WRF模式物理参数化方案对一次局地大暴雨预报的影响研究

基于WRF(Weather Research and Forecasting)模式及其3Dvar(3-Dimentional Variational)资料同化系统,采用36、12、4 km嵌套网格进行快速更新循环同化和不同的微物理及积云对流参数化方案对比试验,对2011年5月8日鲁中一次局地大暴雨过程进行了研究。结果表明,快速更新循环同化地面观测资料是影响模式降水落区预报准确性的关键因素,不同的微物理和积云对流参数化方案主要影响降水强度预报。采用不同的微物理参数化方案和积云对流参数化方案进行降水预报对比试验表明,LIN方案和WSM6(WRF Single-Moment 6-class)微物理参数化方案对降水预报均较好,LIN方案降水预报较WSM6方案略强。4 km网格预报使用K-F (Kain-Fritsch)积云对流参数化方案或不使用积云对流参数化方案,预报的降水均较好。4 km网格使用旧的K-F积云对流参数化方案,预报的近地层大气风场偏弱,导致大气动力抬升作用偏弱,从而造成模式降水预报偏弱。     

耦合模式中海浪参数对台风浪预报的影响研究

以西北太平洋一次"双台风"共同影响下的台风浪为例,针对模式中风摄入和白帽耗散、底摩擦、波破碎、波-波非线性相互作用等海浪物理过程对台风浪预报的影响进行了敏感性试验分析。在此基础上,基于各物理过程最优参数化方案探讨了耦合模式和单独海浪模式的海浪预报性能,分析了耦合模式的海浪预报场分布特征。结果表明:不同海浪物理过程参数化对于波高预报的准确性是有所差异的。在相对最优的海浪各参数化方案组合下,无论耦合模式还是单独海浪模式都能较好地反映波高的变化和分布趋势。相比而言,耦合模式对于台风浪大值区的浪高预报要比单独海浪模式的更接近观测,且可以很好地刻画出双台风影响下浪的分布演变特征,对于西太平洋台风浪的预报具有很好的适用性。     

西北太平洋热带气旋中期路径的统计释用综合预报模式

提出一个热带气旋中期路径的统计释用综合预报模式。该模式应用ＴＣ参数，初妈时刻及未来时刻的环境场参数和导出因子，构造了多种中期天气时空惊讶的预报因子；应用数值天气预报产品的统计释用技术和多种分类预报模式的综合集成，构造了ＴＣ中期路径的客观定量预报模式。     

积云参数化的台风移动预报模式的试验

一、引 言 日本气象厅使用在业务上的台风移动预报模式是Ookochi于1978年研制的。这个模式有两个缺点,即移动性的多嵌套网格系统和规定的加热场。这样,多嵌套网格系统很难与积云参数化相配置,并且难以一次模拟较多的台风,规定的加热场不能真实地描述对流加热情 况。因此,我们研制了一个含有积云参数化的简单的系统模式。为了检验这个模式的性能,使之作为一个业务预报模式,我们进行了实时资料的试验,由新模式所作出的预报将详细地与以前使用的多嵌套网格系统所作出的预报进行比较。     

基于GPU技术的风暴潮集合预报

台风预报的准确性在风暴潮预报中起着重要作用。台风强度和路径的不确定性意味着使用集合模式来预报风暴潮。本文利用中央气象台的最优路径台风参数驱动国家海洋环境预报中心业务化的水动力学模型,开展华南沿海的风暴潮模拟,模式模拟结果与实测吻合较好。为了改进计算效率,采用CUDA Fortran 语言对模型进行了改造,改造后的模型在计算结果与原模型基本一致的基础上,计算时间缩短了99%以上。通过融合欧洲中期天气预报中心（ECWMF）的50条路径与3种可能台风强度构造出了150个台风事件,并用150个台风事件驱动改进的风暴潮数值模型,计算结果可以提供集合预报产品和概率预报产品。通过“山竹”台风风暴潮过程可以发现集合平均预报结果和概率预报结果与实测吻合较好。改进的数值模型可以运行普通工作站上,非常适合风暴潮集合预报,并且可以提供更好的决策产品。     

人工神经网络在潮汐数值预报中的应用

潮汐数值预报经过了几十年的发展,但是其预报精度并不能让人十分满意,本文试图将传统的潮汐数值预报模式与近年来发展迅速的人工神经网络相结合并改进潮汐数值预报的精度。文章建立了一个神经网络系统,采用潮汐数值模式的输出结果作为网络输入,潮位观测资料作为输出,用建立的神经网络进行训练,结果表明人工神经网络可以明显地改进潮汐数值预报的精度。     

初始化方案对有限区域海面风场数值预报模式的影响

在一个已应用于海洋要素预报的有限区域海面风场预报模式的基础上,以渤海海域为实验海区,研究初始化方案对有限区域海面风场数值预报模式的影响。对“未采用初始化”与“采用初始化”方法进行了预报实验比较,结果表明,初始化方法对有限区域海面风场模式作用明显,尤其是海面风场的开始阶段,主要体现在海风与陆风预报能力的差异上。     

福建沿海精细化台风风暴潮集合数值预报技术研究及应用

该文首先基于高级环流模型(ADCIRC)建立了一个适合台湾海峡及福建沿海区域的精细化台风风暴潮数值预报模式。利用所建立的精细化数值预报模式对影响台湾海峡及福建沿海的8次台风风暴潮个例进行了模拟,对模拟的24个站次的风暴潮增水峰值与实测值进行了对比,平均绝对误差小于15 cm;其次,为了尽可能减小由于台风路径预报误差而造成的风暴潮增减水误差,本文采用了集合数值预报技术,试报证明此方法可以在一定程度上减小风暴潮增减水误差。     

POAMA海气耦合模式对2003和2004年南海夏季风预报能力的评估

运用澳大利亚大气海洋耦合预报模式（Predictive Ocean Atmosphere Model for Australia,POAMA）的输出结果,采用泰勒图与分类统计分析方法,评估了该模式对2003和2004年南海夏季风的爆发和演变进行实时预报的能力。通过对泰勒图的分析发现,随着预报初始时间越来越接近实际的季风爆发时间,模式预报南海夏季风爆发和演变的能力越来越强。当提前1-30d预报南海夏季风时,模式能够很好地预报风场、射出长波辐射OLR（Outgoing Longwave Radiation）和降水场的空间分布,其中对风场的预报最好。通过对季风爆发指数和分类统计的分析,定量分析了模式预报南海夏季风爆发的能力,结果表明该模式对南海夏季风爆发时间有一定的预报能力,其最大预报时限可以提前10-15d左右,这与目前中期预报的上限（2周）是一致的。     

黄海水温垂直剖面的遥感反演与数值预报模型

基于黄海水温垂直剖面分层结构统计分析,建立了一个卫星遥感SST反演水温垂直剖面的参数化模式,以该模式反演数据作为一维数值预报模式的初值,同时用大气预报场资料作为预报模式的过程资料,发展了一个以卫星遥感资料作为主要输入量的黄海水温垂直剖面的数值预报模型.模型实现了利用卫星遥感资料反演预报黄海水温垂直剖面的目的,5 d时效的预报效果明显优于已往强温跃层数值预报模式.     

现代海洋/大气资料同化方法的统一性及其应用进展

海洋／大气资料同化的理论基础是用数值模式作为动力学强迫对观测信息进行提炼，或者说，从包含观测误差(噪声)的空间分布不均匀的实测资料中依据动力系统自身的演化规律(动力学方程或模式)来确定海洋／大气系统状态的最优估计。本文对主要的现代海洋／大气资料同化方法，包括最优插值(()ptimal Interpolation，简称()Ⅰ)、变分方法(3—Dimensional Variational和4—Dimensional Variational，分别简称3DVAR和4DVAR)和滤波方法(Filtering)的原理、算法设计和实际应用进行系统地回顾，并对这些资料同化方法的优缺点进行分析和讨论。在滤波框架下，所有的现代资料同化方法都被统一了：()Ⅰ和3DVAR是不随时间变化的滤波器，4DVAR和卡曼滤波是线性滤波器，即非线性滤波的退化情形；而集合滤波能构建非线性的滤波器，因为集合在某种程度上体现了系统的非高斯信息。一个非线性滤波器的主要优点是能计算和应用随时间变化的各阶误差统计距，如误差协方差矩阵。将非线性滤波器计算的随时间变化的误差协方差矩阵引入到（）Ⅰ或4DVAR中，也许能实质性地改进这些传统方法。在实际应用中，方法的优劣可能取决于所选用的数值模式和可获得的计算资源，因此需针对不同的问题选取不同的资料同化方法。由于各种资料同化方法具有统一性，因此可建立测试系统来评价这些方法，从而对各种方法获得更深入的理解，改进现有的资料同化技术，并提高人们对海洋／大气环境的预测能力。     

Impacts of TRMM SRR assimilation on the numerical prediction of tropical cyclone

1 IntroductionObservation of the tropical rainfall is crucial forthe research on tropical weather and climate. Nu-merous studies have shown that the ingestion of rain-fall data into a numerical model can have considera-ble impacts on simulation results(Kr…     

云尺度雷达资料的混合同化对台风“桑美”的数值模拟研究

本文采用基于WRFDA的集合-变分混合同化系统（En3DVAR）在云尺度分辨率下同化了雷达观测资料考察其对登陆台风"桑美"的影响。高时空分辨率的雷达径向风资料在台风登陆前的3 h同化窗内以每30 min的频率同化进WRF模式（Weather Research and Forecasting）。研究结果表明:En3DVAR试验在3 h同化窗内的均方根误差相比3DVAR试验改进显著,这可能得益于混合同化系统中提供的"流依赖"的集合协方差信息。系统性的诊断分析表明En3DVAR试验在台风内核区产生了较为明显正温度增量,对台风内核区的热力和动力结构均有较好调整,而3DVAR则在台风内核区产生了负温度增量;相比3DAVR试验,En3DVAR在采用了"流依赖"的集合协方差信息后还可以对背景场上的台风的位置进行系统性的偏差订正。总体而言,En3DVAR试验预报的台风路径和强度相比3DVAR改进显著,其正效果主要来源于混合背景误差协方差中的"流依赖"集合协方差信息。     

TRMM海表降水率同化在热带气旋Danas(2001) 数值模拟中的应用研究

研究了TRMM/TMI海表降水率资料的四维变分同化在热带气旋(TC)数值模拟中的作用.使用中尺度气象模式MM5设计了若干数值试验模拟了TC Danas(2001)由热带低压初生到台风生成的发展过程.在满足MM5模式动力约束的前提下,将TRMM海表降水率资料直接同化进入较高分辨率(18 km)的模式初始场.结果表明,使用MM5模式的4D-VAR同化系统直接同化TRMM/TMI海表降水率资料是可行的.这种做法提高了TRMM资料的利用率,不仅在模式初始场中加入了更多实测信息,而且避免了两次同化(1DVAR+4DVAR)可能引起的误差.直接同化TRMM资料通过调整气压、温度、湿度等要素初始场,改善了模式对热带气旋结构(如暖心、涡度、散度)的描述和降水的模拟.在此基础上,同化不仅改进了对Danas强度的模拟,而且成功地模拟了热带气旋环境场的演变过程,因而改进了路径的模拟.     

A comparison between 3DVAR and EnOI techniques for satellite altimetry data assimilation

Two conceptually different assimilation schemes, three dimensional variational (3DVAR) assimilation and Ensemble Optimum Interpolation (EnOI) are compared in the context of satellite altimetric data assimilation. Similarities and differences of the two schemes are briefly discussed and their impacts on the model simulation are investigated.With a tropical Pacific ocean model, two assimilation experiments of sea level anomaly (SLA) data from TOPEX/Poseidon are performed for 5 years from 1997 to 2001. Annual mean states of temperature and salinity fields are compared with analysis data and some independent observations. It is found that EnOI generally produces moderate improvements on both temperature and salinity fields, while changes induced by 3DVAR assimilation are strong and vary remarkably in different areas. For instance, 3DVAR tends to excessively modify the temperature field along the thermocline depth and even deteriorate the simulation, but it is more effective than EnOI below the thermocline depth. However, for the salinity field 3DVAR outperforms EnOI nearly for almost the whole layer. As the difference relative to the WOA01 analysis is compared, it is apparently reduced to below 0.3 psu in most areas in the 3DVAR experiment. On the other hand, the pattern of difference in the EnOI experiment resembles that of the simulation and the magnitude is only diminished to some extent. One advantage of EnOI is that it yields more consistent improvements even in areas where there are large model errors. It is more reliable than 3DVAR in such a sense. It is also revealed that the T–S relation plays a very important role in altimetric data assimilation. Further, the distinct performance of the two schemes can be partly accounted for by their inherent assumptions and settings.     

4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)

We present the background, development, and preparation of a state-of-the-art 4D variational (4DVAR) data assimilation system in the Regional Ocean Modeling System (ROMS) with an application in the Intra-Americas Sea (IAS). This initial application with a coarse model shows the efficacy of the 4DVAR methodology for use within complex ocean environments, and serves as preparation for deploying an operational, real-time assimilation system onboard the Royal Caribbean Cruise Lines ship Explorer of the Seas. Assimilating satellite sea surface height and temperature observations with in situ data from the ship in 14 day cycles over 2 years from January 2005 through March 2007, reduces the observation-model misfit by over 75%. Using measures of the Loop Current dynamics, we show that the assimilated solution is consistent with observed statistics.     

4DVAR data assimilation in the Intra-Americas Sea with the Regional Ocean Modeling System (ROMS)

We present the background, development, and preparation of a state-of-the-art 4D variational (4DVAR) data assimilation system in the Regional Ocean Modeling System (ROMS) with an application in the Intra-Americas Sea (IAS). This initial application with a coarse model shows the efficacy of the 4DVAR methodology for use within complex ocean environments, and serves as preparation for deploying an operational, real-time assimilation system onboard the Royal Caribbean Cruise Lines ship Explorer of the Seas. Assimilating satellite sea surface height and temperature observations with in situ data from the ship in 14 day cycles over 2 years from January 2005 through March 2007, reduces the observation-model misfit by over 75%. Using measures of the Loop Current dynamics, we show that the assimilated solution is consistent with observed statistics.     

基于ROMS和4DVAR的沿轨与网格化SSH数据同化效果评价

Remote sensing products are significant in the data assimilation of an ocean model. Considering the resolution and space coverage of different remote sensing data, two types of sea surface height(SSH) product are employed in the assimilation, including the gridded products from AVISO and the original along-track observations used in the generation. To explore their impact on the assimilation results, an experiment focus on the South China Sea(SCS) is conducted based on the Regional Ocean Modeling System(ROMS) and the four-dimensional variational data assimilation(4 DVAR) technology. The comparison with EN4 data set and Argo profile indicates that, the along-track SSH assimilation result presents to be more accurate than the gridded SSH assimilation, because some noises may have been introduced in the merging process. Moreover, the mesoscale eddy detection capability of the assimilation results is analyzed by a vector geometry–based algorithm. It is verified that, the assimilation of the gridded SSH shows superiority in describing the eddy's characteristics, since the complete structure of the ocean surface has been reconstructed by the original data merging.     

GPM微波成像仪资料同化对台风“麦德姆”路径预报的影响研究

基于中尺度数值模式WRF及其三维变分同化系统,自主构建了新的探测仪器GMI(Global Precipitation Measurement (GPM) Microwave Imager)的同化模块。本文以2014年太平洋台风季中台风“麦德姆”为例,实现了GMI资料在登陆台风中的有效应用。试验结果表明:晴空条件下GMI资料同化能够对模式背景场中的台风位置进行有效修正。与没有同化该资料的控制试验相比,同化GMI微波成像仪资料可以有效改进台风暖心结构的分析,同时使得台风涡旋环流结构增强,并进而提高了对台风路径的预报水平。     

Towards a true 4‐dimensional data assimilation algorithm: application of a cycling representer algorithm to a simple transport problem

3‐dimensional variational algorithms are widely used for atmospheric data assimilation at the present time, particularly on the synoptic and global scales. However, mesoscale and convective scale phenomena are considerably more chaotic and intermittent and it is clear that true 4‐dimensional data assimilation algorithms will be required to properly analyze these phenomena. In its most general form, the data assimilation problem can be posed as the minimization of a 4‐dimensional cost function with the forecast model as a weak constraint. This is a much more difficult problem than the widely discussed 4DVAR algorithm where the model is a strong constraint. Bennett and collaborators have considered a method of solution to the weak constraint problem, based on representer theory. However, their method is not suitable for the numerical weather prediction problem, because it does not cycle in time. In this paper, the representer method is modified to permit cycling in time, in a manner which is entirely internally consistent. The method was applied to a simple 1‐dimensional constituent transport problem where the signal was sampled (perfectly and imperfectly) with various sparse observation network configurations. The cycling representer algorithm discussed here successfully extracted the signal from the noisy, sparse observations