The Structure of Background-error Covariance in a Four-dimensional Variational Data Assimilation System: Single-point Experiment

A four dimensional variational data assimilation (4DVar) based on a dimension-reduced projection (DRP-4DVar) has been developed as a hybrid of the 4DVar and Ensemble Kalman filter (EnKF) concepts. Its good flow-dependent features are demonstrated in single-point experiments through comparisons with adjoint-based 4DVar and three-dimensional variational data (3DVar) assimilations using the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5). The results reveal that DRP-4DVar can reasonably generate a background error covariance matrix (simply B-matrix) during the assimilation window from an initial estimation using a number of initial condition dependent historical forecast samples. In contrast, flow-dependence in the B-matrix of MM5 4DVar is barely detectable. It is argued that use of diagonal estimation in the B-matrix of the MM5 4DVar method at the initial time leads to this failure. The experiments also show that the increments produced by DRP-4DVar are anisotropic and no longer symmetric with respect to observation location due to the effects of the weather trends captured in its B-matrix. This differs from the MM5 3DVar which does not consider the influence of heterogeneous forcing on the correlation structure of the B-matrix, a condition that is realistic for many situations. Thus, the MM5 3DVar assimilation could only present an isotropic and homogeneous structure in its increments.     

GRAPES全球四维变分同化系统极小化算法预调节

在进行多次外循环更新的增量分析框架下,前一次极小化迭代过程中产生的信息可提供给下一次极小化做预调节。该文在GRAPES全球四维变分同化系统中对极小化算法——L-BFGS算法实施了这种预调节,通过全观测的个例试验和批量试验进行评估,发现进行预调节后L-BFGS算法的收敛效率得到明显提高,而且在1个月的循环试验中表现十分稳定。该工作可以帮助GRAPES全球四维变分同化系统有效减少极小化的迭代次数,有利于满足业务化运行的时效要求。另外,间隔6 h和间隔24 h的两次4DVar分析对应的海森矩阵变化不大,因此,前一时刻极小化过程产生的信息提供给后一时刻的极小化进行预调节也有一定效果。     

一种基于历史样本投影的三维变分同化方法

对于中尺度数值天气预报来说,初始条件的准确与否已成为影响预报技巧的主要因素之一。现有的大气观测资料在时空分布上的不均匀,以及存在的观测误差,使得我们必须引进资料同化方法,为中尺度数值模式提供最优的初始场。由于传统的三维变分同化(3DVar)方法缺乏模式约束以及背景误差协方差矩阵(B矩阵)不具有流依赖性,因此本文提出一种基于历史样本投影的3DVar(HSP-3DVar)方法,它不仅具有流依赖的B矩阵,而且比传统的3DVar简单易行。为了评价HSP-3DVar的同化性能,我们基于区域暴雨预报模式AREM(Advanced Regional Eta Model)对其进行了观测系统模拟试验(OSSE),结果表明:HSP-3DVar能够有效融合观测信息,模式初值在各层的均方根误差都显著地降低。     

A New Approach to Data Assimilation

A significant attempt to design a timesaving and efficient four-dimensional variational data assimilation (4DVar) has been made in this paper, and a new approach to data assimilation, which is noted as 'three-dimensional variational data assimilation of mapped observation (3DVM)' is proposed, based on the new concept of mapped observation and the new idea of backward 4DVar. Like the available 4DVar, 3DVM produces an optimal initial condition (IC) that is consistent with the prediction model due to the inclusion of model constraints and best fits the observations in the assimilation window through the model solution trajectory. Different from the 4DVar, the IC derived from 3DVM is located at the end of the assimilation window rather than at the beginning conventionally. This change greatly reduces the computing cost for the new approach, which is almost the same as that of the three-dimensional variational data assimilation (3DVar). Especially, such a change is able to improve assimilation accuracy because it does not need the tangential linear and adjoint approximations to calculate the gradient of cost function. Therefore, in numerical test, the new approach produces better IC than 4DVar does for 72-h simulation of TY9914 (Dan), by assimilating the three-dimensional fields of temperature and wind retrieved from the Advanced Microwave Sounding Unit-A (AMSU-A) observations. Meanwhile, it takes only 1/7 of the computing cost that the 4DVar requires for the same initialization with the same retrieved data.     

The REM Adjoint System and Its 4DVar Data Assimilation Experiments

The Regional Eta-coordinate Model(REM) has performed well in forecasting heavy rainfalls in China in recent years.A four-dimensional variational assimilation system(4DVar) is developed to improve the forecast skill of the REM.The tangent linear model and adjoint model codes are written according to thecode to coderule,and the establishment of the REM adjoint modeling system is introduced in detail in this paper.The tangent linear and adjoint models of the REM are validated against the observational data,...     

System of Multigrid Nonlinear Least-squares Four-dimensional Variational Data Assimilation for Numerical Weather Prediction (SNAP): System Formulation and Preliminary Evaluation

A new forecasting system—the System of Multigrid Nonlinear Least-squares Four-dimensional Variational(NLS-4DVar) Data Assimilation for Numerical Weather Prediction(SNAP)—was established by building upon the multigrid NLS-4DVar data assimilation scheme, the operational Gridpoint Statistical Interpolation(GSI)-based data-processing and observation operators, and the widely used Weather Research and Forecasting numerical model. Drawing upon lessons learned from the superiority of the operational GS...     

A New Global Four-Dimensional Variational Ocean Data Assimilation System and Its Application

A four-dimensional variational data assimilation （4DVar） system of the LASG/IAP Climate Ocean Model, version 1.0 （LICOM1.0）, named LICOM-3DVM, has been developed using the three-dimensional variational data assimilation of mapped observation （3DVM）, a 4DVar method newly proposed in the past two years. Two experiments with 12-year model integrations were designed to validate it. One is the assimilation run, called ASSM, which incorporated the analyzed weekly sea surface temperature （SST） fields from Reynolds and Smith （OISST） between 1990 and 2001 once a week by the LICOM-3DVM. The other is the control run without any assimilation, named CTL. ASSM shows that the simulated temperatures of the upper ocean （above 50 meters）, especially the SST of equatorial Pacific, coincide with the Tropic Atmosphere Ocean （TAO） mooring data, the World Ocean Atlas 2001 （WOA01） data and the Met Office Hadley Centre＇s sea ice and sea surface temperature （HadISST） data. It decreased the cold bias existing in CTL in the eastern Pacific and produced a Nifio index that agrees with observation well. The validation results suggest that the LICOM-3DVM is able to effectively adjust the model results of the ocean temperature, although it＇s hard to correct the subsurface results and it even makes them worse in some areas due to the incorporation of only surface data. Future development of the LICOM-3DVM is to include subsurface in situ observations and satellite observations to further improve model simulations.     

基于历史样本投影的四维变分陆面数据同化方法及其初步应用

法面临着计算量上的挑战。本研究将一种历史样本投影的四维变分同化方法(Historical-Sample-Projection4DVar,简写为HSP-4DVar)应用于陆面数据同化,建立起CoLM陆面模型的HSP-4DVar系统。相比其他四维变分同化方法,HSP-4DVar的分析值是显式求解,不需要编写和使用伴随模式,从而大大节省了计算量,是一种易于实现的同化方案。通过同化56个月的土壤湿度观测数据表明,新的陆面同化系统不仅省时,而且能够有效吸取观测信息,使得同化后的均方根误差显著降低,各层土壤湿度模拟都有所改善,陆表1000mm层的改善最为明显。     

Rainfall Assimilation Using a New Four-Dimensional Variational Method: A Single-Point Observation Experiment

Accurate forecast of rainstorms associated with the mei-yu front has been an important issue for the Chinese economy and society. In July 1998 a heavy rainstorm hit the Yangzi River valley and received widespread attention from the public because it caused catastrophic damage in China. Several numerical studies have shown that many forecast models, including Pennsylvania State University National Center for Atmospheric Research’s fifth-generation mesoscale model (MM5), failed to simulate the heavy precipitation over the Yangzi River valley. This study demonstrates that with the optimal initial conditions from the dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar) system, MM5 can successfully reproduce these observed rainfall amounts and can capture many important mesoscale features, including the southwestward shear line and the low-level jet stream. The study also indicates that the failure of previous forecasts can be mainly attributed to the lack of mesoscale details in the initial conditions of the models.     

台风数值模拟中bogus涡旋四维变分同化的研究

研究用MM5四维变分同化系统同化bogus涡旋变量对台风数值模拟的影响,对2002年13号台风Phanfone进行48h预报,结果表明：用四维变分方法同化bogus涡旋变量效果优于在背景场中直接植入模型涡旋的传统bogus技术。同时同化bogus涡旋的风、气压、温度和比湿效果不如只同化气压和其中任一变量。同化bogus涡旋的气压和比湿,能够调整模式初始场的其他变量,得到结构更清晰、与环境场更协调的初始涡旋,从而在一定程度上提高了台风Phanfone路径和强度预报的准确性。     

基于VDRAS的快速更新雷达四维变分分析系统

基于雷达资料快速更新四维变分同化 (RR4DVar) 技术和三维数值云模式,初步研发了一个针对对流尺度数值模拟的快速更新雷达四维变分分析系统。系统通过对京津冀6部多普勒天气雷达资料进行RR4DVar同化,并融合5 min自动气象站观测和中尺度数值模式结果,可快速分析得到12~18 min更新的低层大气三维动力、热力场的对流尺度结构特征。针对2009年7月22日发生在京津冀的一次强风暴个例,通过一系列敏感性试验,并利用局地加密资料进行检验对比,表明有效的雷达资料RR4DVar同化及自动气象站和中尺度模式资料融合方案、恰当的中尺度背景场设置和动力约束方法是获得合理结果的关键。研究也表明:恰当的系统配置能够模拟出与对流生消发展密切相关的近风暴环境特征,包括低层入流、垂直风切变、低层辐合上升和暖舌等,以及风暴自身形成的冷池、出流等与风暴演变密切相关的对流尺度结构。     

陆面气温资料的一种推广三维变分同化方法及其在简化情形的检验

发展了一种同化窗口为1个月的陆面气温推广三维变分资料同化方法,并建立了逐日资料到月平均资料的观测算子.作为对该同化方法的初步检验,以2000年1月中国陆地区域时间分辨率低、空间分辨率较高且在高原地区与台站观测资料比较接近的CRU(Climate Research Unit)月平均陆面气温资料作为观测,以时间分辨率高、空间分辨率较低且在高原地区与台站资料有较大误差的NCEP逐日气温资料作为背景场,采用背景误差协方差矩阵(简称B矩阵)的两种简化对角形式进行同化试验,得到高时空分辨率的格点陆面气温分析场,并进行对比分析和均方根误差检验.结果显示,由两种简化形式得到的逐日资料和月平均资料的质量均得到改善,在我国东南和中部大部分地区与台站观测基本一致,在青藏高原、新疆等台站稀疏地区与地形对应良好,为M-SDGVM模型或其他陆面过程模式高质量陆面气象驱动场的准备提供了一种新的手段.尤其在B矩阵中考虑了方差逐日变化的简化形式所得到的分析场整体上要优于不考虑的,这为最终在B矩阵中考虑协方差(即对推广三维变分同化不作简化)进一步改进同化效果打下了基础.     

基于雷达资料四维变分同化和三维云模式对一次超级单体风暴发展维持热动力机制的模拟分析

利用三维云尺度数值模式和雷达资料快速更新循环四维变分同化(4DVar)技术,对京津冀地区一次强降水超级单体风暴发展演变的热动力机制进行了数值模拟和结果分析,并结合雷达、加密探空和自动站资料,揭示了快速变化的近风暴大气环境及风暴自身的热动力三维特征对超级单体形成、发展和演变的影响.雷达回波观测分析表明,这是一次由多单体合...     

云导风资料同化在伴随模式同化系统中的应用

以一次发生在长江流域的暴雨过程为例,设计了几种数值试验方案,并进行模拟。结果表明,MM5伴随模式同化系统能有效改善初始场与模式的协调能力,提高模式对于降水场和其它要素场的预报;使用云导风资料修正初始场后直接模拟的效果比未使用时直接模拟的效果要好,对部分区域的强降水预报精度有一定程度的改善;使用伴随模式同化系统后,加入云导风资料的同化试验对其它要素的改善与直接同化常规资料的效果相比,改善优势不明显,但从各要素的误差来看,对于风场的改善最好。     

Assimilation of Doppler Radar Data with an Ensemble 3DEnVar Approach to Improve Convective Forecasting

An ensemble three-dimensional ensemble-variational(3DEnVar)data assimilation(E3DA)system was developed within the Weather Research and Forecasting model’s 3DVar framework to assimilate radar data to improve convective forecasting.In this system,ensemble perturbations are updated by an ensemble of 3DEnVar and the ensemble forecasts are used to generate the flow-dependent background error covariance.The performance of the E3DA system was first evaluated against one experiment without radar DA and one radar DA experiment with 3DVar,using a severe storm case over southeastern China on 5 June 2009.Results indicated that E3DA improved the quantitative forecast skills of reflectivity and precipitation,as well as their spatial distributions in terms of both intensity and coverage over 3DVar.The root-mean-square error of radial velocity from 3DVar was reduced by E3DA,with stronger low-level wind closer to observation.It was also found that E3DA improved the wind,temperature and water vapor mixing ratio,with the lowest errors at the surface and upper levels.3DVar showed moderate improvements in comparison with forecasts without radar DA.A diagnosis of the analysis revealed that E3DA increased vertical velocity,temperature,and humidity corresponding to the added reflectivity,while 3DVar failed to produce these adjustments,because of the lack of reasonable cross-variable correlations.The performance of E3DA was further verified using two convective cases over southern and southeastern China,and the reflectivity forecast skill was also improved over 3DVar.     

风廓线雷达资料对GRAPES_MESO数值预报系统影响的初步研究

针对一个南方切变线系统降水个例,通过观测系统模拟试验(OSSEs)对我国拟建风廓线雷达观测网内的不同类型风廓线雷达观测资料在GRAPES_MESO系统中的影响、对风廓线雷达观测和探空观测及两者混合使用时在GRAPES_MESO系统中的影响差异进行了初步分析和讨论。试验结果表明,在GRAPES_MESO系统中,风廓线雷达资料对500 hPa高度以下水平风速分析场的修正作用明显优于探空资料;风廓线雷达资料对水平风速分析场的影响高度极限大约在300 hPa附近,经过6h的传播,其影响可以向上继续传播至250 hPa以上高度;在300 hPa高度以下,同化对流层Ⅱ型风廓线雷达资料对水平风速分析场的影响比边界层型风廓线雷达资料略大;单独同化风廓线雷达资料对降水预报的贡献较弱,与探空观测混合使用时有助改进降水预报。     

卫星资料变分同化在一次中尺度强暴雨模拟中的应用

利用ATOVS资料和常规观测资料, 采用GRAPES 3D-Var同化系统和中尺度数值模式MM5设计了仅同化常规观测资料的NOATOVS试验和同化常规观测资料及ATOVS辐射率资料的ATOVS试验, 对2004年6月22—24日长江中下游和西南地区东部的特大暴雨进行了分析和模拟。结果表明:直接同化ATOVS辐射率资料获得的分析场可以有效改进对流层温、湿场分布, 对风场也有一定的影响。对比试验结果表明:ATOVS试验可以较好地模拟出暴雨天气形势、主要影响系统, 对降雨的落区、强度也有较好的反映, 模拟的局地暴雨强度与实际降雨量基本一致, 同化卫星资料的改善效果较为明显。即同化ATOVS资料对于改进中尺度局地暴雨过程模拟效果是可行的。     

The REMO Ocean Data Assimilation System into HYCOM （RODAS_H）： General Description and Preliminary Results

The first version of the Brazilian Oceano- graphic Modeling and Observation Network （REMO） ocean data assimilation system into the Hybrid Coordi- nate Ocean Model （HYCOM） （RODAS H） has recently been constructed for research and operational purposes. The system is based on a multivariate Ensemble Optimal Interpolation （EnOI） scheme and considers the high fre- quency variability of the model error co-variance matrix. The EnOl can assimilate sea surface temperature （SST）, satellite along-track and gridded sea level anomalies （SLA）, and vertical profiles of temperature （T） and salinity （S） from Argo. The first observing system experiment was carried out over the Atlantic Ocean （78°S-50°N, 100°W-20°E） with HYCOM forced with atmospheric reanalysis from 1 January to 30 June 2010. Five integra- tions were performed, including the control run without assimilation. In the other four, different observations were assimilated： SST only （A SST）; Argo T-S profiles only （AArgo）; along-track SLA only （A_SLA）; and all data employed in the previous runs （A_All）. The A_SST, A_Argo, and A_SLA runs were very effective in improv- ing the representation of the assimilated variables, but they had relatively little impact on the variables that were not assimilated. In particular, only the assimilation of S was able to reduce the deviation of S with respect to ob- servations. Overall, the A_All run produced a good analy- sis by reducing the deviation of SST, T, and S with respect to the control run by 39%, 18%, and 30%, respectively, and by increasing the correlation of SLA by 81%.     

AMSU资料变分同化及在暴雨数值模拟中的应用研究

在中尺度数值模拟中,利用中国气象科学研究院数值天气预报创新基地开发的GRAPES三维变分同化系统,对AMSU-A/B微波遥感资料进行了同化试验,研究了这种资料在我国夏季暴雨数值预报中的作用。以2003年7月4日的一次暴雨过程为例,分析了同化结果及模拟结果,结果表明:(1)单独同化AMSU-A资料主要改进了初始温度场,而单独同化AMSU-B资料主要改进了初始湿度场;(2)无论是同化AMSU-A资料还是同化AMSU-B资料,对暴雨预报都有一定的改进作用,但是同化AMSU-B资料的改进作用更明显;(3)同时同化AMSU-A/B资料比只使用其中一种资料的模拟效果好,可以更好地改进模拟的暴雨落区及强度,使结果与实况更加接近。使用AMSU资料,对我国夏季暴雨数值预报有改进作用。     

An Economical Approach to Flow-Adaptive Moderation of Spurious Ensemble Correlations and Its Application in the Proper Orthogonal Decomposition-Based Ensemble Four Dimensional Variational Assimilation Method

The purpose of this study is to describe an economical approach to an existing adaptive localization technique and its implementation in the proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar). Owing to the applications of the sparse processing and EOF decomposition techniques, the computational costs of this proposed sparse flow-adaptive moderation(SFAM) localization scheme are significantly reduced. The effectiveness of PODEn4 DVar with SFAM localization is demonstrated by using the Lorenz-96 model in comparison with the Smoothed ENsemble Correlations Raised to a Power(SENCORP) and static localization schemes, separately. The performance of PODEn4 DVar with SFAM localization shows a moderate improvement over the schemes with SENCORP and static localization, with low computational costs under the imperfect model.