基于WACCM+DART的临近空间SABER和MLS温度观测资料同化试验

敬文琪; 崔园园; 王业桂; 蒋辉明; 蔡其发; 兰伟仁

doi:10.11728/cjss2020.02.227

基于WACCM+DART的临近空间SABER和MLS温度观测资料同化试验

doi: 10.11728/cjss2020.02.227

1. 94758部队气象台宁德 355103;
2. 河北省气象台石家庄 050021;
3. 中国科学院大气物理研究所北京 100029

基金项目:

国家自然科学基金项目资助（41375105）

详细信息

作者简介:
敬文琪,E-mail:wenqijing@foxmail.com

通讯作者:
王业桂,E-mail:wangyg0110@163.com

中图分类号: P351
计量
- 文章访问数: 1071
- HTML全文浏览量: 66
- PDF下载量: 89
- 被引次数: 0
出版历程
- 收稿日期: 2018-12-17
- 修回日期: 2019-12-21
- 刊出日期: 2020-03-15

Assimilation of Near Space Temperature Data from SABER and MLS Observations into the Whole Atmosphere Community Climate Model and Data Assimilation Research Test-bed

1 Meteorological Observatory of 94758 Troops, Ningde 355103;
2 Hebei Meteorological Observatory, Shijiazhuang 050021;
3 Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

摘要

摘要: 基于WACCM+DART（Whole Atmosphere Community Climate Model，Data Assimilation Research Test-Bed）临近空间资料同化预报系统，以2016年2月的一次平流层爆发性增温（SSW）事件为例，开展了临近空间SABER（Sounding of the Atmosphere using Broadband Emission Radiometry）和MLS（Microwave Limb Sounder）温度观测资料集合滤波同化试验.结果表明：同化SABER和MLS温度观测资料可显著降低WACCM模式在中间层和平流层中上部（0.001～10hPa）大气温度场的预报误差，改善CR试验在SSW发生时中间层变冷现象偏强、纬向风场首次发生反转的层次偏低以及增温恢复阶段0.1～10hPa的东风层提前消退、纬向风速偏大、平流层顶位置偏高等现象.基于ERA5（The Fifth Generation of ECMWF Reanalyses）再分析资料的检验表明：同化SABER和MLS温度资料明显有利于减小北半球高纬度地区（60°-90°N）平流层中上层和下中间层（0.1～14hPa）纬向风场以及平流层和中间层中下层（0.01～100hPa）温度场的分析误差；同化低层大气观测也有利于减小0.1～14hPa纬向风场和0.01～100hPa温度场的分析误差，但是不如同化SABER和MLS温度资料对临近空间纬向风场和温度场分析误差的改善效果显著.
- WACCM模式 /
- DART资料同化系统 /
- SABER /
- MLS /
- 温度
Abstract: This study performs SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) and MLS (Microwave Limb Sounder) temperature data assimilation experiments to simulate a SSW (Stratospheric Sudden Warming) process occurred in February 2016, based on WACCM+DART (Whole Atmosphere Community Climate Model, Data Assimilation Research Test-bed). The following main conclusions are obtained. First, assimilating SABER and MLS temperature observations significantly reduces WACCM's forecast error of temperature fields in mesosphere and middle-upper stratosphere (0.001～10hPa), and effectively improves control experiment's several discrepancies with observations and reanalysis, such as colder mesosphere during SSW, lower layer height that zonal wind direction firstly changes when SSW occurs, east zonal wind layers in 0.1～10hPa prematurely vanishing, stronger zonal wind and higher stratopause height during SSW recovery phase. The verification based on ERA5 reanalysis suggests that assimilating SABER and MLS temperature observations is in favor of reducing analysis error of zonal wind in low mesosphere and middle-upper stratosphere (0.1～14hPa) and temperature in stratosphere and middle-lower mesosphere (0.01～100hPa) above high-latitude areas (60°-90°N)in the northern hemisphere. In addition, assimilating low atmospheric observations is also beneficial for reducing analysis error of zonal wind in 0.1～14hPa and temperature in 0.01～100hPa, but this reduction effect is not as significant as that of assimilating SABER and MLS temperature observations.
- WACCM model /
- DART data assimilation system /
- SABER /
- MLS /
- Temperature

HTML全文

参考文献(51)

[1]	LÜ Daren, CHEN Zeyu, GUO Xia, et al. Recent progress in near space atmospheric environment study[J]. Adv. Mech., 2009, 39(6):674-682(吕达仁, 陈泽宇, 郭霞, 等. 临近空间大气环境研究现状[J]. 力学进展, 2009, 39(6):674-682)
[2]	XIAO Cunying. Researches on the Dynamics of the Atmosphere in the Near Space[D]. Beijing:Center for Space Science and Applied Research, Chinese Academy of Sciences, 2009(肖存英. 临近空间大气动力学特性研究[D]. 北京:中国科学院空间科学与应用研究中心, 2009)
[3]	CHEN Fenggui, CHEN Guangming, LIU Kehua. Analysis of near apace environment and its effect[J]. Equip. Environ. Eng., 2013, 10(4):71-75(陈凤贵, 陈光明, 刘克华. 临近空间环境及其影响分析[J]. 装备环境工程, 2013, 10(4):71-75)
[4]	ZHANG J, TIAN W, CHIPPERFIELD M P, et al. Persistent shift of the Arctic polar vortex towards the Eurasian continent in recent decades[J]. Nat. Clim. Change, 2016, 6(12):1094-1099
[5]	TIAN Wenshou, TIAN Hongying, SHANG Lin, et al. Advances in interactions between tropical stratosphere and troposphere[J]. J. Trop. Meteor., 2011, 27(5):765-774(田文寿, 田红瑛, 商林, 等. 热带平流层与对流层之间相互作用的研究进展[J]. 热带气象学报, 2011, 27(5):765-774)
[6]	GERBER E P, BALDWIN M P, AKIYOSHI H, et al. Stratosphere-troposphere coupling and annular mode variability in chemistry-climate models[J]. J. Geophys. Res.:Atmos., 2010, 115(D3).DOI: 10.1029/2009JD013770
[7]	XIE F, TIAN W, CHIPPERFIELD M P. Radiative effect of ozone change on stratosphere-troposphere exchange[J]. J. Geophys. Res.:Atmos., 2008, 113(D7).DOI: 10.1029/2008JD009829
[8]	XIE F, LI J, TIAN W, et al. A connection from Arctic stratospheric ozone to El Niño-Southern Oscillation[J]. Environ. Res. Lett., 2016, 11(12):124026
[9]	LIU H L, MCINERNEY J M, SANTOS S, et al. Gravity waves simulated by high-resolution Whole Atmosphere Community Climate Model[J]. Geophys. Res. Lett., 2014, 41(24):9106-9112
[10]	HERSBACH H, DEE D. ERA5 reanalysis in production[J]. ECMWF Newsletter, 2016, 7:147
[11]	JIN H, MIYOSHI Y, PANCHEVA D, et al. Response of migrating tides to the stratospheric sudden warming in 2009 and their effects on the ionosphere studied by a whole atmosphere-ionosphere model GAIA with COSMIC and TIMED/SABER observations[J]. J. Geophys. Res.:Space Phys., 2012, 117(A10). DOI: 10.1029/2012JA017650
[12]	SCHMIDT H, BRASSEUR G P, CHARRON M, et al. The HAMMONIA chemistry climate model:sensitivity of the mesopause region to the 11-year solar cycle and CO₂ doubling[J]. J. Clim., 2006, 19(16):3903-3931
[13]	AKMAEV R A, FULLER-ROWELL T J, WU F, et al. Tidal variability in the lower thermosphere:comparison of Whole Atmosphere Model (WAM) simulations with observations from TIMED[J]. Geophys. Res. Lett., 2008, 35(3).DOI: 10.1029/2007GL032584
[14]	GARCIA R R, MARSH D R, KINNISON D E, et al. Simulation of secular trends in the middle atmosphere, 1950-2003[J]. J. Geophys. Res.:Atmos., 2007, 112(D9).DOI: 10.1029/2006JD007485
[15]	RICHTER J H, SASSI F, GARCIA R R. Toward a physically based gravity wave source parameterization in a general circulation model[J]. J. Geophys. Res.:Atmos, 2010, 67(1):136-156
[16]	ECKERMANN S D, MCCORMACK J P, COY L, et al. NOGAPS-ALPHA:a prototype high-altitude global NWP model[R]. Washington DC:Naval Research Lab, 2004
[17]	HU Xiong, GONG Jiancun, YANG Junfeng, et al. A study of near-space atmospheric prediction methods[R]//Proceedings of the 3rd China High Resolution Earth Observation. Beijing, 2014
[18]	XIAO Cunying, HU Xiong, YANG Junfeng, et al. Near-space Aura/MLS Satellite Data Assimilation Technology and its Application in Numerical Forecast[C]//Proceedings of the 4th China High Resolution Earth Observation. Wuhan:Wuhan University, 2017
[19]	XU J, LIU H L, YUAN W, et al. Mesopause structure from Thermosphere, Ionosphere, Mesosphere, Energetics, and Dynamics (TIMED)/Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) observations[J]. J. Geophys. Res.:Atmos., 2007, 112(D9).DOI: 10.1029/2006JD007711
[20]	LIVESEY N J. Earth Observing System (EOS) Aura Microwave Limb Sounder (MLS) Version 4.2 x Level 2 Data Quality and Description Document, 91109-8099, Version 4.2 x[R]. Pasadena:Jet Propulsion Lab, 2015
[21]	FISCHER H, BIRK M, BLOM C, et al. MIPAS:an instrument for atmospheric and climate research[J]. Atmos. Chem. Phys., 2008, 8(8):2151-2188
[22]	TANG Lei, JIANG Shan, LI Zimu, et al. Performance improvement of Rayleigh wind lidar and wind field observation in middle and upper atmosphere[J]. Chin. J. Lasers, 2016, 43(7):263-272(唐磊, 蒋杉, 李梓霂, 等. 瑞利测风激光雷达系统性能改进与中高层大气风场观测[J]. 中国激光, 2016, 43(7):263-272)
[23]	YUAN Wei, XU JiYao, MA RuiPing, et al. First observation of mesospheric winds by a Fabry-Perot interferometer in China[J]. Chin. Sci. Bull., 2010, 55(35):3378-3383(袁韡, 徐寄遥, 马瑞平, 等. 我国光学干涉仪对中高层大气风场的首次观测[J]. 科学通报, 2010, 55(35):3378-3383)
[24]	LU D, PAN W, WANG Y. Atmospheric profiling synthetic observation system in Tibet[J]. Adv. Atmos. Sci., 2018, 1531(3):244-247
[25]	ANDERSON J, HOAR T, RAEDER K, et al. The data assimilation research test bed:a community facility[J]. Bull. Am. Meteor. Soc., 2009, 90(9):1283-1296
[26]	XU X, MANSON A H, MEEK C E, et al. Mesospheric wind semidiurnal tides within the Canadian middle atmosphere model data assimilation system[J]. J. Geophys. Res.:Atmos., 2011, 116(D17).DOI: 10.1029/2011JD015966
[27]	PEDATELLA N M, RAEDER K, ANDERSON J L, et al. Application of data assimilation in the Whole Atmosphere Community Climate Model to the study of day-to-day variability in the middle and upper atmosphere[J]. Geophys. Res. Lett., 2013, 40(16):4469-4474
[28]	PEDATELLA N M, RAEDER K, ANDERSON J L, et al. Ensemble data assimilation in the whole atmosphere community climate model[J]. J. Geophys. Res., 2014, 119(16):9793-9809
[29]	ALLEN D R, COY L, ECKERMANN S D, et al. NOGAPS-ALPHA simulations of the 2002 Southern Hemisphere stratospheric major warming[J]. Mon. Weather Rev., 2006, 134(2):498-518
[30]	ECKERMANN S D, HOPPEL K W, COY L, et al. High-altitude data assimilation system experiments for the northern summer mesosphere season of 2007[J]. J. Atmos. Sol.:Terr. Phys., 2009, 71(3):531-551
[31]	KIESEWETTER G, SINNHUBER B M, VOUNTAS M, et al. A long-term stratospheric ozone data set from assimilation of satellite observations:high-latitude ozone anomalies[J]. J. Geophys. Res.:Atmos., 2010, 115(D10). DOI: 10.1029/2009JD013362
[32]	INNESS A, BLECHSCHMIDT A M, BOUARAR I, et al. Data assimilation of satellite-retrieved ozone, carbon monoxide and nitrogen dioxide with ECMWF's Composition-IFS[J]. Atmos. Chem. Phys., 2015, 15(9):5275-5303
[33]	SWADLEY S D, POE G A, BELL W, et al. Analysis and characterization of the SSMIS upper atmosphere sounding channel measurements[J]. IEEE Trans. Geosci. Electron., 2008, 46(4):962-983
[34]	HOPPEL K W, ECKERMANN S D, COY L, et al. Evaluation of SSMIS upper atmosphere sounding channels for high-altitude data assimilation[J]. Mon. Weather Rev., 2013, 141(10):3314-3330
[35]	DONG Yaning. Study on the Global Ozone Satellite Data Assimilation Experiment Based on Ensemble Kalman Filter[D]. Nanjing:Nanjing University of Information Science and Technology, 2016(董亚宁. 基于集合卡尔曼滤波全球臭氧卫星观测资料同化试验研究[D]. 南京:南京信息工程大学, 2016)
[36]	XIE Yanxin, WU Xiaocheng, HU Xiong, et al. Preliminary study on 3-dimensional variational assimilation of global temperature field in near space[J]. Infrared Laser Eng., 2017, 46(8):47-52(谢衍新, 吴小成, 胡雄, 等. 临近空间全球温度场三维变分同化[J]. 红外与激光工程, 2017, 46(8):47-52)
[37]	JING Wenqi, WANG Yegui, CUI Yuanyuan, et al. Assimilation of near space ozone data from SABER and MLS observations into the whole atmosphere community climate model and data assimilation research test-bed[J]. Chin. J. Atmos. Sci., 2019, 43(2):233-250(敬文琪, 王业桂, 崔园园, 等. 基于WACCM+DART的临近空间SABER和MLS臭氧观测同化试验研究[J]. 大气科学, 2019, 43(2):233-250)
[38]	REMSBERG E E, MARSHALL B T, GARCIA-COMAS M, et al. Assessment of the quality of the Version 1.07 temperature-versus-pressure profiles of the middle atmosphere from TIMED/SABER[J]. J. Geophys. Res.:Atmos., 2008, 113(D17).DOI: 10.1029/2008JD010013
[39]	LIN S J. A “vertically Lagrangian” finite-volume dynamical core for global models[J]. Mon. Weather Rev., 2004, 132(10):2293-2307
[40]	MARSH D R, GARCIA R R, KINNISON D E, et al. Modeling the whole atmosphere response to solar cycle changes in radiative and geomagnetic forcing[J]. J. Geophys. Res.:Atmos., 2007, 112(D23).DOI: 10.1029/2006JD008306
[41]	MARSH D R, MILLS M J, KINNISON D E, et al. Climate change from 1850 to 2005 simulated in CESM1(WACCM)[J]. J. Clim., 2013, 26(19):7372-7391
[42]	KINNISON D E, BRASSEUR G P, WALTERS S, et al. Sensitivity of chemical tracers to meteorological parameters in the MOZART-3 chemical transport model[J]. J. Geophys. Res.:Atmos., 2007, 112(D20).DOI: 10.1029/2006JD007879
[43]	ANDERSON J L. An ensemble adjustment Kalman filter for data assimilation[J]. Mon. Weather Rev., 2001, 129(12):2884-2903
[44]	ANDERSON J L. A non-Gaussian ensemble filter update for data assimilation[J]. Mon. Weather Rev., 2010, 138(11):4186-4198
[45]	ANDERSON J L. Spatially and temporally varying adaptive covariance inflation for ensemble filters[J]. Tellus A, 2009, 61(1):72-83
[46]	ANDERSON J L.Localization and sampling error correction in ensemble Kalman filter data assimilation[J]. Mon. Weather Rev., 2012, 140(7):2359-2371
[47]	WANG Y, JING W, CUI Y, et al. Application of ERA5 reanalysis to the construction of initial conditions for WACCM simulations[J]. Chin. J. Space Sci., 2018, 38(4):460-468
[48]	HOFFMAN R N, KALNAY E. Lagged average forecasting, an alternative to Monte Carlo forecasting[J]. Tellus A, 1983, 35(2):100-118
[49]	JAPAN METEOROLOGICAL AGENCY. Annual Report on the Climate System 2016[R]. Tokyo:Japan Meteorological Agency, 2017
[50]	COY L, ECKERMANN S D, HOPPEL K W, et al. Mesospheric precursors to the major stratospheric sudden warming of 2009:validation and dynamical attribution using a ground-to-edge-of-space data assimilation system[J]. J. Adv. Model. Earth Syst., 2011, 3(4).DOI: 10.1029/2011MS000067
[51]	REN S, POLAVARAPU S, BEAGLEY S R, et al. The impact of gravity wave drag on mesospheric analyses of the 2006 stratospheric major warming[J]. J. Geophys. Res.:Atmos., 2011, 116(D19).DOI: 10.1029/2011JD015943