Four-dimensional data assimilation method based on SVD: Theoretical aspect

Summary A new method of four-dimensional data assimilation based on Singular Value Decomposition (SVD) is proposed. In it, a set of atmospheric states is obtained by integrating a numerical weather prediction model and simulated observations are taken and calculated from the model variables. Then the SVD technique is used to create the base vectors from this coupled data set. Finally, the analysis is obtained by projecting actual observation data into a space spanned by the base vectors. Using this approach, the four-dimensional data assimilation becomes a simple linear inverse problem the linearization of the nonlinear forward model is avoided, and the developments of the adjoint and background error covariance matrix are no longer needed. Since the SVD technique is used here, the method is simply called 4DSVD.     

两种四维奇异值分解同化方法的比较及误差分析

4DSVD是最近提出的一种新的资料同化方法。目前还存在一些需要解决的问题,比如如何选取样本,如何得到支撑大气吸引子的基向量以及选取基向量的个数问题等等。作者利用奇异值分解(SVD)与经验正交函数分解(EOF)两种方法来获得支撑大气吸引子的基向量,推导了基于这两种方法的4DSVD分析场的理论公式,并用简单的数值试验比较了基于这两种方法的4DSVD分析场的空间相关系数和误差,初步分析了分析场与基向量个数的关系以及与样本选取的关系和分析误差的来源及各种误差对分析误差影响的相对大小。结果表明,用SVD方法作为获得支撑大气吸引子基向量的方法得到的分析场较EOF方法稳定,分析场与基向量个数有密切关系,观测误差、模式误差和观测代表性误差是分析误差的主要来源,且其引起的分析误差随着基向量个数增多而增大。     

GRAPES全球三维变分同化中卫星微波温度计亮温的背景误差及在质量控制中的应用

模式变量背景误差在观测空间的投影,也即观测变量的背景误差包含了变分同化系统的重要信息,其在诊断和分析变分同化系统中资料的影响等方面具有重要作用,特别是在背景场检查质量控制中。在GRAPES全球三维变分同化(3DVar)系统中仅给定了控制变量的背景误差,并未直接给定观测变量的背景误差。为了能够对GRAPES全球3DVar进行全面的诊断和分析,改进卫星微波温度计资料的质量控制,推导出GRAPES全球3DVar同化系统控制变量随机扰动方法估计观测变量的背景误差的公式,为分析和改进GRAPES全球3DVar提供了一个有力工具,并进而估计了AMSU-A亮温的背景误差,分析了AMSU-A不同通道亮温的背景误差特征,将其应用于GRAPES全球3DVar的AMSU-A亮温的背景场检查质量控制中。结果表明,控制变量随机扰动方法估计的GRAPES全球3DVar同化系统AMSU-A亮温的背景误差正确合理。同化循环预报试验结果表明,亮温的背景误差在背景场检查中的应用显著提高了GRAPES全球3DVar同化的亮温资料的数量,显著提高了GRAPES南半球对流层中高层位势高度场的预报技巧。在GRAPES全球3DVar同化系统中推导和实现的控制变量扰动方法为诊断和分析GRAPES全球3DVar观测资料同化效果提供了有力工具。      

Quality Assessment and Forecast Sensitivity of Global Remote Sensing Observations

The satellite-derived wind from cloud and moisture features of geostationary satellites is an important data source for numerical weather prediction(NWP) models. These datasets and global positioning system radio occultation(GPSRO)satellite radiances are assimilated in the four-dimensional variational atmospheric data assimilation system of the UKMO Unified Model in India. This study focuses on the importance of these data in the NWP system and their impact on short-term24-h forecasts. The quality of the wind observations is compared to the short-range forecast from the model background. The observation increments(observation minus background) are computed as the satellite-derived wind minus the model forecast with a 6-h lead time. The results show the model background has a large easterly wind component compared to satellite observations. The importance of each observation in the analysis is studied using an adjoint-based forecast sensitivity to observation method. The results show that at least around 50% of all types of satellite observations are beneficial. In terms of individual contribution, METEOSAT-7 shows a higher percentage of impact(nearly 50%), as compared to GEOS, MTSAT-2and METEOSAT-10, all of which have a less than 25% impact. In addition, the impact of GPSRO, infrared atmospheric sounding interferometer(IASI) and atmospheric infrared sounder(AIRS) data is calculated. The GPSRO observations have beneficial impacts up to 50 km. Over the Southern Hemisphere, the high spectral radiances from IASI and AIRS show a greater impact than over the Northern Hemisphere. The results in this study can be used for further improvements in the use of new and existing satellite observations.     

A stochastic model of SST for climate simulation experiments

 This study describes the implementation of a statistical method to simulate a multi-century sequence of global sea surface temperature (SST) fields. A multi-variable auto-regressive (AR) model is trained on the observed time series of SST from the data set compiled at the Hadley Centre (GISST 2.0). To reduce the dimensionality of the model, the stochastic process is in practice fitted to empirical orthogonal function (EOF) time coefficients of the SST series, retaining the first 14 EOFs. Selected lag cross-covariances among the EOF time series are retained, based on the structure of the cross-correlation matrix and lags up to 64 months are included. Though the resulting system is quite large (a 14-dimensional AR process, with 400 parameters to be determined) the calculation is possible and a stable process is obtained. The process can then be used to investigate some statistical properties of the SST data set and to generate synthetic SST data that could be used in very long numerical experiments with atmospheric or ocean models in which only the main features of the observed statistics of the SST must be retained. Results indicate that the synthetic SST data set seems to be of usable quality as boundary condition for the atmosphere or the ocean in climate experiments. Analysis of extreme events and extreme decades in the synthetic SST data confirms the exceptional character of the 1980s, but also provides circumstantial evidence that the 1980s were indeed within the limits of the statistics of the previously observed record. Received: 6 August 1996 / Accepted: 29 September 1997     

极轨气象卫星自动地标导航方法

该文实现了一种极轨气象卫星的自动地标导航方法。地标导航能够纠正由于姿态而引起的定位误差。首先根据当前轨道遥感卫星图像中海洋、陆地、河流等地物特征能量的概率分布情况,利用全球模板,建立地标库,然后通过最大相关系数方法计算地标偏移量,从而获得姿态偏差,之后利用计算得到的姿态偏差对遥感卫星图像重新导航,获得地理定位结果。利用FY-1D扫描辐射计的遥感数据对方法进行检验,结果表明:该文所提出的自动地标导航方法可以有效纠正由姿态而引起的定位误差,达到像素级的定位精度。该方法能够突破传统地标导航方法需要丰富的遥感卫星历史资料的限制,拓展传统地标导航方法的适应范围。该方法已在我国2008年5月发射的新一代极轨气象卫星FY-3号上得到应用,并将在下一代静止轨道气象卫星FY-4号上进一步开发。     

Regional climate model sensitivity to domain size

Regional climate models are increasingly used to add small-scale features that are not present in their lateral boundary conditions (LBC). It is well known that the limited area over which a model is integrated must be large enough to allow the full development of small-scale features. On the other hand, integrations on very large domains have shown important departures from the driving data, unless large scale nudging is applied. The issue of domain size is studied here by using the “perfect model” approach. This method consists first of generating a high-resolution climatic simulation, nicknamed big brother (BB), over a large domain of integration. The next step is to degrade this dataset with a low-pass filter emulating the usual coarse-resolution LBC. The filtered nesting data (FBB) are hence used to drive a set of four simulations (LBs for Little Brothers), with the same model, but on progressively smaller domain sizes. The LB statistics for a climate sample of four winter months are compared with BB over a common region. The time average (stationary) and transient-eddy standard deviation patterns of the LB atmospheric fields generally improve in terms of spatial correlation with the reference (BB) when domain gets smaller. The extraction of the small-scale features by using a spectral filter allows detecting important underestimations of the transient-eddy variability in the vicinity of the inflow boundary, which can penalize the use of small domains (less than 100 × 100 grid points). The permanent “spatial spin-up” corresponds to the characteristic distance that the large-scale flow needs to travel before developing small-scale features. The spin-up distance tends to grow in size at higher levels in the atmosphere.     

不同场景下的交通能见度估算模型的应用

能见度监测是交通出行安全的重要保障,尤其对机场和高速公路的大范围低能见度的监测和预警更为重要。在传统人工目测方法的基础上,以激光透射能见度仪为代表的仪器测量方法更为准确,但存在探测范围小、维护成本高、全覆盖耗资大的局限性。为了克服以上缺陷,使交通能见度的估计更为灵活、高效,本文基于机场气象站点观测数据、机场大雾以及高速公路低能见度图像,构建优化三种不同场景下的能见度估计模型,并探讨了不同模型的适用性。1)基于气象站点观测的能见度估计,运用相关系数矩阵和特征重要性分析筛选出相对湿度、温度、水平风速3个变量,并考虑昼夜分别构建三元三次多项式拟合模型,模型的决定系数(R²)可达0.9以上;2)基于机场大雾图像的能见度估计深度学习模型,利用尺度不变特征变换方法提取图像关键点的特征向量,输入全连接神经网络(fully connected neural network)模型,加快训练过程并提高模型的可解释性;3)基于高速公路图像的能见度估计的反演模型,根据暗通道先验理论和能见度测量基本方程,计算大气光亮度和透射率,并根据图像距离信息得到单目图像的能见度,该方法无须预置目标物和...     

单多普勒天气雷达反演降水粒子垂直速度Ⅰ：算法分析

针对多普勒雷达风场反演的体积速度处理(Volume Velocity Processing,VVP)方法中系数矩阵的病态问题,从数学上进行了分析和论证,对反演的误差进行了敏感性分析,并对垂直速度的求解方程作了改进。系数矩阵的条件数将随着待反演参量的不同而差别很大,通常的处理方法是舍弃量级较小的参量,通过对误差范数的分析,证明这种处理尽管存在模型误差,但能够降低求解难度和结果误差。对系数矩阵病态原因的分析发现,系数矩阵矢量的线性相关造成了矩阵奇异,当合并或舍弃线性相关项时,待反演参量会受模型误差的影响,并且这种模型误差的大小并不仅与待反演参量的量级有关,而且随着位置的不同而改变,但是部分待参量仍然可以保持准确值。在对VVP算法误差分析的基础上,分析并验证了舍弃部分参量时的反演误差,改进的算法为准确反演降水粒子的垂直速度提供了理论基础。     

A fine mesh analysis scheme designed for mountainous terrain

Summary A fine mesh analysis scheme (grid distance approximately 47 km) has been developed which takes care of mountain ridges of different height, to be able to study cyclogenesis induced by the Alps from the standpoint of observational quantitative vorticity dynamics.The method represents a 2-D univariate statistical interpolation scheme with isotropic correlation function over flat terrain but anisotropic across major mountain ridges like the Alps and Pyrrences.The checked and corrected ALPEX-IIb data set-which is the best data set for the Alpine region concerning time and space resolution as well as quality up to now-is used to produce analyses of basic and derived atmospheric variables. It is shown that structures in the upper meso-scale are resolved with pronounced gradients across mountains.With 9 Figures     

基于全视野数字图像的能见度估算方法

基于全视野数字图像的能见度估算方法是将全视野图像大气透射率与大气能见度通过曲线拟合方法建立联系,得到估算模型进行能见度估算。首先通过历史全视野图像和对应时刻的大气能见度建立样本数据集,再利用暗原色先验理论获取全视野图像大气透射率,通过多项式拟合方法建立离散样本数据集中大气透射率与对应时刻大气能见度的关系,即全视野图像能见度估算模型,最终将所需估算时刻的数字图像输入估算模型进行能见度估算,得到大气能见度数值。结果表明:建立的基于全视野数字图像能见度估算模型在环境光照均匀时大气能见度估算值与能见度实测值有较好的一致性,可作为前向散射仪的补充观测方法,弥补其采样存在的局限性。     

北斗反射信号海风探测数据分析与检定

利用导航卫星反射信号(GNSS-R)进行海风海浪探测为海洋气象提供了一种新的观测手段,数据分析及有效性检定是确定系统探测能力的重要过程.在描述北斗GEO卫星进行海风探测机理的基础上,利用北斗反射信号的相关时间进行了海风的反演.同时针对国家北斗应用项目"基于北斗导航卫星的大气海洋和空间监测预警应用示范工程"在山东进行试验和试运行期间产生的风速观测数据进行分析,并通过与标准数据的对比,确定了该方法的有效性.     

大气相对湿度资料融合及融合湿度精度检验——基于2013年1月我国中东部地区卫星和探空相对湿度数据

利用2013年1月MODIS(MODerate-resolution Imaging Spectroradiometer)的大气廓线产品计算得到大气相对湿度廓线,并与探空观测得到的相对湿度资料进行对比,发现总体上MODIS反演的相对湿度高于探空值,且在相对湿度较低区域误差较大;需要剔除有云影响的MODIS数据和相对湿度小于5%的探空数据;用平均值订正法对MODIS数据进行均一性修正,采用直接订正法、最大相关系数法开展MODIS和探空相对湿度资料融合试验,对融合结果进行分析对比和精度检验。结果表明:(1)直接订正法融合湿度在相对湿度较低区域高于探空湿度,在相对湿度较高区域低于探空湿度,最大相关系数法融合湿度在高湿区和低湿区均与探空接近;(2)融合结果精度检验表明,直接订正法融合湿度与探空湿度的平均绝对误差在850 hPa不超过10%,在925 hPa不超过12%,在1000 hPa不超过9%;采用最大相关系数法融合得到的相对湿度值在各层与探空湿度的绝对误差均不超过4%。总体来看最大相关系数法比直接订正法融合效果更好。     

On quasi–biennial oscillation in air–sea system

From the COADS (Comprehensive Ocean-Atmosphere Data Set) I and the COADS II, we got a monthly data set of sea surface temperature (SST). zonal and meridional wind components at sea level (U,V) and sea level pressure (SLP) with 4o × 4o grid system covering the period from Jan. 1950 to Dec. 1987 to study the evolutional features of the quasi-biennial oscillation (QBO) in the air-sea system. The analytic method of complex empirical orthogonal function (CEOF) is used to obtain the composite temporal sequences of amplitude (six phases for half a period) for the first and the second main components of SST, U, V and SLP. It is shown from the results that the main character-istics for different phases of the sea surface temperature anomaly’s (SSTA) QBO are warm water / cold water in the equator of the eastern Pacific (EEP). There are two warm or cold water centers of the SSTA in the EEP, which are lo-cated in the equator of the central Pacific (ECP) and the east part of the EEP. The features of the source propagation and the influence of these two centers on atmospheric circulation are discussed and it can be seen that in the forma-tion of these two centers, there are different features in oceanic and atmospheric circulations and air-sea coupled pro-cess.     

Robust GEFA Assessment of Climate Feedback to SST EOF Modes

Atmospheric response to SST variability was estimated using generalized equilibrium feedback analysis (GEFA) in the SST EOF space with synthesis data from an idealized climate model. Results show that the GEFA atmospheric response to the leading SST EOF modes is much more accurate and robust than the GEFA feedback matrix in physical space. Therefore, GEFA provides a practical method for assessing atmospheric response to large-scale SST anomalies in terms of the leading EOFs.     

一次人工增雨作业中CA-FCM与其他评估方案的比较研究

根据2002年4月5日河南省人工催化增雨作业数据,分别采用作业区域趋势对比双比分析评估方案、区域趋势相关回归评估方案、区域趋势协变量多元回归评估方案、FCM(浮动对比区历史回归)评估方案、以降水量为协变量的CA-FCM评估方案和以降水量与整层大气可降水量为协变量的CA-FCM评估方案进行效果评估。结果表明:协变量由降水量和整层大气降水量2个组成的CA-FCM方法,由于引入了不受催化影响的物理协变量作为控制因子,以及采用了聚类分析提高了对比区和影响区相关性,其评估效果最好,显著水平高于0.05;其他评估方法评估效果较差。     

Methodological Development of the Conditional Sampling Method. Part II: Quality Control Criteria of Relaxed Eddy Accumulation Flux Measurements

Determination of biosphere–atmosphere exchanges requires accurate quantification of the turbulent fluxes of energy and of a wide variety of trace gases. Relaxed Eddy Accumulation (REA) is a method that has received increasing attention in recent years, because it does not require any rapid sensor for the scalar measurements as the Eddy Correlation method (EC) does. As in all micrometeorological studies, REA measurements in the atmospheric surface layer are valid under some restrictive conditions so as to be representative of a specific ecosystem. These conditions are the homogeneity of the underlying surface, stationary and horizontally homogeneous turbulence. For most experiments these conditions are not fully satisfied. Data uncertainties can also be related to not fulfilling the method principles or to the technical characteristics of the REA system itself. In order to assess REA measurement quality, a methodological approach of data analysis is developed in this study. This methodological analysis is based on the establishment of criteria for data quality control. A set of them, deduced from the mean and turbulent flow, are called ‘Dynamic criteria’ and are designated to control the stationarity and homogeneity of the w function and the validation of Taylor’s hypothesis. A second set (‘REA operational criteria’) is designed to check the sampling process and, more precisely, the homogeneity of the negative and positive selection process throughout the sampling period. A third set of criteria (‘Chemical scalar criteria’) concerns the scalar measurements. Results of the criteria application to data measured at two different experimental sites are also presented. Cut-off limits of criteria are defined based on their statistical distribution and shown to be specific for each site. Strictness of each criterion, defined by the percentage of flagged samples, is analysed in conjunction with the meteorological conditions and atmospheric stability. It is found that flagged samples mainly correspond to neutral and stable nocturnal conditions. During daytime, nearly free convection conditions can also yield poor quality data.     

Air–sea interaction over ocean fronts and eddies

Air–sea interaction at ocean fronts and eddies exhibits positive correlation between sea surface temperature (SST), wind speed, and heat fluxes out of the ocean, indicating that the ocean is forcing the atmosphere. This contrasts with larger scale climate modes where the negative correlations suggest that the atmosphere is driving the system. This paper examines the physical processes that lie behind the interaction of sharp SST gradients and the overlying marine atmospheric boundary layer and deeper atmosphere, using high resolution satellite data, field data and numerical models. The importance of different physical mechanisms of atmospheric response to SST gradients, such as the effect of surface stability variations on momentum transfer, pressure gradients, secondary circulations and cloud cover will be assessed. The atmospheric response is known to create small-scale wind stress curl and divergence anomalies, and a discussion of the feedback of these features onto the ocean will also be presented. These processes will be compared and contrasted for different regions such as the Equatorial Front in the Eastern Pacific, and oceanic fronts in mid-latitudes such as the Gulf Stream, Kuroshio, and Agulhas Return Current.     

A coupled simple climate model and its global analysis

Summary An atmosphere-land coupled simple climate model is constructed and its climatic properties are analyzed by introducing a global analysis method, cell mapping. The simple model is a nonlinear six order simplified climate model featured with chaotic dynamics, dissipation, and forcing source, which are the main features of the real climate system. The cell mapping method is applied with this coupled system. Numerical experiments are carried out for investigating the interactions between the fast-changing atmospheric variables and slow-changing underlying surface variables. The predictability of the system is also investigated via the global analysis, with which the evolution of the system is translated to the evolution of probability transition on a Markov Chain. An effective scheme is proposed for computing the probability transition matrix for the coupled system. Predictions can be made based on the combination of dynamics and statistics. The importance of constructing the coupled model is shown by globally analyzing the predictability of the coupled system. The coupling mechanism prolongs the memorization of initial information, and then the predictability as well.     

中国区域性极端降水事件及人口经济暴露度研究

This paper tests the idea of substituting the atmospheric observations with atmospheric reanalysis when setting up a coupled data assimilation system.The paper focuses on the quantification of the effects on the oceanic analysis resulted from this substitution and designs four different assimilation schemes for such a substitution.A coupled Lorenz96 system is constructed and an ensemble Kalman filter is adopted.The atmospheric reanalysis and oceanic observations are assimilated into the system and the analysis quality is compared to a benchmark experiment where both atmospheric and oceanic observations are assimilated.Four schemes are designed for assimilating the reanalysis and they differ in the generation of the perturbed observation ensemble and the representation of the error covariance matrix.The results show that when the reanalysis is assimilated directly as independent observations,the root-mean-square error increase of oceanic analysis relative to the benchmark is less than 16%in the perfect model framework;in the biased model case,the increase is less than 22%.This result is robust with sufficient ensemble size and reasonable atmospheric observation quality(e.g.,frequency,noisiness,and density).If the observation is overly noisy,infrequent,sparse,or the ensemble size is insufficiently small,the analysis deterioration caused by the substitution is less severe since the analysis quality of the benchmark also deteriorates significantly due to worse observations and undersampling.The results from different assimilation schemes highlight the importance of two factors:accurate representation of the error covariance of the reanalysis and the temporal coherence along each ensemble member,which are crucial for the analysis quality of the substitution experiment.