共查询到18条相似文献,搜索用时 46 毫秒
1.
集合最优插值中的样本选取 总被引:1,自引:0,他引:1
背景误差协方差控制了分析向观测调整的幅度以及调整的结构,所以其对同化分析的质量起着至关重要的作用。对于集合同化方法而言,样本决定了背景误差协方差的分布。基于HYCOM海洋数值模式结果,针对集合最优插值方法,探讨了静态样本的选取和更新对背景误差协方差结构分布的影响,研究结果表明:由变量的原始状态数据估计的静态样本会夸大样本相关性,由扣除季节变化得到的距平数据统计出的静态样本能比较合理地反映背景误差协方差的结构;在季风控制区,具有季节变化的样本比静态样本更能适应背景误差协方差随流形分布的特征。同时一系列的同化试验也被实施来进一步调查不同样本对同化分析的影响。 相似文献
2.
集合KALMAN滤波和最优插值方法在不同观测分布的比较理想试验 总被引:3,自引:0,他引:3
目前一种比较流行并且可行的同化方法-集合Kalman滤波(EnKF)能够计算依赖于流的误差统计量。理论上,EnKF能够比最优插值、三维变分等更准确地计算误差统计量,能更好地融合背景场和观测场的信息。作者利用二维平流扩散方程经过10天的同化循环,比较不同观测分布的情况下EnKF和最优插值(OI)的模拟能力。理想试验结果显示,随着观测分布密度的减小,尤其是当观测的分辨率大于OI估计的相关尺度时,集合Kalman滤波的结果比最优插值有更明显的改进。 相似文献
3.
4.
基于最优插值方法分析的中国区域地面观测与卫星反演逐时降水融合试验 总被引:21,自引:4,他引:21
为了发展一套适用于中国区域的高分辨率(0.1°×0.1°)逐时降水产品,以CMORPH卫星反演降水为背景场,以基于3万个自动气象站观测的逐时降水量分析的中国降水格点分析产品(Chinese Precipitation Analyses,CPA)作为地面观测场,采用最优插值方法对二者进行了融合试验.用2009年6-8月的样本统计分析了卫星反演与地面观测降水的误差及其协相关形式,按照误差结构来分配权重.融合试验的个例检验表明,该方案在有站点的地区能较好地引入地面观测信息,在没有站点观测的地区则保留CMORPH的原始信息,最终形成一套覆盖中国区域的高时空分辨率的降水场.2009年6-8月独立样本检验的统计结果也表明,该融合产品的平均偏差、均方根误差、相对误差分别为-0.004 mm/h、1.271 mm/h和15.964%,平均空间相关系数达到0.778,与融合前CMORPH的各统计值相比,改进幅度基本都超过了50%,且与风云系列卫星的同类型产品相比精度也有一定程度的提高. 相似文献
5.
利用2007年6月8日—8月31日东亚地区TIGGE集合预报资料中欧洲中期天气预报中心(European Centre for Medium-range Weather Forecasts,ECMWF)和英国气象局(United Kingdom M et Office,UKM O)两个中心的地面2 m气温资料进行集合成员优选研究。结果表明,对于24~96 h预报,集合成员优选方法能够较好地选出预报技巧较高和预报技巧较低的集合成员。个例分析表明,在极端天气出现的地区,优选集合平均的预报优势较为明显。对比ECMWF和UKMO的集合成员优选结果发现,ECMWF的预报效果优于UKMO的预报效果。 相似文献
6.
利用全国垃圾填埋场的点源数据,基于实际调研和实验室分析建立中国不同区域、不同规模、不同填埋时间的排放因子矩阵,采用IPCC推荐的一级降解动力学(FOD)方法自下而上地核算了中国2107个垃圾填埋场在2007年的甲烷(CH4)排放量。针对不同区域和类型的填埋场,分别就城市垃圾组分、可降解有机碳、CH4修正因子、CH4氧化系数、填埋场CH4收集率等进行了深入研究。结果显示,中国2007年填埋场CH4排放量为118.61万t,与《中华人民共和国气候变化第二次国家信息通报》2005年填埋场排放量(220万t)差异较大,其主要原因是城市垃圾填埋场统计数据的差异,例如填埋场个数及垃圾填埋量。中国绝大部分填埋场CH4年排放量在700 t以下,超过1000 t的有279个,超过1万t的仅10个。江苏省的CH4排放量最高,达到9.87万t;西藏的排放量最小,仅为0.21万t。东部江苏、广东、浙江等省的整体排放量较高,西部地区西藏、宁夏、青海等地的排放水平较低。 相似文献
7.
8.
最优集合预报订正方法在客观温度预报中的应用 总被引:1,自引:0,他引:1
数值模式的直接输出预报在实际应用时常与实况产生一定的偏差,对模式预报进行有效的本地化订正是提高预报准确率的重要手段。以欧洲中期天气预报中心(ECMWF)模式细网格资料,采用最优集合(anolog ensemble,AnEn)预报订正方法对北京市各站1~7 d的日最高气温和日最低气温进行订正,并对相关参数进行了本地化。采用了滑动训练期、优化变量权重两种方案进行训练。检验评估结果表明:(1)滑动训练期采用60 d时能同时保证计算效率和预报准确率;采用最优变量权重方案后,与预报员主观预报准确率对比,AnEn的最低气温优于预报员主观预报,最高气温基本相当;增加训练期的长度(引入多年的历史资料)相比优化变量权重方案能更有效地提高预报准确率。(2)AnEn预报订正方法在改善数值模式预报的固有偏差(如对由数值模式对局地地形、边界层日变化等形成的误差)效果显著,有较好的应用价值;对于因局地天气(如霾、降水、大风等)影响下,AnEn的温度预报准确率虽优于ECMWF,但不如主观预报,未来还有改进空间。还对检验结果进行了时间和空间验证,确保在以后的业务尤其是智能网格预报业务中的运行效果。 相似文献
9.
10.
采用基于自适应相关函数的最优插值法对四川广元的雷达定量降水估测进行实时订正并评估其效果。结果表明,该方法有效改善了雷达定量降水估测对弱降雨高估、对强降雨低估的问题,提高了降水估测精度,同时可以更好地模拟降水空间分布特征和时空变化;研究建立的自适应相关函数模型可以根据测站分布情况动态计算最优权重因子,降低了雷达-雨量计联合校准法对测站分布的要求,便于在不同地区开展实际业务应用。 相似文献
11.
A Multivariate Empirical Orthogonal Function-Based Scheme for the Balanced Initial Ensemble Generation of an Ensemble Kalman Filter 下载免费PDF全文
The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful choice of an initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance of the system. Based on the multivariate empirical orthogonal function (MEOF) method, a new ensemble initialization scheme is developed to generate balanced initial perturbations for the ensemble Kalman filter (EnKF) data assimilation, with a reasonable consideration of the physical relationships between different model variables. The scheme is applied in assimilation experiments with a global spectral atmospheric model and with real observations. The proposed perturbation method is compared to the commonly used method of spatially-correlated random perturbations. The comparisons show that the model uncertainties prior to the first analysis time, which are forecasted from the balanced ensemble initial fields, maintain a much more reasonable spread and a more accurate forecast error covariance than those from the randomly perturbed initial fields. The analysis results are further improved by the balanced ensemble initialization scheme due to more accurate background information. Also, a 20-day continuous assimilation experiment shows that the ensemble spreads for each model variable are still retained in reasonable ranges without considering additional perturbations or inflations during the assimilation cycles, while the ensemble spreads from the randomly perturbed initialization scheme decrease and collapse rapidly. 相似文献
12.
Initial perturbation scheme is one of the important problems for ensemble prediction. In this paper,
ensemble initial perturbation scheme for Global/Regional Assimilation and PrEdiction System (GRAPES)
global ensemble prediction is developed in terms of the ensemble transform Kalman filter (ETKF) method.A new GRAPES global ensemble prediction system (GEPS) is also constructed. The spherical simplex 14-member ensemble prediction experiments, using the simulated observation network and error characteristics of simulated observations and innovation-based in ation, are carried out for about two months. The structure characters and perturbation amplitudes of the ETKF initial perturbations and the perturbation growth characters are analyzed, and their qualities and abilities for the ensemble initial perturbations are given.
The preliminary experimental results indicate that the ETKF-based GRAPES ensemble initial perturbations could identify main normal structures of analysis error variance and reflect the perturbation amplitudes.The initial perturbations and the spread are reasonable. The initial perturbation variance, which is approximately equal to the forecast error variance, is found to respond to changes in the observational spatial variations with simulated observational network density. The perturbations generated through the simplex method are also shown to exhibit a very high degree of consistency between initial analysis and short-range forecast perturbations. The appropriate growth and spread of ensemble perturbations can be maintained up to 96-h lead time. The statistical results for 52-day ensemble forecasts show that the forecast scores ofensemble average for the Northern Hemisphere are higher than that of the control forecast. Provided that using more ensemble members, a real-time observational network and a more appropriate inflation factor,better
effects of the ETKF-based initial scheme should be shown. 相似文献
13.
14.
15.
16.
Shuoben Bi Shengjie Bi Xuan Chen Han Ji Ying Lu 《Asia-Pacific Journal of Atmospheric Sciences》2018,54(4):611-622
Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction. 相似文献
17.
18.
基于GRAPES全球集合预报系统(GRAPES-GEPS)及2020年2月13-16日的全国寒潮天气过程,开发出一类新的集合预报产品—K-均值聚类产品。采用爬山法确定最佳聚类数量,并采用K-均值聚类算法对集合样本进行分类。结果表明,该方法的500hPa位势高度场所有类别的聚类产品均呈现出中高纬Ω形的环流形势及低压系统后部冷平流的走向,发生概率最高的聚类产品最能反映实况中环流形势的分布。对于850hPa温度场,其聚类产品均呈现出全国温度从北到南呈带状逐渐增加的空间分布特征,发生概率最高的第一类聚类产品与实况最为接近。对于10m风速聚类产品,在较大风速处,集合样本离散度较大,不同类别的风速大小差异显著;发生概率较高的第一类聚类产品,其对天津及周边地区10m风速的分布及强度描述均较准确,并能提供有价值的预报信息。K-均值聚类能有效地实现集合预报样本信息的浓缩,该产品可为预报员判断某一时次的天气预报提供直观指导。 相似文献