三维变分同化中不同物理量对台风预报的影响

采用NMC方法统计了2006年9月的背景误差协方差,利用Bogus资料对台风进行了初始化,并对2006年“桑美”台风进行了同化试验。结果发现,同化不同的Bogus资料,所得到的台风初始场各不相同,对台风预报的影响也各不相同。对海平面气压同化,可以使台风强度明显加强,形成成熟的暖心结构;基于风速同化,对台风强度的改变较弱,对暖心结构的改进不是很明显。在同化海平面气压和风速的基础上,针对相对湿度的同化,在一定程度上可以改善台风强度预报,有利于提高台风路径的预报精度。     

非对称Bogus台风与QuikSCAT风场资料的三维变分同化应用对台风数值模拟影响研究

针对台风Bogus三维变分同化现有方法所存在的不足,引入台风移向、移速等客观因素构造出非对称台风风场模型,并与QuikSCAT风场产品相结合共同改善模式初始场。通过对比初始场增量发现,同化台风Bogus资料能较好地订正出包括高空反气旋环流在内的整层大气环流特征,而QuikSCAT风场资料则主要改善了底层环流特征,修正了台风中心位置,并且部分弥补了Bogus资料影响范围有限这一不足。接着通过设计试验方案对“云娜”台风加强阶段进行了模拟研究,结果表明：在变分同化系统的约束下,背景场、Bogus资料与QuikSCAT风场资料较协调地结合在一起,提高了台风路径及强度模拟水平,尤其是底层内外物理量的共同改进,对台风的模拟效果有明显的正效应。     

散射计和下投式探空仪资料对台风模拟的影响

为了评估同化QuikSCAT海平面气压场资料和Dropsonde资料对台风"Bilis"和"Kat-rina"模拟的影响,通过WRF-3DVAR变分同化系统将QuikSCAT海平面气压场资料和Dropsonde资料同化到WRF模式中,并以未同化任何资料和同化了QuikSCAT海平面气压场以及同化了Drop-sonde资料的数据为初始场,应用WRF模式进行模拟预报,并对得到的初始分析场和模拟预报得到的结果与台风实况报文进行了对比,得到的结论是:对台风初始时刻海平面气压场分布、初始台风中心定位、台风初始暖心结构的改善、台风路径、海平面最低气压场、海面最大风速和雷达回波的模拟方面,同化QuikSCAT海平面气压场的效果比同化Dropsonde资料明显,其中二者对海平面最低气压场的改进效果比海面最大风速的要好。但是两种资料的同化对初始相对湿度和台风强度细节变化方面都没有产生明显改善作用。同化QuikSCAT海平面气压场试验虽然仅仅同化了单层海平面气压场,但是在三维变分同化系统的动力约束作用下,也可以影响到中高层的各个要素场。     

基于海平面气压动力反演的台风涡旋初始化方法

提出了一种基于海平面气压动力反演和变分同化技术初始化数值模式台风涡旋以改进台风预报的方法.使用QuikSCAT海面风资料,针对中纬度和热带地区使用不同的边界层模式反演出与观测风场动力相适应的海平面气压;然后基于三维变分动力约束将海平面气压同化进入中尺度模式.对登陆中国的两个台风个例的初始化敏感性数值试验研究发现,初始化方法在实现台风风场与气压场的动力平衡的基础上,不仅通过调整边界层入流结构和垂直风切变明显改变了大气不稳定状态和对流触发条件,而且,也调整了台风在对流层顶附近的流出结构.尤其值得注意的是,尽管同化只是使初始台风强度略有加强,但随着模式积分时间的增长,这种模式边界层物理过程与资料同化相互耦合的方法对台风预报的影响在逐渐加强,改善了对台风登陆过程中强度维持和迅速减弱阶段的描述;并通过不同程度地调整对流层中低层和高层引导气流,改善了台风路径的数值模拟.     

改进的台风初值化方案及四维变分同化的个例试验

由于洋面上常规观测资料严重不足,使得客观分析的结果难以准确地描写初始台风的热力结构和环流特征,这是造成台风数值预报误差较大的重要原因.传统的Bogus方案虽然对台风预报有很大改进,但是仍有许多不足.文中在前人研究工作的基础上,对Bogus方案进行了改进,提高了对台风强度的描述能力,并结合四维变分资料同化的方法,利用模式动力学约束,自动生成具有非对称环流结构的台风初值,最后进行了四维变分同化试验和数值模拟试验.数值结果表明:这种结合Bogus方法和四维变分同化方法的台风初值化方案对于台风的路径模拟和强度模拟均有较好的改进.     

几种台风初始化方法及其对比实验

利用中尺度MM5V3模式及其伴随系统，运用四维变分同化方法，将Bogus台风作为观测资料进而优化台风初始场，对0422号台风个例进行了数值模拟和试验，并与传统Bogus方法和Nudging方法进行比较。结果表明，经过BDA方案优化的台风初始场更加接近实际台风的结构，有效地提高了台风路径和强度预报的准确率。     

AMSU-A资料在台风模拟中的循环同化研究

以WRF(V3. 7)中尺度模式及其三维变分同化系统(WRF-3Dvar)为平台,GFS(0. 5°×0. 5°)提供模式背景场,对AMSU-A微波辐射资料进行循环同化试验,研究台风数值模拟中AMSU-A资料的可用性及循环同化方法的可行性。以2014年第19号台风"黄蜂"为例,开展了控制试验和多组同化试验。同化试验主要包括循环同化常规观测资料和不同卫星测得的AMSU-A微波辐射资料。在对各试验初始场进行对比的基础上,比较了不同同化试验方案的模拟效果,结果表明:1) AMSU-A资料同化能有效地调整初始场,模拟的台风路径和强度都有一定的改善; 2)不同卫星探测的AMSU-A资料同化效果存在较大的差异,其中NOAA-15资料同化模拟台风"黄蜂"的路径和强度与实况最为接近; 3)循环同化是增加数值模式中资料使用量、改善台风模拟效果的有效方法。     

CMA-GFS全球预报系统中的台风初始化

在CMA-GFS(CMA Global Forecast System)全球四维变分资料同化系统(4DVar)基础上,参照BDA(Bogus Data Assimilation)方法,建立了一个全球模式台风初始化方案.该方案通过4DVar同化窗口吸收诊断处理后的1 h间隔台风中心定位及中心气压信息,利用模式动力物理约束...     

应用ATOVS资料和非对称Bogus资料对登陆台风韦帕的4DVAR数值模拟分析

前人研究中BDA方法采用的轴对称Bogus台风不能反映个别台风具体特征并弃掉了背景场的合理成分,也没有考虑大环境场的影响及湿热要素的同化。因此,提出一种充分融合分析场信息和实际观测信息并考虑副高影响的精细非对称台风Bogus方法,并在MM5的伴随同化系统中引入快速辐射传输模式RTTOV8,通过四维变分同化(4DVAR)技术,加入海面风场和气压场Bogus资料及多颗卫星多条轨道上的ATOVS红外和微波卫星辐射亮温资料并考虑Noah陆面过程方案来对登陆台风韦帕进行数值模拟,结果表明,单独同化海面Bogus资料的BDA方案可间接产生初始场非对称三维环流结构和暖心结构,但对湿度场及台风周围大环境场的调整不足,台风登陆后的路径预报改善也不明显;引入陆面过程方案弥补了Bogus资料对台风登陆后路径预报的不足;加入ATOVS资料能对湿度场及台风周围环境场做出调整,重构了大量中尺度结构信息,取得更为精细的初始台风环流和温压湿场结构,保持BDA方案路径及强度预报的优势的同时,使预报的降水强度增加,降水落区发生改变。     

非对称台风风场的动力初始化应用研究

在对比NCAR-AFWA三维Bogus(虚拟)风场及风压场方案的基础上,虚拟台风模型中的水平风场采用考虑台风移速、移向、摩擦等客观因素的非对称方案,垂直风场采用考虑高空反气旋环流的风廓线方案,然后利用Nudging动力初始化技术形成新的模式初始场.最后对"云娜"台风进行数值模拟,结果表明:通过合理引入客观因子及观测事实对台风Bogus模型进行构建,并利用同化方法使各个物理量相互协调,可以使初始场与实况更加接近,从而在一定程度上提高台风路径及强度的预报水平,并对降水分布的预报效果也有所改善.     

An Ensemble-Based Three-Dimensional Variational Assimilation Method for Land Data Assimilation

Land surface models are often highly nonlinear with model physics that contain parameterized discontinuities. These model attributes severely limit the application of advanced variational data assimilation methods into land data assimilation. The ensemble Kalman filter （EnKF） has been widely employed for land data assimilation because of its simple conceptual formulation and relative ease of implementation. An updated ensemble-based three-dimensional variational assimilation （En3-DVar） method is proposed for land data assimilation This new method incorporates Monte Carlo sampling strategies into the 3-D variational data assimilation framework. The proper orthogonal decomposition （POD） technique is used to efficiently approximate a forecast ensemble produced by the Monte Carlo method in a 3-D space that uses a set of base vectors that span the ensemble. The data assimilation process is thus significantly simplified. Our assimilation experiments indicate that this new En3-DVar method considerably outperforms the EnKF method by increasing assimilation precision. Furthermore, computational costs for the new En3-DVar method are much lower than for the EnKF method.     

大气数据同化方法的研究与应用进展

简要介绍了大气数据同化的基本思想与方法，阐述了松弛逼近法、Kalman滤波和变分约束法三种大气数据同化方法研究状况和应用进展情况。     

Numerical experiments for Typhoon Dan incorporating AMSU-A retrieved data with 3DVM

Two sets of assimilation experiments on a landfalling typhoon—Typhoon Dan(1999)over the western North Pacific were designed to compare the performances of two kinds of variational data assimilation schemes that are the 3-Dimensional Variational data assimilation of Mapped observation(3DVM)and the 4-dimensional variational data assimilation(4DVar).Results show that:(1)both the 3DVM and 4DVar successfully improved the simulations of typhoon intensity and track incorporating the satellite AMSU-A retrieved temperature and wind data into the initial conditions,and the 3DVM more significantly due to the flow-dependent of background error covariance matrix and observation error covariance matrix like 3-dimensional variational data assimilation(3DVar)circle;(2)inclusions of extra model integration iterations at each observation time in the 3DVM make it more consistent with prediction model;(3)the 3DVM is much more time-saving due to the exclusion of the adjoint technique in it.     

FY-3A卫星微波资料的集合变分混合同化试验

以2012年"北京7.21暴雨"为例,实现了集合变分混合同化方法对FY-3A的微波温度仪和微波湿度仪资料的直接同化,并与三维变分方法进行了比较。结果表明:虽然两种同化方法同化FY-3A微波资料都能改进降水模拟效果,但是与实况相比,集合变分混合同化方法改进效果更为明显,其能有效减少虚假强降水的模拟,改进强降水中心位置的模拟,SAL评分定量检验也同样表明,集合变分混合同化方法对暴雨的模拟效果要优于三维变分同化方法;无论是热力学变量还是动力学变量,集合变分同化得到的初始场均方根误差均显著小于三维变分同化的结果;两种方法同化FY-3A微波资料均能改变初始场中的各种物理量信息,但不同方法得到的同化增量大小和分布却有明显的差异:三维变分同化方法对初始场的调整区域和强度都要大于混合同化方法,且其同化增量表现出均匀和各向同性的分布特点;而利用集合信息的混合同化方法得到的同化增量分布表现为非均匀性和各向异性,具有"流依赖性"的特征,这使得初始场的分布更合理,有利于改善降水的模拟效果。     

Experiments of the WRF three-/four-dimensional variational (3/4DVAR) data assimilation in the forecasting of Antarctic cyclones

The three-/four-dimensional variational data assimilation systems (3/4DVAR) of the Weather Research and Forecasting (WRF) model were explored in the forecasting of two Antarctic synoptic cyclones, which had large influence on the Ross Sea/Ross Ice Shelf region in October 2007. A suite of variational data assimilation experiments, including regular 3DVAR, high-resolution 3DVAR, and 4DVAR experiments, were designed to evaluate their performances in weather analysis and forecasting in Antarctica. In general, both 4DVAR and high-resolution 3DVAR experiments showed better forecasting skill than regular 3DVAR experiments. High-resolution 3DVAR experiments were the most efficient in reducing the analysis errors of surface winds and temperature, and had the best performance during the first 24 h of forecasting. However, during the following forecast period, 4DVAR experiments showed either better or about comparable performance to high-resolution 3DVAR experiments. These results indicate that increasing the spatial resolution during 3DVAR is an economical approach to improving the weather analysis and forecasting over Antarctica. At the same time, the 4DVAR approach had a longer impact on forecasting than the high-resolution 3DVAR approach. Understandably, both of the variational assimilation approaches are promising techniques toward improving the regional analysis and forecasting over Antarctica.     

The impact of spatial and temporal distribution of satellite observations on tropical cyclone data assimilation: Preliminary results

Summary Preliminary work gauging the impact of varying the spatial and temporal resolution of Cloud Drift Wind (CDW) data using different assimilation techniques is presented, particularly within the framework of a generalised inverse data assimilation scheme. Results are presented for the NW Pacific and Australian regions. There were three main findings for the cases examined. Firstly, hourly and 12-hourly highdensity CDWs produced lower mean forecast errors (relative to intermittent assimilation) than those produced using operational CDWs from the Global Telecommunication System (GTS). This is consistent with Le Marshall et al. (1994). Secondly, intermittent assimilation was significantly worse than both the nudging and variational procedures. Finally, there was little improvement using the variational as opposed to the nudging scheme when using 12-hourly data insertion and operational winds from the GTS. There, the variational procedure proved to be about 4 per cent superior to the nudging. This result is of note, given the variational procedure takes about an order of magnitude longer to produce the initial field than does nudging, and should be contrasted with an earlier finding by one of the authors (LML) where the variational procedure was found to be clearly superior to the nudging approach (Bennett et al., 1993) where an enhanced CDW field was used.With 2 Figures     

Initialization and simulation of a landfalling typhoon using a variational bogus mapped data assimilation (BMDA)

Summary Recently, a new data assimilation method called “3-dimensional variational data assimilation of mapped observation (3DVM)” has been developed by the authors. We have shown that the new method is very efficient and inexpensive compared with its counterpart 4-dimensional variational data assimilation (4DVar). The new method has been implemented into the Penn State/NCAR mesoscale model MM5V1 (MM5_3DVM). In this study, we apply the new method to the bogus data assimilation (BDA) available in the original MM5 with the 4DVar. By the new approach, a specified sea-level pressure (SLP) field (bogus data) is incorporated into MM5 through the 3DVM (for convenient, we call it variational bogus mapped data assimilation – BMDA) instead of the original 4DVar data assimilation. To demonstrate the effectiveness of the new 3DVM method, initialization and simulation of a landfalling typhoon – typhoon Dan (1999) over the western North Pacific with the new method are compared with that with its counterpart 4DVar in MM5. Results show that the initial structure and the simulated intensity and track are improved more significantly using 3DVM than 4DVar. Sensitivity experiments also show that the simulated typhoon track and intensity are more sensitive to the size of the assimilation window in the 4DVar than that in the 3DVM. Meanwhile, 3DVM takes much less computing cost than its counterpart 4DVar for a given time window.     

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.     

国外几套再分析资料的对比与分析

针对目前最主要的3种再分析资料NCEP、ECMWF、JMA,从各家再分析中心所采用的同化方案、所用到的数据、质量控制方法及相关的偏差校正方法方面,进行相关介绍和对比,以便对再分析资料的特点有更为充分的了解,对我国未来再分析工作的发展起到借鉴作用。通过对比发现,各家再分析中心采用的同化方案主要为三维、四维变分方法和最优插值法。各家最主要的差别在于所选用的数据类型不同,以及所采用模式在分辨率上的差异。此外,还从经验出发简要给出了各类再分析资料在不同方面的优缺点,从而为各类再分析资料的选择使用方面提供参考。简单陈述了国内再分析工作的进展,并给出了提高我国再分析工作质量所需要关注和亟待解决的问题。     

两种样本生成方法对DRP-4DVar同化方法性能影响的比较及分析

降维投影四维变分同化方法(DRP-4DVar)利用历史预报的集合来统计背景误差协方差,并将分析变量投影到样本空间下求解代价函数,因而集合样本的质量对DRP-4DVar同化方法的性能有着重要影响.文中尝试使用三维变分(3DVar)控制变量的扰动方法来产生集合样本,并与原来的历史预报扰动方法做比较.历史预报扰动样本具有随流...