共查询到20条相似文献，搜索用时 31 毫秒
1.
2.
3.
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。 相似文献
4.
TIAN Xiang-Jun 《大气和海洋科学快报》2014,(1)
The purpose of this paper is to provide a robust and flexible implementation of a proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar) through Rlocalization.With R-localization,the implementation of the local PODEn4DVar analysis can be coded for parallelization with enhanced assimilation precision.The feasibility and effectiveness of the PODEn4DVar local implementation with R-localization are demonstrated in a two-dimensional shallow-water equation model with simulated observations(OSSEs) in comparison with the original version of the PODEn4DVar with B-localization and that without localization.The performance of the PODEn4DVar with localization shows a significant improvement over the scheme with no localization,particularly under the imperfect model scenario.Moreover,the R-localization scheme is capable of outperforming the Blocalization case to a certain extent.Further,the assimilation experiments also demonstrate that PODEn4DVar with R-localization is most efficient due to its easy parallel implementation. 相似文献
5.
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 GSI analysis system, for its various observation operators and the ability to assimilate multiple-source observations, SNAP adopts GSI-based data-processing and observation operator modules to compute the observation innovations. The multigrid NLS-4DVar assimilation framework is used for the analysis, which can adequately correct errors from large to small scales and accelerate iteration solutions. The analysis variables are model state variables, rather than the control variables adopted in the conventional 4DVar system. Currently, we have achieved the assimilation of conventional observations, and we will continue to improve the assimilation of radar and satellite observations in the future. SNAP was evaluated by case evaluation experiments and one-week cycling assimilation experiments. In the case evaluation experiments, two six-hour time windows were established for assimilation experiments and precipitation forecasts were verified against hourly precipitation observations from more than 2400 national observation sites. This showed that SNAP can absorb observations and improve the initial field, thereby improving the precipitation forecast. In the one-week cycling assimilation experiments, six-hourly assimilation cycles were run in one week. SNAP produced slightly lower forecast RMSEs than the GSI 4DEnVar (Four-dimensional Ensemble Variational) as a whole and the threat scores of precipitation forecasts initialized from the analysis of SNAP were higher than those obtained from the analysis of GSI 4DEnVar. 相似文献
6.
在基于本征正交分解POD（Proper Orthogonal Decomposition）的集合四维变分同化方法（POD4DEnVar）建立的雷达资料同化系统（PRAS）的基础上,本文利用非线性最小二乘法的集合四维变分同化方法（NLS-4DVar）对PRAS进行改进,解决PRAS在高度非线性情况下的适应性问题,建立了新的雷达资料同化系统（NRAS）。通过观测系统模拟试验OSSEs（Observing System Simulation Experiments）和两次实际暴雨同化试验（2010年7月8日,中国中部地区;2014年3月30日,中国华南地区）对NRAS进行检验,并与PRAS的同化结果进行了对比。结果表明:无论是OSSEs还是实际雷达资料的同化,相对于PRAS,NRAS能够进一步提高同化效果。通过增加迭代的次数,NRAS能够有效地调整初始场的风场和水汽场,进一步提高了降水强度和位置的预报精度。但随着迭代次数的增加,对初始场的调整变小,进而对降水预报效果的改进也减小。试验结果表明NRAS能够有效解决PRAS在高度非线性情况下的应用问题,通过有限次数的迭代,即可得到近似收敛的结果。因而NRAS有望在数值预报中更有效地同化雷达资料,提高中小尺度天气的预报水平。 相似文献
7.
We applied the multigrid nonlinear least-squares four-dimensional variational assimilation(MG-NLS4DVar) method in data assimilation and prediction experiments for Typhoon Haikui(2012) using the Weather Research and Forecasting(WRF) model. Observation data included radial velocity(V_r) and reflectivity(Z) data from a single Doppler radar, quality controlled prior to assimilation. Typhoon prediction results were evaluated and compared between the NLS-4DVar and MG-NLS4DVar methods. Compared with a forecast that began with NCEP analysis data, our radar data assimilation results were clearly improved in terms of structure, intensity, track, and precipitation prediction for Typhoon Haikui(2012). The results showed that the assimilation accuracy of the NLS-4DVar method was similar to that of the MG-NLS4DVar method,but that the latter was more efficient. The assimilation of V_r alone and Z alone each improved predictions of typhoon intensity, track, and precipitation; however, the impacts of V_r data were significantly greater that those of Z data.Assimilation window-length sensitivity experiments showed that a 6-h assimilation window with 30-min assimilation intervals produced slightly better results than either a 3-h assimilation window with 15-min assimilation intervals or a 1-h assimilation window with 6-min assimilation intervals. 相似文献
8.
敏感性试验表明集合变换卡尔曼滤波(Ensemble Transform Kalman Filter,ETKF)方法在混合(Hybrid)同化过程中易受观测资料数量变化的影响而产生较大程度的协方差震荡,从而可能导致系统不稳定。为设计一种简便、稳定的Hybrid同化系统,构建了一种基于物理控制变量扰动及多物理参数化方案的Hybrid同化及预报系统。本系统随着循环的进行,不断对Hybrid同化分析场进行控制变量扰动得到集合成员初始场,并且对各集合成员采用不同物理参数化方案以更合理地表征背景场的误差特征。连续10 d的循环同化及预报试验表明,本文同化方案效果明显优于三维变分方案,动力场的整体同化和预报效果与ETKF方案基本相当。本方案相比于ETKF方法不受观测波动影响,在没有经任何参数调试情况下,取得了良好同化和预报效果,为Hybrid同化的便捷运行提供了一种稳定可靠的手段。 相似文献
9.
为了建立一个应用于区域数值预报的四维变分资料同化（4DVar）系统,在近期开发的扰动预报模式GRAPES_PF基础上,开发完善增量四维变分同化系统框架。该框架中暂不包含物理过程（长短波辐射、边界层过程、对流参数化和云微物理等）。对比业务使用的GRAPES 3DVar系统,增加了温度控制变量。将无量纲Exner气压与流函数的线性风压平衡方程直接在地形追随垂直坐标面上求解,且通过广义共轭余差法（GCR）求解扰动亥姆霍兹（Helmholtz）伴随方程。利用人造“探空”资料对2015年10月台风“彩虹”进行了理想数值试验。试验结果表明,所开发的扰动四维变分同化框架得到了预期的结果,即同化更多资料并反复受到模式约束的四维变分同化系统能有效改善初值质量,进而改善区域数值预报。建立的区域四维变分同化框架合理可行,为进一步发展包含完整物理过程的区域四维变分同化系统奠定了研究基础。 相似文献
10.
将国内多普勒天气雷达的反射率因子及径向风资料引入ARPS-3DVar同化系统进行同化,针对2006年登陆浙江苍南并造成严重影响的超强台风“桑美”,探讨多普勒雷达资料同化对台风模拟初始场和预报场的改进作用。结果表明:利用ARPS-3DVar同化雷达资料可以明显改善6 h同化窗口内的降水、风场和回波结构,并能提高模式对中尺度雨团位置、强度的模拟能力;雷达资料初始场同化后模拟的台风涡旋和台风眼结构与位置更加接近实况,各物理量空间分布结构更具有明显中尺度特征,从而改善了台风路径和降水的预报。但模拟过程中台风强度模拟偏弱,有待进一步改进。 相似文献
11.
GRAPES的新初始化方案 总被引：5，自引：2，他引：3
四维变分同化由于引入预报模式作为一项约束,理论上它的分析场已经具有较好的平衡性,但实施时还会有诸多因重力波导致的高频振荡过程,因此,四维变分同化(4DVar)分析仍需要初始化。文中描述了GRAPES全球四维变分同化系统(GRAPES-4DVar)的新初始化方案的科学设计、公式演绎以及试验结果。GRAPES-4DVar的新初始化方案采用数字滤波方案作为代价函数的一项约束控制重力波引发的不平衡结构,约束强加在分析增量上与极小化迭代过程同步进行。新的初始化方案是变分同化系统的一部分,数字滤波的积分时间与4DVar的同化时间窗一致,不会对4DVar产生额外的计算资源消耗;并能适应长时间窗的同化,不会因为时间窗的延长而削弱慢波过程。新初始化方案中,模式轨迹的光滑程度可在变分同化中通过重力波控制项的权重系数方便控制。GRAPES全球四维变分同化的理想和循环同化批量试验都表明,在四维变分同化中,重力波的控制依然非常重要,具有初始化的GRAPES试验,无论分析还是预报技巧都较无初始化的有明显优势。与以前分析和滤波独立实施的旧初始化方案相比,新方案的分析和预报效果略优,同时有效地节省循环同化系统的运行时间,这对四维变分同化来说非常重要。 相似文献
12.
利用自主构建的基于风暴尺度的WRF-En SRF系统同化模拟多普勒雷达资料,讨论了微物理方案及其参数的不确定性对同化效果的影响。试验采用组合微物理方案以及扰动微物理方案中的参数的方法,结果表明,模式误差非常小甚至可以忽略时,使用单个微物理方案并扰动参数能够使真实风暴的主要特征在分析场中较未扰动参数得到更好地反映;存在模式误差时,使用单个微物理方案并扰动参数后,分析场中的各要素的分布较未扰动参数更加接近真实风暴,同化效果得到改进,且改进效果比模式误差非常小时更为明显;存在模式误差时,组合微物理方案并扰动参数后,分析场中对流云团的形态较未组合方案或未扰动参数更接近真实风暴,主要要素场的配置最能反映真实风暴的特征,同化效果最为理想。结果也表明,扰动参数时、参数扰动范围较小时,同化效果较优。 相似文献
13.
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. 相似文献
14.
多普勒雷达资料同化对江苏一次飑线过程的数值模拟 总被引：2，自引：6，他引：2
应用新一代中尺度预报模式WRF模式及其3DVar同化系统, 针对江苏地区2009年6月14日飑线过程进行了多普勒雷达资料的同化试验研究, 在对雷达资料进行严格质量控制的基础上, 设计一系列尺度化因子优化调整及同化频率的敏感性试验。试验结果表明:同化后初始场得到不同程度改善, 适当的尺度化因子设定, 能够有效改进对模式初始场中700 hPa风场和850 hPa温度场以及组合反射率因子等要素的分析, 进而改善短时降水预报和风暴的垂直结构配置;并且同化频率越高, 对初始场的组合反射率因子分布与观测更为接近, 短时降水预报越准确。 相似文献
15.
针对GRAPES(Global/Regional Assimilation and Prediction System)模式三维变分系统高层背景场温湿廓线外推方案的局限性,提出以气候垂直廓线重新构造高层温湿垂直结构,以减小外推方案的偏差。首先采用一维变分同化系统,展开模拟实验:分析目前模式中使用的外推方案误差及其对反演结果的影响,利用高层大气气候廓线构造垂直结构并分析同化偏差。最后,运用GRAPES全球分析预报系统进行同化实验并分析改进程度。结果显示:模拟研究表明采用高层背景场温湿廓线外推方案与实际观测相比最大偏差在1 h Pa附近可达数十度以上,不仅影响平流层,而且对对流层也有影响;用气候温度数据修正GRAPES高层温度数据,可以减少50%以上的偏差,证明了用气候值高层数据优化现行GRAPES模式中同化系统高层插值方案的可行性。全球GRAPES三维变分同化试验结果显示,改进方案不仅显著的改善平流层分析质量,对对流层中高层也有改进。 相似文献
16.
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 总被引：2，自引：0，他引：2
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。 相似文献
17.
The WRF 3DVar System Combined with Physical Initialization for
Assimilation of Doppler Radar Data 
下载免费PDF全文
下载免费PDF全文 The three-dimensional variational data assimilation (3DVar) system of the Weather Research and Forecasting (WRF) model (WRF-Var) is further developed with a physical initialization (PI)
procedure to assimilate Doppler radar radial velocity and reflectivity observations. In this updated 3DVar system, specific humidity, cloud water content, and vertical velocity are first derived from reflectivity with PI, then the model fields of specific humidity and cloud water content are replaced with the modified ones, and finally, the estimated vertical velocity is added to the cost-function of the existing WRF-Var (version 2.0) as a new observation type, and radial velocity observations are assimilated directly by the method afforded by WRF-Var. The new assimilation scheme is tested with a heavy convective precipitation event in the middle reaches of Yangtze River on 19 June 2002 and a Meiyu front torrential rain event in the Huaihe River Basin on 5 July 2003. Assimilation results show that the increments of analyzed variables correspond well with the horizontal distribution of the observed reflectivity. There are positive increments of cloud water content, specific humidity, and
vertical velocity in echo region and negative increments of vertical velocity in echo-free region where the increments of horizontal winds present a clockwise transition. Results of forecast experiments show that the effects of adjusting cloud water content or vertical velocity directly with PI on forecast are not obvious. Adjusting specific humidity shows better performance in forecasting the precipitation than directly adjusting cloud water content or vertical velocity. Significant
improvement in predicting precipitation as well as in reducing the model's spin-up time are achieved when radial velocity and reflectivity observations are assimilated with the new scheme. 相似文献
18.
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。 相似文献
19.
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. 相似文献
20.
利用WRF（Weather Research and Forecasting）模式和基于本征正交分解的四维集合变分同化方法（POD-4DEnVar）,对2015年12月9日一次华南暴雨过程进行多普勒雷达资料同化试验,并与三维变分同化试验（WRF-3DVar）进行对比,讨论了POD-4DEnVar方法中局地化半径对模拟效果的敏感性。结果表明,比较不同化雷达资料的控制试验,WRF-3DVar和WRF-POD-4DEnVar试验的降水模拟结果得到明显改善,且WRF-POD-4DEnVar的降水强度更接近实况。两种同化方法通过改变不同的初始要素达到改进降水模拟效果的目的,3DVar方法通过调整初始风场,间接减弱暴雨发生的水汽条件,POD-4DEnVar方法则直接调整湿度场。在降水过程中,同化试验改变了冷空气活动和水汽通量辐合的模拟结果,从而改善降水的模拟效果。POD-4DEnVar方法对局地化半径比较敏感,随局地化半径增大,同化对风场和湿度场的影响范围扩大,当局地化半径取为200 km时,降水模拟的效果最好。 相似文献