东亚地区高分辨率物理反演方法的数值试验;内部比较研究

为了考察同步物理反演法(Simultaneous physical retrieval method,简称SPRM)的性能,本文着重在反演方法、红外大气透过率算法效应以及SPRM对不同初估值的依赖性三方面,进行了内部比较研究。结果表明:使用透过率调整算法,可以在一定程度上改善物理反演法精度:SPRM的反演精度对初估值的依赖性比统计回归反演法(Statistical regression retrievalmethod,简称SRRM)弱;本阶段SPRM温度反演试验,就大范围纬向平均而论,尚不及改进的SRRM的精度,但在探空资料稀少地区同步物理反演的气象场结构略优于SRRM。     

综合的遥感反演算法的数值试验

使用改进的初值化反演方法(Improved Initialization Inversion Method 简称3I)的 TIGR(TOVS Imitial Guess Retrieval)数据库资料作为同步物理反演方法(Simultaneous Physical Re-trieval Method 简称 SPRM)的验前初估值,构建了一个新的综合的同步物理反演算法(TIGR/SPRM Algorithm 简称 TSPRA),并对其算法的可行性和实现过程作了分析。在试验中使用NOAA 卫星 TOVS 资料,对3I、SPRM、TSPRA 以及业务的统计回归反演方法(Statistical Re-gression Retrieval Method 简称 SRRM)作了比较试验研究。     

东亚地区气象参数卫星遥感反演理论和方法研究Ⅰ:ISPRM和SRRM

文中对10a来改进的同步物理反演方法(ISPRM)实践与发展进行了总结。在不违背先验信息和卫星测值之间独立的原则下,所做的改进都是针对这类非适定反演问题的欠定性(under-determinedness)进行的。由适定反演理论知道,正演是反演的基础,又是卫星辐射率测值输入和反演结果输出之间的桥梁。为了获得好的正演,就必须竭尽可能充分利用一切可以使用的先验信息,把非适定反演问题转化成适定的反演问题,从而通过Ridge回归或Bayes反演过程,把先验信息、理论模拟信息和卫星探测信息三者最佳地结合起来,以最大限度减少这类反演问题的欠定性对数值解的影响。     

东亚地区气象参数卫星遥感反演理论和方法研究I:ISPRM和SRRM

文中对10a来改进的同步物理反演方法（ISPRM）实践与发展进行了总结。在不违背先验信息和卫星测值之间独立的原则下,所做的改进都是针对这类非适定反演问题的欠定性（under－determinedness）进行的。由适定反演理论知道,正演是反演的基础,又是卫星辐射率测值输入和反演结果输出之间的桥梁。为了获得好的正演,就必须竭尽可能充分利用一切可以使用的先验信息,把非适定反演问题转化成适定的反演问题,从而通过Ridge回归或Bayes反演过程,把先验信息、理论模拟信息和卫星探测信息三者最佳地结合起来,以最大限度减少这类反演问题的欠定性对数值解的影响。     

从FY3A卫星IRAS的多通道辐射率估算OLR

介绍了从FY3A卫星IRAS仪器的通道辐射率估算OLR的技术,包括:用红外辐射传输正演的方法建立IRAS多通道辐射率加权求和计算OLR的反演模式,以及由此模式计算得到的OLR产品。模式的全球RMS=3.53 W/m2,表明远高于从NOAA AVHRR通道5亮温计算OLR的反演模式精度。文中给出IRAS全球OLR产品个例,及该OLR产品与NOAA-17 OLR产品的精度对比。     

东亚地区气象参数卫星遥感反演理论和方法研究Ⅱ:ISPRM和VSPRM2

在改进的同步物理模式（ＩＳＰＲＭ）实践基础上,根据泛函数变分原理,按ＴＯＶＳ遥感方程,提出了较普遍的变分同步物理反演模式（ＶＳＰＲＭ２）,其一级功能（ＶＳＰＲＭ１）和ＩＳＰＲＭ完全相同。ＶＳＰＲＭ从协合（ｓｙｎｅｒｇｙ）遥感反演机理,进一步拓宽和发展了ＩＳＰＲＭ模式。分析了ＶＳＰＲＭ２模式性能,指出这类模式的反演潜力。特别着重分析了模式的水汽核函数参数化和水汽遥感反演问题。针对这类非适定反问题的强欠定性质,为妥善使用先验信息,以获得精确的正演辐射率Ｒ。,还对透射率精确计算,低层温、湿廓线反演通道最佳选择和云层影响修正的改进作了进一步分析。经过长期业务运行条件反演比较实验,结果表明改进的物理反演方法（ＩＳＰＲＭ）是适于中国大陆包括青藏高原温、湿参数分布的反演方法,特别在有云条件下能反演出温、湿廓线。     

应用先进微波探测器AMSU资料遥感反演春季陆地表层湿度

利用先进微波探测器(AMSU)正演快速微波辐射传输模型,完成了AMSU 窗区通道亮温与地表温湿特征的相关性分析,根据模拟计算结果改进了现有业务上反演地表微波辐射率的统计分析方法,得到适用于我国非冻土积雪覆盖地区地表微波辐射率反演的指数分析模型;进一步通过地表微波辐射传输模型迭代反演得到土壤体积含水量信息.2001年相互匹配的AMSU资料和地面农业气象观测站地表相对湿度观测结果的对比分析表明二者间具有一定的相关性.利用2002年3月和2003年3月的AMSU资料,反演了我国陆地区域地表湿度;连续两年春季地表湿度反演结果的对比分析表明,与2002年春季相比,2003年我国北方沙尘暴发生源区地表湿度反演值普遍偏高,潮湿的下垫面特征与沙尘暴发生频次的减少之间有一定的对应关系.试验结果表明,利用AMSU遥感资料可以获取大范围陆地表层湿度信息,进行区域尺度陆地表层湿度特征的动态分析,为我国沙尘暴监测分析提供陆地表层湿度基础信息.     

基于辐射传输模式的中国典型区域大气样本库特征分析

在对中国区域北京、哈尔滨、杭州、昆明、拉萨、三亚、西宁7个代表站逐日探空资料进行质量检验的基础上，对其中1、4、7、10四个月(分别代表4个季节)的探空资料进行了大气温湿廓线的特征分析，同时又与目前国际上用于辐射传输计算的43条温湿廓线进行了简单的对比。结果表明，中国区域的大气廓线有明显的时间、空间分布特征，与海拔高度之间联系不大；仅用国际通用的温湿廓线进行我国区域的卫星正演、反演计算有其缺陷性，必须将具有我国天气、气候特征的大气样本补充进去。所收集样本可用于计算大气透射率、反演的初估场及反演结果的真实性检验等。     

SSM/I资料反演大范围地表湿度试验

利用SSM/ I遥感数据, 结合2002年汛期“973”外场观测资料及T106资料, 借助微波辐射传输正演模型, 通过物理反演原理提取SSM/ I低频19 GHz通道像元平均地表微波比辐射率, 然后利用地表微波辐射模型, 估算SSM/ I像元的地表湿度信息。以2002年8月中旬发生在长江流域的暴雨天气过程为例, 将反演得到的地表湿度信息与地面观测到的降水信息进行对比分析, 并与地面的洪涝特征进行对比分析, 得到了与地面降水观测结果较为一致的对比结果。     

利用NOAA/AMSU资料和GRAPES同化系统对陆面比辐射率计算的改进研究

地表比辐射率计算的不确定性,直接影响到卫星资料在数值预报中同化应用的效果。本文采用美国NOAA/NESDIS的Weng等[1,2]提出的复杂陆面比辐射率模式,同时用NOAA卫星AMSU-ACh1或Ch2反演的地表比辐射率来调整该模式所需的地表参数,从而在缺少详细地表参数的情况下,改进AMSU-A Ch3和Ch15的地表比辐射率计算精度。在积雪地表情况下,用NOAA卫星AMSU-A资料直接反演各通道的比辐射率,在GRAPES同化系统中的应用表明,结果有明显的改进。     

THE STUDY OF RETRIEVAL THEORY AND METHODS FROM SATELLITE REMOTE SENSING FOR METEOROLOGICAL PARAMETERS OVER EASTERN ASIA-PARTI:ISPRM AND SRRM*

A review of ten-year's practice in developing the improved simultaneous physical retrieval method(ISPRM)is given in the hope that some creative ideas can be drawn from it.The improvement upon the SPRM is associated with the under-determinedness of this ill-posed inverse problem.In our experiment,the precondition is observed that prior information must be independent of the satellite measurements.The well-posed retrieval theory has told us that the forward process is fundamental for the retrieval,and it is the bridge between the input of satellite radiance and the output of retrievals.In order to obtain a better result from the forward process.the full advantage of every prior information available must be taken.It is necessary to turn the ill-posed inverse problem into the well-posed one.Then by using the Ridge regression or Bayes algorithm to find the optimal combination among the first guess,the theoretical analogue information and the satellite observations,the impact of the under-determinedness of this inverse problem on the numerical solution is minimized.     

THE STUDY OF RETRIEVAL THEORY AND METHODS FROM SATELLITE REMOTE SENSING FOR METEOROLOGICAL PARAMETERS OVER EASTERN ASIA-PARTI:ISPRM AND SRRM

A review of ten-year's practice in developing the improved simultaneous physical retrievalmethod(ISPRM)is given in the hope that some creative ideas can be drawn from it.Theimprovement upon the SPRM is associated with the under-determinedness of this ill-posed inverseproblem.In our experiment,the precondition is observed that prior information must beindependent of the satellite measurements.The well-posed retrieval theory has told us that theforward process is fundamental for the retrieval,and it is the bridge between the input of satelliteradiance and the output of retrievals.In order to obtain a better result from the forward process.the full advantage of every prior information available must be taken.It is necessary to turn the ill-posed inverse problem into the well-posed one.Then by using the Ridge regression or Bayesalgorithm to find the optimal combination among the first guess,the theoretical analogueinformation and the satellite observations,the impact of the under-determinedness of this inverseproblem on the numerical solution is minimized.     

一种新的TOVS大气湿度反演方法及试验

利用国家气象中心的数值天气预报（NWP）6 h预报场。作为同步物理反演模式初估场，由气象卫星垂直探测（TOVS）资料推导出大气湿度，并给出了试验结果。结果表明，TOVS湿度反演的相对误差（RMS）约为20%。其中，夏季反演结果略优于冬季，而且低层（1000 hPa）和高层（500 hPa以上）与初估场相比有较大的改进，中层（850，700 hPa）二者大致相当。与统计回归反演结果相比其精度约提高10%。该研究为在我国建立“卫星反演/数据同化/数值预报”相互影响的循环系统奠定了基础     

THE STUDY OF RETRIEVAL THEORY AND METHODS FROM SATELLITE REMOTE SENSING FOR METEOROLOGICAL PARAMETERS OVER EASTERN ASIA—PART Ⅱ:ISPRM AND VSPRM2

Based on the practice of improved simultaneous physical retrieval model(ISPRM),in the lightof the functional analysis approach,the variational simultaneous physical retrieval model(VSPRM)has been developed.Its approximation of 1st degree is VSPRM1,which is identicalwith the ISPRM.Its approximation of 2nd degree is VSPRM2,more advanced than the VSPRM1.This paper has analyzed the function of VSPRM2,pointing out the potentiality of synergy retrievalof this model.Also,it has dealt with the problem of parameterization of water vapor's kernelfunctions and retrieval of water vapor remote sensing.Because of the characteristics of this strong ill posed inverse problem,prior information mustbe used wisely in order to get the accurate calculation of radiance R.In the previous paper,wediscussed how to build the best first guess field,the way to determine the P_s and to correct thecalculation of radiance.In this paper,we continue discussing in depth about the calculation oftransmittance,the determination of surface parameters and the selection for an optimumcombination of channels for the low-level sounding.The long-term experiment and comparison work under operational environment have shownthat the ISPRM is useful for retrieval of temperature and water vapor parameters over Chinaincluding the Tibetan Plateau,and it further proves the scientific nature of well-posed inversetheory.     

THE STUDY OF RETRIEVAL THEORY AND METHODS FROM SATELLITE REMOTE SENSING FOR METEOROLOGICAL PARAMETERS OVER EASTERN ASIA-PART Ⅱ:ISPRM AND VSPRM2*

Based on the practice of improved simultaneous physical retrieval model(ISPRM),in the light of the functional analysis approach,the variational simultaneous physical retrieval model(VSPRM) has been developed.Its approximation of 1st degree is VSPRM1,which is identical with the ISPRM.Its approximation of 2nd degree is VSPRM2,more advanced than the VSPRM1.This paper has analyzed the function of VSPRM2,pointing out the potentiality of synergy retrieval of this model.Also,it has dealt with the problem of parameterization of water vapor's kernel functions and retrieval of water vapor remote sensing.Because of the characteristics of this strong ill posed inverse problem,prior information must be used wisely in order to get the accurate calculation of radiance R.In the previous paper,we discussed how to build the best first guess field,the way to determine the P_s and to correct the calculation of radiance.In this paper,we continue discussing in depth about the calculation of transmittance,the determination of surface parameters and the selection for an optimum combination of channels for the low-level sounding.The long-term experiment and comparison work under operational environment have shown that the ISPRM is useful for retrieval of temperature and water vapor parameters over China including the Tibetan Plateau,and it further proves the scientific nature of well-posed inverse theory.     

Study of the Simultaneous Physical Retrieval Method for Meteorological Parameters over the Continental Plateau of China

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Ensemble retrieval of atmospheric temperature profiles from AIRS

ABSTRACT Satellite-based observations provide great opportunities for improving weather forecasting. Physical retrieval of atmo spheric profiles from satellite observations is sensitive to the uncertainty of the first guess and other factors. In order to improve the accuracy of the physical retrieval, an ensemble methodology was developed with an emphasis on perturbing the first guess. In the methodology, a normal probability density function （PDF） is used to select the optimal profile from the ensemble retrievals. The ensemble retrieval algorithm contains four steps： （1） regression retrieval for original first guess; （2） perturbation of the original first guess to generate new first guesses （ensemble first guesses）; （3） using the ensemble first guesses and nonlinear iterative physical retrieval to generate ensemble physical results; and （4） the final optimal profile is selected from the ensemble physical results by using PDE Temperature eigenvectors （EVs） were used to generate the pertur- bation and generate the ensemble first guess. Compared with the regular temperature profile retrievals from the Atmospheric InfraRed Sounder （AIRS）, the ensemble retrievals RMSE of temperature profiles selected by the PDF was reduced between 150 and 320 hPa and below 400 hPa, with a maximum improvement of 0.3 K at 400 hPa. The bias was also reduced in many layers, with a maximum improvement of 0.69 K at 460 hPa. The combined optimal （CombOpt） profile and a mean optimal （MeanOpt） profile of all ensemble physical results were improved below 150 hPa. The MeanOpt profile was better than the CombOpt profile, and was regarded as the final optimal （FinOpt） profile. This study lays the foundation for improving temperature retrievals from hyper-spectral infrared radiance measurements.