地球科学进展 ›› 2017, Vol. 32 ›› Issue (2): 139 -150. doi: 10.11867/j.issn.1001-8166.2017.02.0139

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高光谱大气红外探测器AIRS资料质量控制研究进展
王根 1, 2( ), 张华 3, *( ), 杨寅 3   
  1. 1.中国气象局沈阳大气环境研究所,辽宁 沈阳 110016
    2.安徽省气象信息中心, 安徽 合肥 230031
    3.国家气象中心, 北京 100081
  • 收稿日期:2016-10-03 修回日期:2016-12-18 出版日期:2017-02-20
  • 通讯作者: 张华 E-mail:203wanggen@163.com;zhangh@cma.gov.cn
  • 基金资助:
    中国气象局沈阳大气环境研究所开放基金课题“基于非高斯模型的AIRS水汽通道同化初步研究”(编号:2016SYIAE14);公益性行业(气象)科研专项“作物湿渍害星地一体化监测与预警技术研究及应用示范”(编号: GYHY201406028)资助

Research Progress of Quality Control for AIRS Data

Gen Wang 1, 2( ), Hua Zhang 3, *( ), Yin Yang 3   

  1. 1.The Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110016, China
    2.Anhui Meteorological Information Centre, Hefei 230031, China
    3.National Meteorological Center of China, Beijing 100081, China
  • Received:2016-10-03 Revised:2016-12-18 Online:2017-02-20 Published:2017-02-20
  • Contact: Hua Zhang E-mail:203wanggen@163.com;zhangh@cma.gov.cn
  • About author:

    First author:Wang Gen(1983-),male,Taizhou City, Jiangsu Province, Engineer. Research areas include satellite data assimilation, numerical simulation of GRAPES and multi-source data fusion.E-mail:203wanggen@163.com

  • Supported by:
    Project supported by the Shenyang Institute of Atmospheric Environment of China Meteorological Administration “Application of non-gaussian model on AIRS water channel brightness temperature assimilation”(No.2016SYIAE14);The China’s Research and Development Special Fund for Public Welfare Industry “Technology research and application demonstration of monitoring and warning of the combination satellite-ground on crops wet damages”(No.GYHY201406028)

同化卫星资料能够得到模式较好的初始场,目前资料变分同化是基于误差服从高斯分布这一理论,因此在同化高光谱大气红外探测器(AIRS)资料前,必须进行资料质量控制。从通道选择、异常值剔除、偏差订正、云检测和数据稀疏化5个方面对AIRS资料质量控制研究现状进行分析与讨论。归纳总结了基于信息熵分步迭代法、主成分累计影响系数法和主成分—逐步回归法3种通道选择方法。经分析比较认为信息熵分步迭代法使用得较为广泛,但所选通道之间存在“弱相关”;主成分—逐步回归法能够获得信息量较大的通道组合,由于算法的原因,执行过程较耗时。探讨了莱茵达法则和稳健性较强的双权重法对异常离群值剔除,得到双权重法效果较好。介绍了离线和在线偏差订正方法,包括静态、自适应、回归法、变分、基于辐射传输模式、基于卡尔曼滤波偏差订正法和偏差订正的动态更新技术。对比发现静态法时效性较好;变分法能够解决数据漂移等问题;基于模式和卡尔曼法虽效果较好,但较耗时不适合业务化使用;综合而言,偏差订正动态更新技术的效果和时效性都较好。分析了晴空视场点、晴空通道、云辐射订正和不同仪器云产品的匹配4种云检测方法。从数值业务时效性角度出发,晴空视场点和晴空通道云检测法较为可行,但经过晴空视场点云检测后同化的资料量比晴空通道法少,会造成在气象敏感区如高层通道资料的丢弃,在一定程度上会影响分析场的质量。进一步分析了跳点跳线、box法和主成分分析法在AIRS资料稀疏化中的初步应用研究。从同化时效性和可操作性出发,得出box法可行;主成分分析法算法复杂度较高,但具有一定的应用前景。在综述质量控制部分基础上,进一步给出了该领域未来的相关研究方向。

Satellite data assimilation can provide accurate initial field for Numerical Weather Prediction (NWP) models. So far, data variational assimilation is based on the theory where error obeys Gaussian distribution, so as to apply the least square method. During classical variational assimilation, if the data contain outliers, the results of optimal parameter estimation is meaningless. Therefore, quality control is quite necessary for Atmospheric Infrared Sounder (AIRS) data before data assimilation. This paper made a comment of the advances in the quality control using AIRS data, which analyzed and discussed the research status from five aspects: channel selection, outliers elimination, bias correction, cloud detection and data sparseness. Three methods for channel selection were summarized, which are stepwise iterative method based on information entropy, the cumulative effect coefficient of principal component and principal components—Stepwise regression, respectively. Comparatively, stepwise iterative method based on information entropy is more widely used, but the selected channels are weak related; Channel combination with large amount of information can be obtained through the method of principal components—stepwise regression, but the implementation process is time-consuming due to the algorithm. Both the lane of law and the double weight method were used in outliers elimination, and the result shows that the latter one is better. Two kinds of bias correction method including off-line and on-line, were introduced, which contain static, adaptive, regression method, variational, method based on the radiative transfer model, bias correction with Kalman filter and dynamic update of bias correction technique. It is found that the timeliness of static method is better; while variational method could solve the problems of data drift and so on. The result is better when using bias correction based on the model and Kalman methods, but it is more time-consuming and not suitable for business application. Generally, the effect and timeliness of dynamic update one is the best among them. In this paper four kinds of cloud detection method are discussed here, including the sky field-of-view, sky channel, cloud radiation correction and different instrument cloud products matching. The first two methods are more feasible from the perspective of timeliness for numerical prediction, but the data quantity using could detection method of sky field-of-view is less than sky channel, leading to discarding of lots of channel data in climate sensitive area such as upper channel, and thus affecting the quality of analysis field. Further on, the methods of hops jumper, box and principal component applied to AIRS data sparseness were analyzed. From assimilation timeliness and operability, box method is feasible; although there is high complexity with algorithm of principal component analysis, which has a certain application prospect. After reviewing the quality control section, some further research directions in these fields were given respectively.

中图分类号: 

图1 质量控制和变分同化逻辑关系框架
TBs表示亮温,“O-B”表示观测亮温-模拟亮温, “O”表示观测亮温,“B”表示模拟亮温
Fig.1 The framework of logic relations of quality control and variational assimilation
TBs:Brightness Temperature,“O-B”: Observation brightness temperature-simulation brightness temperature,“O”: Observation brightness temperature, “B”:Simulation-minus brightness temperature
图2 预报24~120小时距平相关系数
Fig.2 Anomaly correlation coefficient of forecast 24~120 hours
[1] Wang G, Zhang J W.Generalised variational assimilation of cloud-affected brightness temperature using simulated hyper-spectral atmospheric infrared sounder data[J].Advances in Space Research,2014, 54(1):49-58.
[2] Pan Liujie, Zhang Hongfang, Wang Jianpeng.Progress on verification methods of numerical weather prediction[J].Advances in Earth Science, 2014,29(3):327-335.
[潘留杰,张宏芳,王建鹏.数值天气预报检验方法研究进展[J].地球科学进展,2014,29(3):327-335.]
[3] Liu Yang, Cai Bo, Ban Xianxiu, et al.Research progress of retrieving atmosphere humidity profiles from AIRS data[J].Advances in Earth Science,2013,28(8):890-896.
[刘旸,蔡波,班显秀,等.AIRS红外高光谱资料反演大气水汽廓线研究进展[J].地球科学进展,2013,28(8): 890-896.]
[4] Rabier F, Fourrie N, Chafai D, et al.Channel selection methods for infrared atmospheric sounding interferometer radiances[J].Quarterly Journal of the Royal Meteorological Society,2002,128(581):1 011-1 027.
[5] Zhang Jianwei, Wang Gen, Zhang Hua, et al.Experiment on hyper-spectral atmospheric infrared sounder channel selection based on the cumulative effect coefficient of principal component[J]. Transactions of Atmospheric Sciences, 2011, 34(1):36-42.
[张建伟,王根,张华,等.基于主成分累计影响系数法的高光谱大气红外探测器的通道选择试验[J].大气科学学报,2011,34(1):36-42.]
[6] Wang Gen, Lu Qifeng, Zhang Jianwei, et al.Study on method and experiment of hyper-spectral atmospheric sounder channel selection[J]. Remote Sensing Technology and Application, 2014, 29(5):795-802.
[王根,陆其峰,张建伟,等.高光谱大气红外探测器通道选择方法及试验研究[J].遥感技术与应用,2014,29(5):795-802.]
[7] Harris B A, Kelly G.A satellite radiance-bias correction scheme for data assimilation[J]. Quarterly Journal of the Royal Meteorological Society, 2001,127(574): 1 453-1 468.
[8] Auligne T, McNally A P, Dee D P. Adaptive bias correction for satellite data in a numerical weather prediction system[J]. Quarterly Journal of the Royal Meteorological Society, 2007, 133(624):631-642.
[9] Liu Z Q, Barker D M.Radiance Data Assimilation in WRF-Var: Implementation and Initial Results[R]. The 7th WRF Users’ Workshop, 2006.
[10] Dee D P.Variational bias correction of radiance data in the ECMWF system[C]∥Proceedings of the ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP, Reading, UK,2004.
[11] Watts P D, McNally A P. Identification and correction of radiative transfer modeling errors for atmospheric sounders: AIRS and AMSU-A[C]∥Proceeding of the ECMWF Workshop on Assimilation of High Spectral Resolution Sounders in NWP,UK,2004.
[12] Elana J F, Seung J B, Brianr H, et al.Observation bias correction with an ensemble Kalman filter[J].Tellus, 2009, 61(2):210-226.
[13] Wang Xueman, Li Gang, Zhang Hua.Application of online bias correction of polar orbit satellite observations in GRAPES-3Dvar[J].Meteorological Monthly,2015,41(7):863-871.
[王雪曼,李刚,张华.极轨卫星观测偏差订正动态更新技术在GRAPES-3Dvar的应用研究[J].气象,2015,41(7):863-871.]
[14] English S J, Eyre J R, Smith J A.A cloud-detection scheme for use with satellite sounding radiances in the context of data assimilation for numerical weather prediction[J]. Quarterly Journal of the Royal Meteorological Society, 1999, 125(559): 2 359-2 378.
[15] McNally A P, Watts P D. A cloud detection algorithm for high-spectral-resolution infrared sounders[J]. Quarterly Journal of the Royal Meteorological Society, 2003, 129(595): 3 411-3 423.
[16] Li J, Liu C Y, Huang H L, et al.Optimal cloud-clearing for AIRS radiances using MODIS[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005,43(6): 1 266-1 278.
[17] Guan Li.The Application of Space-borne Infrared Hyper-spectral Data[M]. Beijing: China Meteorological Press,2008: 55-56.
[官莉. 星载红外高光谱资料的应用[M].北京:气象出版社,2008: 55-56.]
[18] Zhang Hua, Xue Jishan, Zhuang Shiyu, et al.Idea experiments of GRAPES three-dimensional variational data assimilation system[J]. Acta Meteorological Sinica, 2004, 62(1): 31-41.
[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.]
[19] Collard A, Saunders R, Cameron J, et al.Assimilation of data from AIRS for improved numerical weather prediction[C]∥13th International TOVS Study Conferences. Adele, Canada, 2003.
[20] Rodgers C D.Information content and optimisation of high spectral resolution remote measurements[J]. Advances in Space Research, 1998, 21(3):361-367.
[21] Fourrié N, Thépaut J N.Validation of the NESDIS Near Real Time AIRS Channel Selection[M]. England: European Center for Medium Range Weather Forcasts,2002.
[22] Fourrié N, Thépaut J N.Evaluation of the AIRS near-real-time channel selection for application to numerical weather prediction[J]. Quarterly Journal of the Royal Meteorological Society, 2003,129(592):2 425-2 439.
[23] Cameron J R N, Collard A D, English S J. Operational use of AIRS observations at the met office[C]∥Proceeding of the 14th International TOVS Study Conferences.Beijing,2005.
[24] Du Huadong, Huang Sixun, Shi Hanqing.Method and experiment of channel selection for high spectral resolution data[J]. Acta Physica Sinica,2008, 57(12):7 685-7 692.
[杜华栋, 黄思训,石汉青.高光谱分辨率遥感资料通道最优选择方法及试验[J].物理学报, 2008,57(12):7 685-7 692.]
[25] Zhang Shuiping.Hyperspectral atmospheric sounding information channel selection study[J].Scientia Meteorologica Sinica, 2009,29(4):475-481.
[张水平. AIRS资料反演大气温度廓线的通道选择研究[J].气象科学,2009, 29(4):475-481.]
[26] Liu Hui, Dong Chaohua, Zhang Wenjian, et al.Retrieval of clear-air atmospheric temperature profiles using AIRS observations[J]. Acta Meteorologica Sinica, 2008, 66(4):513-519.
[刘辉,董超华,张文建,等.AIRS晴空大气温度廓线反演试验[J].气象学报,2008,66(4):513-519.]
[27] Weisz E.Temperature Profiling by the Infrared Atmospheric Sounding Interferometer (IASI): Advanced Retrieval Algorithm and Performance Analysis[R].Graz,Austrnd: Karl-Franzwns Universitat,2001.
[28] Wang Gen, Tang Fei, Liu Xiaobei, et al.Application of M-estimators method on FY3B/IRAS channel brightness temperature generalized variational assimilation[J].Journal of Remote Sensing, 2017, 21(1):52-61.
[王根,唐飞,刘晓蓓,等. M-估计法广义变分同化FY3B/IRAS通道亮温[J].遥感学报,2017,21(1):52-61.]
[29] Wang Gen, Liu Xiaobei, Yang Yin, et al.Application of bi-weighting quality control method to FY-3B/IRAS data[J]. Journal of Chengdu University of Information Technology,2014,29(6):609-615.
[王根,刘晓蓓,杨寅,等.双权重质量控制法在FY-3B/IRAS资料中的应用研究[J].成都信息工程学院学报,2014,29(6):609-615.]
[30] Hocking J, Rayer P, Saunders R, et al.RTTOV v10 Users Guide[Z].NWPSAFMO-UD-023, EUMETSAT, Darmstadt, Germany, 2010.
[31] Weng F, Han Y, Delst P, et al.JCSDA community radiative transfer model (CRTM)[C]∥Proceedings of the 14th International TOVS Study Conference. Beijing, 2005.
[32] Jung J A, Le Marshall J F, Riishojgaard L P, et al. The development of hyperspectral infrared water vapor radiance assimilation techniques in the NCEP Global Forecast System[C]∥ECMWF/EUMETSAT NWP-SAF Workshop on the Assimilation of IASI in NWP,2009.
[33] Zou X, Zeng Z.A quality control procedure for GPS radio occultation data[J]. Journal of Geophysical Research: Atmospheres, 2006,111(D2), doi:10.1029/2005JD005846.
[34] Wang Gen.FY3B/IRAS Data Bias Correction, Cloud Detection, Quality Control and Assimilation Test[D].Nanjing:Nanjing University of Information Science and Technology,2014.
[王根. FY3B/IRAS资料偏差订正、云检测、质量控制和同化测试[D].南京:南京信息工程大学,2014.]
[35] Tavolato C, Isaksen L.On the use of a Huber norm for observation quality control in the ECMWF 4D-Var[J]. Quarterly Journal of the Royal Meteorological Society, 2015, 141(690): 1 514-1 527.
[36] Liu Zhiquan, Zhang Fengying, Wu Xuebao, et al.A regional ATOVS radiance bias correction scheme for radiance assimilation[J]. Acta Meteorological Sinica, 2007, 65(1):113-123.
[刘志权,张凤英,吴雪宝,等.区域极轨卫星ATOVS辐射偏差订正方法研究[J].气象学报,2007,65(1):113-123.]
[37] Kelly G, Flobert J F.Radiance tuning[C]∥Technical Proceedings della 4th International TOVS Study Conference. Igls, Austria, 1988.
[38] Mcmillin L M, Crone L J, Crosby D S.Adjusting satellite radiances by regression with an orthogonal transformation to a prior estimate[J]. Journal of Applied Meteorology, 1989, 28(9):969-975.
[39] Eyre J R.A Bias Correction Scheme for Simulated TOVS Brightness Temperatures[R]. Technical Memorandum. Shinfield: ECMWF,1992.
[40] Cui L M,Sun J H,Qi L L.Two bias correction schemes for ATOVS radiance data[J].Journal of Tropical Meteorology,2010,16(1):71-76.
[41] Harris B A, Kelly G.A satellite radiance bias correction scheme for data assimilation[J]. Quarterly Journal of the Royal Meteorological Society, 2001, 127(574):1 453-1 468.
[42] Wang Xiang.ATVOS Radiation Data Bias Correction Scheme Research and Application[D].Nanjing:Nanjing University of Information Science and Technology,2009:4-5.
[王祥. ATVOS辐射率资料偏差订正方案研究与应用[D].南京:南京信息工程大学,2009:4-5.]
[43] Wang Xiang, Li Gang, Zhang Hua, et al.The GRAPES variational bias correction scheme and associated preliminary experiments[J]. Acta Meteorological Sinica, 2011, 25(1):51-62.
[44] Auligne T, McNally A P. Interaction between bias correction and quality control[J]. Quarterly Journal of the Royal Meteorological Society, 2007, 133(624): 643-653.
[45] Menzel W P, Smith W L, Stewart T R.Improved cloud motion wind vector and altitude assignment using VAS[J]. Journal of Climate and Applied Meteorology, 1983, 22(3): 377-384.
[46] Smith W L, Frey R.On cloud altitude determinations from high resolution interferometer sounder (HIS) observations[J]. Journal of Applied Meteorology, 1990, 29(7): 658-662.
[47] Goldberg M D, Qu Y, McMillin L M, et al. AIRS near-real-time products and algorithms in support of operational numerical weather prediction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(2): 379-389.
[48] Huang H L, Smith W L, Li J, et al.Minimum local emissivity variance retrieval of cloud altitude and effective spectral emissivity-simulation and initial verification[J].Journal of Applied Meteorology, 2004, 43(5): 795-809.
[49] Huang Hunglung, Li Jun, Baggett K, et al.Evaluation of cloud-cleared radiances for numerical weather prediction and cloud contaminated sounding applications[C]∥Atmospheric and Environmental Remote Sensing Data Processing and Utilization: Numerical Atmospheric Prediction and Environment Monitoring.Proceedings of SPIE 5890. SanDiego, California,USA,2005: 38-45.
[50] Chen Jing, Li Gang, Zhang Hua, et al.Application of cloud detection to assimilation of AIRS radiance data[J]. Meteorological Monthly,2011,37(5):555-563.
[陈靖,李刚,张华,等.云检测在高光谱大气红外探测器辐射率直接同化中的应用[J].气象,2011,37(5):555-563.]
[51] Zhu Wengang, Li Gang, Zhang Hua, et al.Study on application technique of cloud detection and clear channels hyperspectral atmospheric infrared detector AIRS data[J].Meteorological Monthly,2013,39(5):633-644.
[朱文刚,李刚,张华,等.高光谱大气红外探测器AIRS资料云检测及晴空通道应用技术初步研究[J].气象,2013,39(5):633-644.]
[52] Heilliette S, Garand L.A practical approach for the assimilation of cloudy infrared radiances and its evaluation using AIRS simulated observations[J].Atmosphere—Ocean,2007, 45(4): 211-225.
[53] Dee D P, Rukhovets L, Todling R, et al.An adaptive buddy check for observational quality control[J], Quarterly Journal of the Royal Meteorological Society, 2001, 127(577): 2 451-2 471.
[54] Lu Qifeng.Initial evaluation and assimilation of FY-3A atmospheric sounding data in the ECMWF system[J]. Science in China (Series D),2011,41(7):890-894.
[陆其峰. 风云三号A星大气探测资料数据在欧洲中期天气预报中心的初步评价与同化研究[J].中国科学:D辑,2011,41(7):890-894.]
[55] McNally A P, Watts P D, Smith J A, et al. The assimilation of AIRS radiance data at ECMWF[J]. Quarterly Journal of the Royal Meteorological Society,2006, 132(616): 935-957.
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