共查询到20条相似文献，搜索用时 93 毫秒
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概要介绍最近5年在国家科技攻关项目“中国气象数值预报系统科技创新研究”框架内所取得的主要成果，重点是卫星等遥感资料在变分同化中的应用，高分辨非静力数值预报模式的发展，全球资料同化与中期数值天气预报系统的发展，数值天气预报系统的模块化与并行计算，数值天气预报新技术的研究等，并扼要介绍我国新一代数值天气预报系统的业务应用试验。最后，对我国数值天气预报的进一步发展做了讨论。 相似文献
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数值天气预报中集合-变分混合资料同化及其研究进展 总被引：4，自引：0，他引：4
数值天气预报中混合资料同化是结合集合预报与变分同化方法各自优势研究发展的一种新型资料同化方案。它在变分资料同化框架的基础上,利用集合预报扰动场信息的优点,构造具有流依赖属性的背景误差协方差结构,克服变分同化中固定、均匀及各向同性的背景误差协方差的缺陷,改善对不同天气系统预报误差协方差的表达能力,从而改善分析和预报的质量。近年来,基于集合与变分方法的混合资料同化快速成为数值预报资料同化发展的新趋势,具有良好的业务应用潜力。对集合与变分混合资料同化的研究进展进行综述,在简要介绍数值天气预报中资料同化发展趋势的基础上,分析混合资料同化方案的理论框架和优势,总结混合资料同化的发展和研究成果,并讨论我国研究和发展混合资料同化的基础和前景及面临的问题,指出混合资料同化发展中亟需研究的关键科学问题。 相似文献
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资料同化方法研究进展 总被引：3，自引：0，他引：3
数值天气预报模式的不断完善和大气观测探测资料（特别是卫星、雷达等非常规探测资料）的大量涌现,推动着资料同化方法的逐步发展.文章主要回顾了资料同化方法研究的发展过程、目前的应用现状以及对未来同化方法的展望.随着人们对资料同化含义的深入理解,对于资料同化的研究由初始的探索阶段逐步发展到以经验性为主的同化方法,主要包括SCM和nudging;统计方法的引入,成为资料同化方法研究发展道路上具有重要意义的一个里程碑,从而出现了多元统计插值同化,比如OI,3D-Var以及PSAS;针对背景误差协方差固定不变与实际情况的差异,考虑时间维的四维资料同化方法成为目前国际上较为主流的同化研究方法,其中以4D-Var和Kalman滤波为代表;随着计算机技术的进步,更加合理的四维资料同化方法将会成为未来业务预报中主要的资料同化方法. 相似文献
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雷达反射率资料的三维变分同化研究 总被引：6，自引：3，他引：3
应用天气研究和预报模式(WRF)三维变分系统中一种新的雷达反射率资料间接同化方法来进行反射率资料的三维变分同化研究,评估雷达反射率资料对夏季短时定量降水预报的作用.该方法不直接同化雷达反射率资料,而是同化由反射率资料反演出的雨水和估计的水汽.以2009年夏季北京地区发生的4次强降水过程为例,考察了北京市气象局业务运行的快速更新循环同化预报系统对京津冀地区雷达网的雷达反射率资料的同化性能以及雷达反射率资料和径向风资料同时同化的效果.数值试验结果表明:(1)同化反演雨水或水汽都能改善降水预报,但同化反演水汽对降水预报效果的改善起了更重要的作用;(2)同化反射率资料能极大地提高短时降水预报的效果,其稳定的正面效果可以延伸到6h的预报时效,而同化径向风资料不能得到稳定的正效果;(3)同化雷达资料时,应用快速更新循环同化预报系统是提高短时定量降水预报的一个有效途径. 相似文献
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为了进一步提高雷暴预报的准确率,在分析研究雷暴预报方法的基础上,提出了一种了基于改进遗传算法优化小波神经网络的雷暴预报方法(IGA-WNN).该方法利用聚类分析和牛顿迭代法对多种群遗传算法的收敛方向和精度进行改进,避免了种群同质化与局部最优问题,采用改进的遗传算法对小波神经网络的初始权值阈值进行了优化.选用南京地区2008—2009年6—8月的探空和闪电定位资料,使用灰关联法挖掘出关联程度较大的对流参数作预报因子,归一化处理后输入模型,采用独立样本进行预报检验.结果表明,与BP神经网络等方法相比,IGA-WNN预报准确率更高,具有更好的非线性处理能力和泛化性. 相似文献
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基于遗传算法的暴雨强度公式参数的优化 总被引：17，自引：2，他引：15
遗传算法是模型参数优化的一种有效方法。将遗传算法应用于北京市郊区不同重现期的暴雨强度与降雨历时关系式中参数的优化,并与传统回归法和优选回归法的优化效果进行了分析比较。实例计算结果表明：遗传算法用于暴雨强度公式中的参数估计精度高于传统回归法和优选回归法的参数估计精度。该方法具有直观、简便和实用特点。 相似文献
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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
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The present study designs experiments on the direct assimilation of
radial velocity and reflectivity data collected by an S-band Doppler weather
radar (CINRAD WSR-98D) at the Hefei Station and the reanalysis data produced by
the United States National Centers for Environmental Prediction using the Weather
Research and Forecasting (WRF) model, the WRF model with a three-dimensional
variational (3DVAR) data assimilation system and the WRF model with an ensemble
square root filter (EnSRF) data assimilation system. In addition, the present
study analyzes a Meiyu front heavy rainfall process that occurred in the Yangtze
-Huaihe River Basin from July 4 to July 5, 2003, through numerical simulation.
The results show the following. (1) The assimilation of the radar radial velocity
data can increase the perturbations in the low-altitude atmosphere over the heavy
rainfall region, enhance the convective activities and reduce excessive simulated
precipitation. (2) The 3DVAR assimilation method significantly adjusts the
horizontal wind field. The assimilation of the reflectivity data improves the
microphysical quantities and dynamic fields in the model. In addition, the
assimilation of the radial velocity and reflectivity data can better adjust the
wind fields and improve the intensity and location of the simulated radar echo
bands. (3) The EnSRF assimilation method can assimilate more small-scale wind
field information into the model. The assimilation of the reflectivity data alone
can relatively accurately forecast the rainfall centers. In addition, the
assimilation of the radial velocity and reflectivity data can improve the
location of the simulated radar echo bands. (4) The use of the 3DVAR and EnSRF
assimilation methods to assimilate the radar radial velocity and reflectivity
data can improve the forecast of precipitation, rain-band areal coverage and the
center location and intensity of precipitation. 相似文献
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An Ensemble-Based Three-Dimensional Variational Assimilation Method for Land Data Assimilation 总被引：1，自引：0，他引：1 
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下载免费PDF全文 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. 相似文献
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Based on the newly developed Weather Research and Forecasting model(WRF)and its three-dimensional variational data assimilation(3DVAR)system,this study constructed twelve experiments to explore the impact of direct assimilation of different ATOVS radiance on the intensity and track simulation of super-typhoon Fanapi(2010)using a data assimilation cycle method.The result indicates that the assimilation of ATOVS radiance could improve typhoon intensity effectively.The average bias of the central sea level pressure(CSLP)drops to 18 hPa,compared to 42 hPa in the experiment without data assimilation.However,the influence due to different radiance data is not significant,which is less than 6hPa on average,implying limited improvement from sole assimilation of ATOVS radiance.The track issue is studied in the following steps.First,the radiance from the same sensor of different satellites could produce different effect.For the AMSU-A,NOAA-15 and NOAA-18,they produce equivalent improvement,whereas NOAA-16 produces slightly poor effect.And for the AMSU-B,NOAA-15 and NOAA-16,they produce equivalent and more positive effect than that provided by the AMSU-A.Second,the assimilation radiance from different sensors of the identical satellites could also produce different effect.The assimilation of AMSU-B produces the largest improvement,while the ameliorating effect of HIRS/3assimilation is inferior to that of AMSU-B assimilation,while the AMSU-A assimilation exhibits the poorest improvement.Moreover,the simultaneous assimilation of different radiance could not produce further improvement.Finally,the experiments of simultaneous assimilation radiance from multiple satellites indicate that such assimilation may lead to negative effect due to accumulative bias when adding various radiance data into the data assimilation system.Thus the assimilation of ATOVS radiance from a single satellite may perform better than that from two or three satellites. 相似文献
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马秀梅 《沙漠与绿洲气象(新疆气象)》2015,9(6):19-26
基于新疆区域数值预报系统（Desert Oasis Gobi Rapid Analysis Forecast System,下称DOGRAFS）,开展了同化C波段雷达资料对2010年10月6日发生在新疆的一次强降水过程预报结果影响的试验分析。其中设计包括不同化任何资料、同化常规资料、同化雷达反射率因子、同化雷达径向风、同时同化反射率因子和径向风五组试验,重点分析了雷达资料同化对此次天气过程降水、温度以及风速模拟效果的影响。结果表明：（1）同化雷达径向风和同时同化径向风和反射率因子相对于其他三组试验,对降水预报的TS和ETs评分更高;（2）相对于其他三组试验,同化雷达径向风和同时同化径向风和反射率因子对模式垂直方向上的温度、风速预报偏差具有一定的改善效果;（3）对于地面2 m温度和10 m风速而言,同化常规观测资料比其他四组试验预报的平均偏差和均方根误差更小;其它四组试验误差相当,差别不明显,表明同化雷达对近地面层温度和风的影响不明显。本研究旨在探索C波段雷达观测资料在新疆区域数值预报系统中的适用性,为今后雷达观测资料在业务系统中的应用提供参考。 相似文献
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以2019年4月24日发生在辽宁省的一次大风天气过程为例,选取GFS(全球预报系统) 0.25°×0.25°再分析资料,基于WRF模式三维变分同化技术,进行一组IASI资料同化试验,和未同化任何资料的控制试验(CTNL)。通过对比两组试验结果考察IASI资料同化改进数值模式初始场的分析及其后续预报的机制。研究结果表明：经过IASI资料同化的模拟结果质量有很大改善,分析场与背景场相比更靠近观测场；经过云检测之后不同通道使用的观测数目多少不一,资料同化对模拟的改善效果在通道0~800最佳；IASI资料同化对地表10 m风场的预报技巧有显著的提高作用,相比控制试验可以更精准地预报大风天气的区域和强度；IASI同化试验的预报质量高于控制试验且随时间比较稳定。 相似文献
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FY-3A卫星微波资料的集合变分混合同化试验 总被引：1，自引：0，他引：1
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。 相似文献