浏览全部资源
扫码关注微信
Received：06 January 2016，
Published：2017-01
移动端阅览
采用FY-3B/IRAS亮温资料进行广义变分同化研究。广义变分同化结合了经典变分同化和稳健M-估计两者的优点。区别于经典变分同化依赖于先前的质量控制并要求误差服从高斯分布，把M-估计法耦合到经典变分同化框架中，得到广义变分同化，其弱化了同化前的质量控制和误差服从高斯分布这两个条件。目标能量泛函包含M-估计以保证对离群值具有稳健性，从而能够得到较好的同化结果。对比变分同化前后的FNL资料湿度与GDAS湿度相关系数作为同化结果检验评价。具体操作过程在FNL作为背景场的基础上分别采用经典和M-估计不同的权重因子变分同化FY3B/IRAS资料，把得到的分析场与GDAS进行相关性比较，由于湿度具有较强的非高斯性，文中首先评估了安徽省13个站GPS/PWV和积分相关湿度廓线得到大气可降水量(即GDAS/PWV和FNL/PWV资料)的相关性，进一步基于信息熵自由度思想进行了近一个月IRAS 20个通道对分析场的影响贡献率诊断研究。
This study adopts the Infrared Atmospheric Sounder of Feng-Yun and the 3rd(B) Weather Satellite(FY-3B/IRAS) brightness temperature to investigate the generalized variational assimilation
which combines the advantages of classical variational assimilation and robust M-estimators. Classical variational assimilation is based on the model variables and satellite observations of the brightness temperature to yield a quadratic functional minimization. The observational errors are needed to follow a Gaussian distribution and subsequently apply the least-square principle. The least-squares method is sensitive to outliers; if the analyzed data contain gross errors
the parameter estimation will be inaccurate. The classical variational assimilation consists of two stages. First
an appropriate algorithm is used to identify and address outliers in the data and then
the assimilation. This approach may result in the loss of useful data because the outliers are not always harmful; some outliers may represent new information
such as weather phenomena. At present
the quality control is generally based on a certain threshold value if the subjective uncertainty is too strong. If outliers persist after the quality control
the optimal parametric results that are obtained through classical variational assimilation become meaningless.M-estimators are added to the framework of classical variational assimilation to obtain a generalized variational assimilation
which is coupled with quality control in the process of assimilation. The main idea is to use the weight factor of M-estimators to re-estimate the contribution rate of the observation items to the objective function in each process of objective function minimization based on classical variational assimilation. The cost function consists of M-estimators to guarantee the robustness to outliers. Thus
the assimilation results are improved. Humidity is an important dynamic variable in the NWP model. It does not only determine the occurrence of precipitation but also changes the temperature through evaporation and condensation
and it also influences the wind field by changing the pressure gradient. In addition
the nonlinearity of humidity is stronger than temperature
which causes the humidity to follow a stronger non-Gaussian distribution. Thus
humidity was used as an assimilation experiment effect validation
and the correlation coefficient of humidity was compared with FNL and GDAS
which are assimilated by different M-estimator weights. The specific operation process that is based on the FNL as the background field adopts classical and different weight factors of M-estimators to the variational assimilation of FY-3B/IRAS. In addition
the correlation between the analyzed field
and the GDAS is compared. The correlation between 13 GPS/PWV stations of Anhui province and the integral humidity profile of the relevant field from both GDAS/PWV and FNL/PWV is evaluated. Furthermore
based on the information entropy of freedom degrees
the contribution of IRAS 20 channels were determined to analyze the field for nearly a month. Preliminary results demonstrate the potential application value of the generalized variational assimilation.
Anderson E. 2003. Variational quality control[EB/OL]. http://www. ecmwf.int/newsevents/training/meteor-ological_presentations/pdf/ DA_Andersson3.pdf http://www.ecmwf.int/newsevents/training/meteor-ological_presentations/pdf/DA_Andersson3.pdf
Anderson E and Järvinen H. 1999. Variational quality control. Quarterly Journal of the Royal Meteorological Society, 125(554): 697–722
Bewley T R, Temam R and Ziane M. 2000. A general framework for robust control in fluid mechanics. Physica D: Nonlinear Phenomena, 138(3/4), 360–392
Bhar L. 2007. Robust regression[EB/OL]. http://iasri.res. in/design/ ebook/EBADAT/3-Diagnostics%20and%20 Remedial%20Measures/5-ROBUST%20REGRESSION1.pdf http://iasri.res.in/design/ebook/EBADAT/3-Diagnostics%20and%20Remedial%20Measures/5-ROBUST%20REGRESSION1.pdf
Dharssi I, Lorenc A C and Ingleby N B. 1992. Treatment of gross errors using maximum probability theory. Quarterly Journal of the Royal Meteorological Society, 118(507): 1017–1036
丁金才. 2009. GPS气象学及其应用[M]. 北京: 气象出版社: 54–55
Ding J C. 2009. GPS Meteorology and Application. Beijing: Meteorological Press: 54–55
杜华栋, 黄思训, 石汉青. 2008. 高光谱分辨率遥感资料通道最优选择方法及试验. 物理学报, 57(12): 7685–7692
Du H D, Huang S X and Shi H Q. 2008. Method and experiment of channel selection for high spectral resolution data. Acta Physica Sinica, 57(12): 7685–7692
Hampel F R. 1971. A general qualitative definition of robustness. The Annals of Mathematical Statistics, 42(6): 1887–1896
Hampel F R. 1974. The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69(346): 383–393
郝民, 张华, 陶士伟, 龚建东. 2013. 变分质量控制在区域GRAPES-3DVAR中的应用研究. 高原气象, 32(1): 122–132
Hao M, Zhang H, Tao S W and Gong J D. 2013. Application of variational quality control to regional GRAPES-3DVAR. Plateau Meteorology, 32(1): 122–132
Hocking J, Rayer P, Saunders R, Matricardi M, Geer A and Brunel P. 2010. RTTOV v10 Users Guide. NWPSAF-MO-UD-023, , Darmstadt, Germany: EUMETSAT
Huber P J. 1964. Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1): 73–101
Huber P J. 1967. The behavior of maximum likelihood estimates under nonstandard conditions // Proceedings of the 5th Berkeley Symp osium on Mathematical Statistics and Probability, Volume 1: Statistics. Berkeley, CA: University of California Press: 221–233
Huber P J. 1981. Robust Statistics. New York: John Wiley and Sons, Inc
Ingleby N B and Lorenc A C. 1993. Bayesian quality control using multivariate normal distributions. Quarterly Journal of the Royal Meteorological Society, 119(513): 1195–1225
Li H, Liu J J, Fertig E, Kalnay E, Kostelich E and Szunyogh I. 2011. Improved analyses and forecasts with AIRS temperature retrievals using the local ensemble transdorm kalman filter. Journal of Tropical Meteorology, 17(1): 43–49
Liu Z Q and Qi C L. 2005. Robust variational inversion: with simulated ATOVS radiances[EB/OL]. http://cimss.ssec.wisc.edu/ itwg/itsc/itsc14/proceedings/ http://cimss.ssec.wisc.edu/itwg/itsc/itsc14/proceedings/
Lsaksen L. 2010. Variational quality control[EB/OL]. http://www.ecmwf.int/newsevents/training/meteorological_presentations/pdf/DA/VarQC.pdf http://www.ecmwf.int/newsevents/training/meteorological_presentations/pdf/DA/VarQC.pdf
陆其峰. 2011. 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. 中国科学: 地球科学, 41(7): 890–894
Lu Q F. 2011. Initial evaluation and assimilation of FY-3A atmospheric sounding data in the ECMWF System. Science China Earth Sciences, 54(10): 1453
Qi C L, Chen Y, Liu H, Wu C Q and Yin D K. 2012. Calibration and validation of the InfraRed Atmospheric Sounder onboard the FY3B satellite. IEEE Transactions on Geoscience and Remote Sensing, 50(12): 4903–4914
Tachim Medjo T. 2002. Iterative methods for a class of robust control problems in fluid mechanics. SIAM Journal on Numerical Analysis, 39(5): 1625–1647
Tukey J W. 1960. A survey of sampling from contaminated distributions // Olkin I. Contributions to Probability and Statistics. Stanford, CA: Stanford University Press
王根. 2014. FY3B/IRAS资料偏差订正、云检测、质量控制和同化测试. 南京: 南京信息工程大学 Wang G. 2014. FY3B/IRAS Data Bias Correction, Cloud Detection, Quality Control and Assimilation Test.Nanjing: Nanjing University of Information Science and Technology (NUIST)
Wang G and Zhang J W. 2014. Generalised variational assimilation of cloud-affected brightness temperature using simulated hyper-spectral atmospheric infrared sounder data. Advances in Space Research, 54(1): 49–58
张华. 2004. ATOVS辐射率资料的直接同化方法及应用研究. 兰州: 兰州大学 Zhang H. 2004. Study on ATOVS Radiation Data Directly Assimilation Method and Application.Lanzhou: Lanzhou University, 2004
张华, 薛纪善, 庄世宇, 朱国富, 朱宗申. 2004. GRAPES三维变分同化系统的理想试验. 气象学报, 62(1): 31–41
Zhang H, Xue J S, Zhuang S Y, Zhu G F and Zhu Z S. 2004. Idea experiments of GRAPES three-dimensional variational data assimilation system. Acta Meteorologica Sinica, 62(1): 31–41
Zhang J W, Wang G, Yang Y, Zhong X and Wang J. 2013. Study on hyper-spectral atmospheric infrared sounder assimilation. International Journal of Hybrid Information Technology, 6(1): 123–128
张思勃, 官莉. 2015. AMSR-E观测资料干扰对反演地表参数的影响. 中国环境科学, 35(1): 260–268
Zhang S B and Guan L. 2015. Effect of AMSR-E data interference on the retrieval of land surface parameters. China Environmental Science, 35(1): 260–268
朱江. 1995. 观测资料的四维质量控制: 变分法. 气象学报, 53(4): 480–487
Zhu J. 1995. Four-dimensional data quality control: variational method. Acta Meteorologica Sinica, 53(4): 480–487
朱文刚, 李刚, 张华, 金大智, 王根, 钟亦鸣. 2013. 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. 气象, 39(5): 633–644
Zhu W G, Li G, Zhang H, Jin D Z, Wang G and Zhong Y M. 2013. Study on application technique of cloud detection and clear channels hyperspectral atmospheric infrared detector AIRS data. Meteorological Monthly, 39(5): 633–644
相关文章
相关作者
相关机构
京公网安备11010802024621