Please wait a minute...
img

Wechat

Adv search
JOURNAL OF DESERT RESEARCH  2013, Vol. 33 Issue (4): 1150-1159    DOI: 10.7522/j.issn.1000-694X.2013.00163
Weather and Climate     
An Application of Assimilated  Dust Concentration Data and AMSU Satellite Radiance Data in GRAPES_SDM Model
DUAN Hai-xia1, LI Yao-hui1, PU Zhao-xia1,2, ZHAO Jian-hua1, ZHANG Liang1
1.Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province/Key Open Laboratory of Climatic Change and Disaster Reduction of CMA, Institute of Arid Meteorology, China Meteorological Administration, Lanzhou 730020, China;
2.Department of Atmospheric Sciences, University of Utah, Salt Lake City 84112, Utah, USA
Download:  PDF (8655KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

Based on the GRAPES_SDM model and GRAPES_3DVAR data assimilation system, four experimental schemes are designed, they are CTRL experiment without data assimilation, noPM experiment that only assimilate sounding data, PM experiment that assimilate PM10 data and sounding data, NOAA experiment that assimilate sounding data and AMSU radiance data, then a large range sandstorm case from 28 April to 30 April in 2011 in northern China is analyzed. The model can reflect the development and evolvement of sand-dust weather system when only assimilating sounding data, but the simulation effect of sand-dust range and intensity have not obvious amelioration. The dust range and intensity can be simulated better by assimilating sounding and PM10 data. When assimilating AMSU radiance data, the model can assimilate the 500 hPa circulation pattern, 200 hPa high-level jet and dust range well, but intensify the dust strength a bit too much. It not only shows that the assimilation of PM10 and ASMU radiance data can improve the simulation effect of sand-dust process, but also lay a certain foundation for sand-dust weather forecast using the two data.

Key words:  sandstorm      GRAPES_SDM model      PM10 dust concentration data      AMSU radiance data     
Received:  16 April 2012      Published:  26 June 2012
ZTFLH:  P456  

Cite this article: 

DUAN Hai-xia1, LI Yao-hui1, PU Zhao-xia1,2, ZHAO Jian-hua1, ZHANG Liang1. An Application of Assimilated  Dust Concentration Data and AMSU Satellite Radiance Data in GRAPES_SDM Model. JOURNAL OF DESERT RESEARCH, 2013, 33(4): 1150-1159.

URL: 

http://www.desert.ac.cn/EN/10.7522/j.issn.1000-694X.2013.00163     OR     http://www.desert.ac.cn/EN/Y2013/V33/I4/1150

[1]尹晓慧.我国沙尘天气研究的最新进展与展望[J].中国沙漠,2009,29(4):728-733.

[2]吕艳丽,刘连友,屈志强,等.中国北方典型沙尘天气特征研究[J].中国沙漠,2012,32(2):447-453.

[3]沈建国,李嘉鹏,牛生杰,等.沙尘天气中气溶胶光学特性的时空分布特征[J].中国沙漠,2007,27(3):495-501.

[4]李彰俊,孙照渤,姜学恭.蒙古气旋天气过程中的沙尘传输特征[J].中国沙漠,2008,28(5):927-930.

[5]陶健红,黄玉霞,陆登荣.河西走廊沙尘活动对兰州PM10浓度的影响及其评估[J].中国沙漠,2007,27(4):672-676.

[6]贾晓鹏,陈开锋.沙尘事件对兰州河谷大气环境PM10的影响[J].中国沙漠,2011,31(6):1573-1578.

[7]周悦,牛生杰,王存忠,等.半干旱区PM10质量浓度时空分布特征研究[J].中国沙漠,2011,31(3):741-749.

[8]刘新春,钟玉婷,何清,等.塔克拉玛干沙漠腹地及周边地区PM10时空变化特征及影响因素分析[J].中国沙漠,2011,31(2):323-330.

[9]黄艇,宋煜,胡文东,等.大连地区一次沙尘过程的激光雷达观测研究[J].中国沙漠,2010,30(4):983-988.

[10]刘春涛,程麟生.黑风暴的沙尘形成与输送参数化及中尺度数值试验[J].气象学报,1997,55(6):726-738.

[11]Shao Y.A model for mineral dust emission[J].Journal of Geophysical Research,2001,106:20239-20254.

[12]Gong S L,Zhang X Y,Zhao T L.Model simulation and validation[J].Journal of Geophysical Research,2003,108(9):4262.

[13]纪飞,秦瑜.东亚沙尘暴的数值模拟(Ⅰ)模式建立[J].北京大学学报(自然科学版),1996,32(3):384-392.

[14]Barnum B H,Winstead N S,Wesely J,et al.Forecasting dust storms using the CARMA-dust model and MM5 weather data[J]. Environmental Modeling & Software,2004,19:129-140.

[15]巢纪平,刘飞.沙尘暴垂直输运的两相流理论Ⅰ:气块模式[J].气象学报,2009,67(1):1-10.

[16]刘飞,巢纪平.沙尘暴垂直输运的两相流理论Ⅱ:气柱挟卷模式[J].气象学报,2009,67(1):11-19.

[17]庄照荣,薛纪善.云迹风资料的三维变分同化及对台风预报的影响试验[J].热带气象学报,2004,20(3):225-236.

[18]丁伟钰,万齐林,端义宏.TRMM降水率资料的三维变分同化及其对“杜鹃”(0313)台风预报的改进[J].大气科学,2005,29(4):600-608.

[19]王叶红,赵玉春,崔春光.多普勒雷达估算降水和反演风在不同初值方案下对降水预报影响的数值研究[J].气象学报,2006,64(4):485-499.

[20]Liu H,Zou X.The impact of NORPEX targeted dropsondes on the 2-3 day forecasts of a landfalling Pacific winter storm using NCEP 3D-Var and 4D-Var systems[J].Monthly Weather Review,2001,129:1987-2004.

[21]Pu Z,Li X,Sun J.Impact of airborne Doppler radar data assimilation on the numerical simulation of intensity changes of Hurricane Dennis near a landfall[J].Journal of the Atmospheric Sciences,2009,66:3351-3365.

[22]Zhang L,Pu Z.Four-dimensional assimilation of multi-time wind profiles over a single station and numerical simulation of a mesoscale convective system observed during IHOP_2002[J].Monthly Weather Review,2011,139: 3369-3388.

[23]齐琳琳,孙建华,张小玲,等.ATOVS资料在长江流域一次暴雨过程模拟中的应用[J].大气科学,2005,29(5):780-794.

[24]潘宁,董超华,张文建.ATOVS辐射率资料的直接变分同化试验研究[J].气象学报,2003,61(2):226-236.

[25]张利红,沈桐立,王洪利.AMSU资料变分同化及在暴雨数值模拟中的应用研究[J].高原气象,2007,26(5):1004-1012.

[26]李耀辉,赵建华,薛纪善,等.基于GRAPES的西北地区沙尘暴数值预报模式及其应用研究[J].地球科学进展,2005,20(9):999-1011.

[27]赵建华,张强,李耀辉,等.“7.17”西北夏季沙尘暴数值模拟[J].中国沙漠,2009,29(6):1221-1228.

[28]张华,薛纪善,庄世宇,等.GRAPES三维变分同化系统的理想试验[J].气象学报,2004,62(1):31-41.

[29]庄世宇,薛纪善,朱国富,等.GRAPES全球三维变分同化系统—基本设计方案与理想实验[J].大气科学,2005,29(6):872-884.

[30]薛纪善,庄世宇,朱国富,等.GRAPES新一代全球/区域变分同化系统研究[J].科学通报,2008,53(20):2408-2417.

[31]马旭林,庄照荣,薛纪善,等.GRAPES非静力数值预报模式的三维变分资料同化系统的发展[J].气象学报,2009,67(1):50-60.

No Suggested Reading articles found!