastrong instability and weak vertical wind shear[J]. J. Tropical Meteor. (in Chinese), 32 (5):645-655, doi: 10.16032/j.issn.1004-4965.2016.05.00.
[6]	戴建华, 陶岚, 丁杨, 等. 2012.一次罕见飑前强降雹超级单体风暴特征分析[J].气象学报, 70 (4):609-627. doi: 10.11676/qxxb2012.050 Dai Jianhua, Tao Lan, Ding Yang, et al. 2012. Case analysis of a large hail-producing severe supercell ahead of a squall line[J]. Acta Meteor. Sinica (in Chinese), 70 (4):609-627, doi: 10.11676/qxxb2012.050.
[7]	Dong J L, Xue M, Droegemeier K. 2011. The analysis and impact of simulated high-resolution surface observations in addition to radar data for convective storms with an ensemble Kalman filter[J]. Meteor. Atmos. Phys., 112 (1-2):41-61, doi: 10.1007/s00703-011-0130-3.
[8]	Fujita T, Stensrud D J, Dowell D C. 2007. Surface data assimilation using an ensemble Kalman filter approach with initial condition and model physics uncertainties[J]. Mon. Wea. Rev., 135 (5):1846-1868, doi: 10.1175/MWR3391.1.
[9]	Hacker J P, Snyder C. 2005. Ensemble Kalman filter assimilation of fixed screen-height observations in a parameterized PBL[J]. Mon. Wea. Rev., 133 (11):3260-3275, doi: 10.1175/MWR3022.1.
[10]	Ha S Y, Snyder C. 2014. Influence of surface observations in mesoscale data assimilation using an ensemble Kalman filter[J]. Mon. Wea. Rev., 142 (4):1489-1508, doi: 10.1175/MWR-D-13-00108.1.
[11]	Houtekamer P L, Derome J. 1995. Methods for ensemble prediction[J]. Mon. Wea. Rev., 123 (7):2181-2196, doi:10.1175/1520-0493(1995)123<2181:MFEP>2.0.CO; 2.
[12]	兰伟仁, 朱江, Xue Ming, 等. 2010a.风暴尺度天气下利用集合卡尔曼滤波模拟多普勒雷达资料同化试验Ⅰ:不考虑模式误差的情形[J].大气科学, 34 (3):640-652. doi: 10.3878/j.issn.1006-9895.2010.03.15 Lan Weiren, Zhu Jiang, Xue Ming, et al. 2010a. Storm-scale ensemble Kalman filter data assimilation experiments using simulated Doppler radar data. Part Ⅰ:Perfect model tests[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 34 (3):640-652, doi: 10.3878/j.issn.1006-9895.2010.03.15.
[13]	兰伟仁, 朱江, Xue Ming, 等. 2010b.风暴尺度天气下利用集合卡尔曼滤波模拟多普勒雷达资料同化试验Ⅱ:考虑模式误差的情形[J].大气科学, 34 (4):737-753. doi: 10.3878/j.issn.1006-9895.2010.04.07 Lan Weiren, Zhu Jiang, Xue Ming, et al. 2010b. Storm-scale ensemble Kalman filter data assimilation experiments using simulated Doppler radar data. Part Ⅱ:Imperfect model tests[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 34 (4):737-753, doi: 10.3878/j.issn.1006-9895.2010.04.07.
[14]	李娜, 冉令坤, 高守亭. 2013.华东地区一次飑线过程的数值模拟与诊断分析[J].大气科学, 37 (3):595-608. doi: 10.3878/j.issn.1006-9895.2012.12007 Li Na, Ran Lingkun, Gao Shouting. 2013. Numerical simulation and diagnosis study of a squall line in eastern China[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 37 (3):595-608, doi: 10.3878/j.issn.1006-9895.2012.12007.
[15]	Li Y Z, Wang X G, Xue M. 2012. Assimilation of radar radial velocity data with the WRF hybrid ensemble-3DVAR system for the prediction of hurricane Ike (2008)[J]. Mon. Wea. Rev., 140 (11):3507-3524, doi: 10.1175/MWR-D-12-00043.1.
[16]	梁建宇, 孙建华. 2012. 2009年6月一次飑线过程灾害性大风的形成机制[J].大气科学, 36 (2):316-336. doi: 10.3878/j.issn.1006-9895.2011.11017 Liang Jianyu, Sun Jianhua. 2012. The formation mechanism of damaging surface wind during the squall line in June 2009[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 36 (2):316-336, doi: 10.3878/j.issn.1006-9895.2011.11017.
[17]	Meng Z Y, Zhang F Q. 2008. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part Ⅲ:Comparison with 3DVAR in a real-data case study[J]. Mon. Wea. Rev., 136 (2):522-540, doi: 10.1175/2007MWR2106.1.
[18]	Meng Z Y, Yan D C, Zhang Y J. 2013. General features of squall lines in East China[J]. Mon. Wea. Rev., 141 (5):1629-1647, doi: 10.1175/MWR-D-12-00208.1.
[19]	Moller A R, Doswell III C A, Foster M P, et al. 1994. The operational recognition of supercell thunderstorm environments and storm structures[J]. Wea. Forecasting, 9 (3):327-347, doi:10.1175/1520-0434(1994) 009<0327:TOROST>2.0.CO; 2.
[20]	Molteni F, Buizza R, Palmer T N, et al. 1996. The ECMWF ensemble prediction system:Methodology and validation[J]. Quart. J. Roy. Meteor. Soc., 122 (529):73-119, doi: 10.1002/qj.49712252905.
[21]	潘玉洁, 赵坤, 潘益农. 2008.一次强飑线内强降水超级单体风暴的单多普勒雷达分析[J].气象学报, 66 (4):621-636. doi: 10.11676/qxxb2008.059 Pan Yujie, Zhao Kun, Pan Yinong. 2008. Single-Doppler radar observation of a heavy precipitation supercell on a severe squall line[J]. Acta Meteor. Sinica (in Chinese), 66 (4):621-636, doi: 10.11676/qxxb2008.059.
[22]	Parker M D, Johnson R H. 2004a. Simulated convective lines with leading precipitation. Part Ⅰ:Governing dynamics[J]. J. Atmos. Sci., 61 (14):1637-1655, doi:10.1175/1520-0469(2004)061<1637:SCLWLP>2.0.CO; 2.
[23]	Parker M D, Johnson R H. 2004b. Simulated convective lines with leading precipitation. Part Ⅱ:Evolution and maintenance[J]. J. Atmos. Sci., 61 (14):1656-1673, doi:10.1175/1520-0469(2004)061<1656:SCLWLP>2.0. CO; 2.
[24]	秦琰琰, 龚建东, 李泽椿, 等. 2014.集合方根滤波同化多普勒雷达资料在一次飑线过程中的应用研究[J].气象学报, 72 (1):133-151. doi: 10.11676/qxxb2014.012 Qin Yanyan, Gong Jiandong, Li Zechun, et al. 2014. Assimilation of Doppler radar observations with an ensemble square root filter:A squall line case study[J]. Acta Meteor. Sinica (in Chinese), 72 (1):133-151, doi: 10.11676/qxxb2014.012.
[25]	Romine G S, Schwartz C S, Berner J, et al. 2014. Representing forecast error in a convection-permitting ensemble system[J]. Mon. Wea. Rev., 142 (12):4519-4541, doi: 10.1175/MWR-D-14-00100.1.
[26]	Schwartz C S, Liu Z Q. 2014. Convection-permitting forecasts initialized with continuously cycling limited-area 3DVAR, ensemble Kalman filter, and "hybrid" variational-ensemble data assimilation systems[J]. Mon. Wea. Rev., 142 (2):716-738, doi: 10.1175/MWR-D-13-00100.1.
[27]	Schwartz C S, Romine G S, Smith K R, et al. 2014. Characterizing and optimizing precipitation forecasts from a convection-permitting ensemble initialized by a mesoscale ensemble Kalman filter[J]. Wea. Forecasting, 29 (6):1295-1318, doi: 10.1175/WAF-D-13-00145.1.
[28]	沈杭锋, 翟国庆, 朱补全, 等. 2010.浙江沿海中尺度辐合线对飑线发展影响的数值试验[J].大气科学, 34 (6):1127-1140. doi: 10.3878/j.issn.1006-9895.2010.06.08 Shen Hangfeng, Zhai Guoqing, Zhu Buquan, et al. 2010. A model study of impact of coastal mesoscale convergence line on development of squall line over Zhejiang Province[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 34 (6):1127-1140, doi: 10.3878/j.issn.1006-9895.2010.06.08.
[29]	Sobash R A, Stensrud D J. 2015. Assimilating surface mesonet observations with the EnKF to improve ensemble forecasts of convection initiation on 29 May 2012[J]. Mon. Wea. Rev., 143 (9):3700-3725, doi: 10.1175/MWR-D-14-00126.1.
[30]	孙虎林, 罗亚丽, 张人禾, 等. 2011. 2009年6月3~4日黄淮地区强飑线成熟阶段特征分析[J].大气科学, 35 (1):105-120. doi: 10.3878/j.issn.1006-9895.2011.01.09 Sun Hulin, Luo Yali, Zhang Renhe, et al. 2011. Analysis on the mature-stage features of the severe squall line occurring over the Yellow River and Huaihe River basins during 3-4 June 2009[J]. Chinese Journal of Atmospheric Sciences (in Chinese), 35 (1):105-120, doi:10.3878/j.issn.1006-9895. 2011.01.09.
[31]	Sun J Z, Wang H L. 2013. Radar data assimilation with WRF 4D-Var. Part Ⅱ:Comparison with 3D-Var for a squall line over the U.S. Great Plains[J]. Mon. Wea. Rev., 141 (7):2245-2264, doi: 10.1175/MWR-D-12-00169.1.
[32]	Thorpe A J, Miller M J, Moncrieff M W. 1982. Two-dimensional convection in non-constant shear:A model of mid-latitude squall lines[J]. Quart. J. Roy. Meteor. Soc., 108 (458):739-762, doi: 10.1002/qj.49710845802.
[33]	Toth Z, Kalnay E. 1993. Ensemble forecasting at NMC:The generation of perturbations[J]. Bull. Amer. Meteor. Soc., 74 (12):2317-2330, doi:10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO; 2.
[34]	Toth Z, Kalnay E. 1997. Ensemble forecasting at NCEP and the breeding method[J]. Mon. Wea. Rev., 125 (12):3297-3319, doi:10.1175/1520-0493(1997)125<3297:EFANAT>2.0.CO; 2.
[35]	Wang X G, Hamill T M, Whitaker J S, et al. 2007. A comparison of hybrid ensemble transform Kalman filter-optimum interpolation and ensemble square root filter analysis schemes[J]. Mon. Wea. Rev., 135 (3):1055-1076, doi: 10.1175/MWR3307.1.
[36]	Wang X G, Barker D M, Snyder C, et al. 2008a. A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part Ⅰ:Observing system simulation experiment[J]. Mon. Wea. Rev., 136 (12):5116-5131, doi: 10.1175/2008MWR2444.1.
[37]	Wang X G, Barker D M, Snyder C, et al. 2008b. A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part Ⅱ:Real observation experiments[J]. Mon. Wea. Rev., 136 (12):5132-5147, doi: 10.1175/2008MWR2445.1.
[38]	Wernli H, Paulat M, Hagen M, et al. 2008. SAL-A novel quality measure for the verification of quantitative precipitation forecasts[J]. Mon. Wea. Rev., 136 (11):4470-4487, doi: 10.1175/2008MWR2415.1.
[39]	袁招洪. 2015.不同分辨率和微物理方案对飑线阵风锋模拟的影响[J].气象学报, 73 (4):648-666. doi: 10.11676/qxxb2015.049 Yuan Zhaohong. 2015. Study of the influence of the different horizontal resolutions and microphysical setups on the idealized simulation of a squall line[J]. Acta Meteor. Sinica (in Chinese), 73 (4):648-666, doi: 10.11676/qxxb2015.049.
[40]	Zhang F M, Yang Y, Wang C H. 2015. The effects of assimilating conventional and ATOVS data on forecasted near-surface wind with WRF-3DVAR[J]. Mon. Wea. Rev., 143 (1):153-164, doi: 10.1175/MWR-D-14-00038.1.
[41]	Zhang H L, Pu Z X. 2014. Influence of assimilating surface observations on numerical prediction of landfalls of hurricane Katrina (2005) with an ensemble Kalman filter[J]. Mon. Wea. Rev., 142 (8):2915-2934, doi: 10.1175/MWR-D-14-00014.1.
[42]	Zhang S W, Li D Q, Qiu C J. 2011. A multimodel ensemble-based Kalman filter for the retrieval of soil moisture profiles[J]. Adv. Atmos. Sci., 28 (1):195-206, doi: 10.1007/s00376-010-9200-6.
[43]	Zhang S W, Liu Y H, Zhang W D. 2013. Ensemble square root filter assimilation of near-surface soil moisture and reference-level observations into a coupled land surface-boundary layer model[J]. Acta Meteor. Sinica, 27 (4):541-555, doi: 10.1007/s13351-013-0402-6.
[44]	Zheng F, Zhu J. 2010. A multivariate empirical orthogonal function-based scheme for the balanced initial ensemble generation of an ensemble Kalman filter[J]. Atmos. Oceanic Sci. Lett., 3 (3):165-169. http://www.cnki.com.cn/Article/CJFDTOTAL-AOSL201003009.htm
[45]	郑媛媛, 张雪晨, 朱红芳, 等. 2014.东北冷涡对江淮飑线生成的影响研究[J].高原气象, 33 (1):261-269. doi: 10.7522/j.issn.1000-0534.2013.00005 Zheng Yuanyuan, Zhang Xuechen, Zhu Hongfang, et al. 2014. Study of squall line genesis with Northeast cold vortex[J]. Plateau Meteor. (in Chinese), 33 (1):261-269, doi: 10.7522/j.issn.1000-0534.2013.00005.