遥感数据同化中亮温数据质量控制分析
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摘要
数据同化可以通过同化观测资料为数值天气预报提供高精度的初值数据。但是,在数据同化使用的卫星观测资料中存在与辐射传输模型模拟数据不一致的数据。同化与模拟不一致的观测数据可能导致分析值不平衡,甚至导致系统崩溃。为保证数据同化结果的准确性,在数据同化前应对观测资料进行质量控制。依据国内外的相关研究,尝试对亮温数据的质量控制方案进行系统总结。针对亮温数据,分析了直接同化中亮温数据的误差来源;介绍了亮温数据质量控制方法,包括合理性检验、离群数据剔除、偏差订正和变分质量控制;并以双权重算法为例,进行了亮温数据质量控制的实例分析;最后,针对亮温数据质量控制提出了几点展望。
Data assimilation can provide high-quality initial data for numerical weather forecast through assimilation of observational data. However,there may be observations that are inconsistent with the simulation data of radiation transmission model in assimilation system. Assimilation of inconsistent observations can lead to an unbalanced analysis and even a crash of the system. In order to ensure the accuracy of results of data assimilation,the quality of observations should be controlled before data assimilation. Based on the relevant research at home and abroad,this paper attempts to systematically summarize the quality control scheme of the brightness temperature data. In this paper,the source of error of brightness temperature data in data assimilation is analyzed. The methods of quality control of brightness temperature data are introduced,including domain truth validation,outlier data elimination,bias correction and variational quality control.And taking the double weight algorithm as an example,an experiment of quality control of brightness temperature data is done. Finally,some prospects are put forward for the brightness temperature data quality control.
Data assimilation can provide high-quality initial data for numerical weather forecast through assimilation of observational data. However,there may be observations that are inconsistent with the simulation data of radiation transmission model in assimilation system. Assimilation of inconsistent observations can lead to an unbalanced analysis and even a crash of the system. In order to ensure the accuracy of results of data assimilation,the quality of observations should be controlled before data assimilation. Based on the relevant research at home and abroad,this paper attempts to systematically summarize the quality control scheme of the brightness temperature data. In this paper,the source of error of brightness temperature data in data assimilation is analyzed. The methods of quality control of brightness temperature data are introduced,including domain truth validation,outlier data elimination,bias correction and variational quality control.And taking the double weight algorithm as an example,an experiment of quality control of brightness temperature data is done. Finally,some prospects are put forward for the brightness temperature data quality control.
引文
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[25]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):1514~1527.
[26]Gauthier P,Chouinard C,Brasnett B.Quality Control:Methodology and Applications[M].Data Assimilation for the Earth System.2003:177~187.
[27]和杰.GRAPES-3DVAR中非高斯分布观测误差资料的变分质量控制研究[D].南京:南京信息工程大学,2016.
[28]马旭林,和杰,周勃旸等.非高斯分布观测误差资料的变分质量控制对暴雨预报的影响[J].大气科学学报,2017,40(2):170~180.
[29]王根,唐飞,刘晓蓓等.M-估计法广义变分同化FY-3B/IRAS通道亮温[J].遥感学报,2017,21(1):52~61.
[30]许剑辉,舒红.基于Triple-Collocation的地面观测与卫星遥感数据融合的雪深反演[J].武汉大学学报·信息科学版,2015,40(4):469~473.
[2]Sakov P,Sandery P.An adaptive quality control procedure for data assimilation[J].Tellus,2017,69(1):1318031.
[3]杨寅,韩威,董佩明.AMSU微波探测资料同化的质量控制方法概述[J].气象,2011,37(11):1395~1401.
[4]Daganzo E,Oliva R,Kerr Y,et al.SMOS Radiometer in the1400-1427-MHz Passive Band:Impact of the RFI Environment and Approach to Its Mitigation and Cancellation[J].IEEE Transactions on Geoscience&Remote Sensing,2013,51(10):4999~5007.
[5]朱江.观测资料的四维质量控制:变分法[J].气象学报,1995(4):480~487.
[6]Oliva R,Nieto S,Félix-Redondo F.RFI Detection Algorithm:Accurate Geolocation of the Interfering Sources in SMOS Images[J].IEEE Transactions on Geoscience&Remote Sensing,2013,51(10):4993~4998.
[7]邹晓蕾.资料同化理论和应用[M].北京:气象出版社,2009.
[8]白翎.陆面数据同化中微波亮温资料质量控制方法研究[D].南京:南京信息工程大学,2013.
[9]Grody N C,Basist A N.Global identification of snowcover using SSM/I measurements[J].IEEE Transactions on Geoscience&Remote Sensing,1996,34(1):237~249.
[10]王云峰,费建芳,袁炳等.消除系统性观测误差的时空梯度信息同化方法研究[J].大气科学,2013,37(1):54~64.
[11]马刚,王云峰,张晓辉等.基于梯度信息的微波辐射亮温资料质量控制方法[J].热带海洋学报,2017,36(2):86~95.
[12]邹晓蕾,马原,秦正坤.运用主成分分析方法诊断和消除风云三号B星微波湿度计观测数据中的噪音[J].气象科技进展,2013(4):52~61.
[13]Zhao J,Zou X,Weng F.Wind Sat Radio-Frequency Interference Signature and Its Identification Over Greenland and Antarctic[J].IEEE Transactions on Geoscience&Remote Sensing,2013,51(9):4830~4839.
[14]冯呈呈,赵虹.FY-3B微波成像仪海洋数据无线电干扰识别[J].遥感学报,2015,19(3):465~475.
[15]Lanzante J R.Resistant,Robust and Non-Parametric Techniques for the Analysis of Climate Data:Theory and Examples,Including Applications to Historical Radiosonde Station Data[J].International Journal of Climatology,2015,16(11):1197~1226.
[16]XZ And,Z Zeng.A quality control procedure for GPS radio occultation data[J].Journal of Geophysical Research Atmospheres,2006,111(D2).
[17]王叶慧,李刚,何光鑫.风云三号卫星微波辐射率资料的质量控制[J].大气科学学报,2013,36(5):560~567.
[18]王根,张华,杨寅.高光谱大气红外探测器AIRS资料质量控制研究进展[J].地球科学进展,2017,32(2):139~150.
[19]Eyre,J.R.A bias correction scheme for simulated TOVSbrightness temperatures[C].1992.
[20]李刚,吴兆军,张华.偏差订正方法在IASI辐射率资料同化中的应用研究[J].大气科学学报,2016,39(1):72~80.
[21]Harris BA,Kelly G.A satellite radiance-bias correction scheme for data assimilation[J].Quarterly Journal of the Royal Meteorological Society,2001,127(574):1453~1468.
[22]Duan B,Zhang W,Yang X,et al.Assimilation of Tropical Cyclone Wind Field Retrieved from Scatterometer and SARBased on the Huber Norm Quality Control[J].Remote Sensing,2017,9(10).
[23]Lorenc A C.Analysis methods for numerical weather prediction[J].Quarterly Journal of the Royal Meteorological Society,1986,112(474):1177~1194.
[24]Lorenc A C,Hammon O.Objective quality control of observations using Bayesian methods.Theory,and a practical implementation[J].Quarterly Journal of the Royal Meteorological Society,1988,114(480):515~543.
[25]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):1514~1527.
[26]Gauthier P,Chouinard C,Brasnett B.Quality Control:Methodology and Applications[M].Data Assimilation for the Earth System.2003:177~187.
[27]和杰.GRAPES-3DVAR中非高斯分布观测误差资料的变分质量控制研究[D].南京:南京信息工程大学,2016.
[28]马旭林,和杰,周勃旸等.非高斯分布观测误差资料的变分质量控制对暴雨预报的影响[J].大气科学学报,2017,40(2):170~180.
[29]王根,唐飞,刘晓蓓等.M-估计法广义变分同化FY-3B/IRAS通道亮温[J].遥感学报,2017,21(1):52~61.
[30]许剑辉,舒红.基于Triple-Collocation的地面观测与卫星遥感数据融合的雪深反演[J].武汉大学学报·信息科学版,2015,40(4):469~473.
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