用三点法计算散度和涡度所出现的计算误差试验

本文用我国74个高空观测站联结成三角形网,并用不同波长和位相的理想风场,准确地检验了三点法汁算散度和涡度所可能出现的计算误差。结果证明了三点法所计算的散度值,其误差可达到或超过天气系统散度的数量级,所得到的天气尺度和中尺度特征完全有可能是虚假的。涡度场值也可能存在很大的误差。     

介绍散度计算一种简便方法

气流的辐合、辐散是天气分析和预报中必须认真考虑的物理量。常用的散度计算三点法为分布无规则测站间的散度计算提供了方便、却有下列不足之处:1.不能准确反应散合情况。由散度表达式     

2010年12月9日兴安盟大雪天气过程分析

文章针对2010年12月9—10日兴安盟出现的大雪天气过程,分析了这次大雪过程在各站历史上的排位和对白灾的影响;从高空环流形势的影响系统及风场的分布特征,地面低压移动方向和位置进行分析;分析了物理量场中的相对湿度、涡度、散度、垂直速度、风场等对本次大雪的作用。最后对欧洲预报,T639、德国、日本降水格点,日本传真图等数值预报场在本次大雪预报中的效果进行检验分析。     

利用行星边界层顶上方最邻近标准等压面的实测风推算地面风的方法及其比较

文中提出了一种在实际地形条件下,利用行星边界层顶上方最邻近标准等压面的实测风推算地面风的方法——修正的Taylor螺线法。试算表明,按这种方法和Taylor螺线法推算的地面风场的基本流型均与实测流场相似,但在风速模的均方根误差上,前者较之后者有显著改进.按文中方法推算得到的地面风场所求得的散度场的分布与天气区的配置也较合理,且散度值与实况比较接近.     

从单多普勒雷达速度场反演散度场

该文提出了在极坐标下从单多普勒雷达速度场计算散度的方法。此方法可以直接利用多普勒速度计算极坐标散度中的径向散度项和径向移动项,而无需作任何假定。只有在计算切向散度项时需要在一定的假定下先反演出切向速度。误差分析表明,反演误差比散度小一个量级。对一次冷锋过程的散度反演试验表明,极坐标散度反演方法可用于分析中尺度天气系统。     

基于风廓线网的散度和涡度计算

近年来风廓线雷达在全国范围内的建设为预报员提供了更高时空分辨率的实时风场观测资料,介绍了从风廓线网计算水平散度和涡度的三点法的基本原理、算法及两种三点法的等价性。根据上海地区风廓线网中尺度特性,对梅雨期间一次过程进行计算,并与不同资料和差分方法进行了比较,以考察利用风廓线网分析中尺度系统结构的能力。结果表明,在边界层内存在明显正涡度区与负散度区,与此期间发生的降水相配合,误差小于差分方法。     

散度变化在中尺度对流系统分析预报中之应用

本文介绍了近年来国内外进行中尺度系统的分析和预报中,对风场的散度变化的研究和应用。提出中尺度散度扬的配置及变化与中尺度天气(如强天气和暴雨)有十分密切的关系,它往往能预示中尺度天气的出现。在此基础上,还介绍了利用散度方程某些特征项来研究未来中尺度散度扬的变化,从而达到定性地应用的目的。从近年来国内外的应用看,这种努力是成功的。     

一种新的单部多普勒雷达反演技术

基于一些发展较强的中小尺度天气系统往往与风场的旋转或辐合、辐散有关的事实，文章提出了一种单部多普勒天气雷达径向风场反演二维水平风场的新方法，即涡度-散度方法。文中对1994年7月12日的个例进行了反演处理，处理结果与天气实况进行了对比分析，认为该方法不失为一种具有实用价值的单部多普勒雷达反演水平二维风场的方法。     

多普勒雷达速度图像识别及散度提取方法研究

大尺度风场往往是冷暖平流与大尺度辐合辐散运动相结合的结果,因此将大尺度风场的多普勒雷达速度特征提炼成以下4种类型:(1)暖平流与辐合迭加;(2)冷平流与辐合迭加;(3)暖平流与辐散迭加;(4)冷平流与辐散迭加。不同类型的风场引起零速度线不同的弯曲特征,对判断大尺度风场的辐合辐散有着指示作用。根据零速度线的弯曲程度以及一定距离圈上零速度点与雷达中心连线的夹角等,首先推导出定量计算大气辐合辐散值的算法,然后提出利用逐个调整法识别零速度点的方法。根据该算法,结合零速度点的识别技术,提出了图像识别原理与方法步骤。据此,能够从多普勒雷达基数据中提取出大气平均散度,得到大尺度天气系统不同高度的散度值,并进一步提出了散度平面位置显示(divergence PPI)的概念。通过实例,根据散度PPI与速度PPI的对比,分析了图像识别的效果,并将图像识别法提取的散度值与EVAD技术提取的散度值进行了对比分析。     

计算任意多边形平均涡度、散度的一种方案

一、引言 计算任意三角形平均涡度、散度的方法,可以推广到对任意多边形平均涡度、散度的计算上。这样可以较方便地讨论某些区域性天气过程或分析那些难以用三角形拟合的天气系统的发生发展问题。 本文给出计算任意多边形平均涡度、散度的计算公式,并以进入南海近海发展起来的8113号台风为实例作了计算。计算结果表明,这种方案是可行的。在天气过程影响之前,若能对进入上游区域的天气系统,用这种方法计算其运动学量,无疑将得到一些比较有牧师意义的信息。     

Predictability of Ensemble Forecasting Estimated Using the Kullback–Leibler Divergence in the Lorenz Model

A new method to quantify the predictability limit of ensemble forecasting is presented using the Kullback–Leibler(KL)divergence(also called the relative entropy), which provides a measure of the difference between the probability distributions of ensemble forecasts and local reference(true) states. The KL divergence is applicable to a non-normal distribution of ensemble forecasts, which is a substantial improvement over the previous method using the ensemble spread. An example from the three-variable Lorenz model illustrates the effectiveness of the KL divergence, which can effectively quantify the predictability limit of ensemble forecasting. On this basis, the KL divergence is used to investigate the dependence of the predictability limit of ensemble forecasting on the initial states and the magnitude of initial errors. The local predictability limit of ensemble forecasting varies considerably with the initial states, as well as with the magnitude of initial errors. Further research is needed to examine the real-world applications of the KL divergence in measuring the predictability of ensemble weather forecasts.     

Strategies to Overcome Filter Divergence in the DRP-4-DVar Approach

This paper discusses an important issue related to filter divergence in the dimension-reduced projection,four-dimensional variational data assimilation(DRP-4-DVar) approach.Idealized experiments with the Lorenz-96 model over a period of 200 days showed that the amplitudes of the root mean square errors(RMSEs) reached the same levels as those of the state variables after approximately 100 days because of the accumulation of sampling errors following the cycle of assimilation.Strategies to reduce sampling errors are critical to ensure the quality of ensemble-based assimilation.Numerical experiments showed that localization and reducing observational errors can alleviate,but cannot completely overcome,the filter divergence in the DRP-4-DVar approach,while the method of perturbing observations and the inflation technique can efficiently eliminate the filter divergence problem.     

多普勒天气雷达速度PPI图散度分布信息提取

根据大面积降水回波径向速度PPI图像产品辐合辐散特征,提出采用中值滤波法来剔除噪声污染和用逐点数据对称法来削弱回波缺失和距离折叠引起的误差。在考虑了降水粒子下落速度的情况下,采用模糊定位法来提取速度图中风速性散度特征,用直方图形式表征风向性散度特征。最后通过比较分析,说明了提取方法可较好的表征PPI速度图中的散度信息,计算散度所表征的动力学特征与大面积降水过程有较好的对应关系。     

THE INITIAL WIND SCHEME OF THE HEMISPHERIC SPECTRAL MODEL AND ITS EFFECT ON THE DATA ASSIMILATION

A series of data assimilation and forecast test have been carried out with a hemispheric spectralmodel(T42L9H).It is found that the numerical scheme for determining hemispheric initial wind isimportant to data assimilation and forecast.An inappropriate scheme may cause computationalsources of divergence near the equator,which are responsible for the spurious strong precipitationand corresponding latent heat release.Obviously,this problem differs from either thehemispheric/global domain effect or the tropical data effect pointed by Dalley et al.(1981).Basedon the previous studies,the new scheme of divergence and vorticity correction is presented,andthe difference with other schemes and its effects on the data assimilation are discussed against thecontrol test.Preliminary tests have shown that the new divergence correction scheme proposed in thispaper may be a preferable choice to overcome the initial computational errors in the hemisphericdata assimilation.     

半球谱模式的风场初值及其对资料同化的影响

利用半球谱模式（Ｔ４２Ｌ９）进行了大量的资料同化和预报试验。结果表明，半球风场初值方案对资料同化和预报有重要的影响。不恰当的初值处理会导致虚假散度源，在非绝热模式中可能进一步诱发虚假的强降水与相应的潜热释放。很明显，这个问题既不同于Ｄａｌｌｅｙ［１］所分析的半球／全球范围效应，也不同于所谓的赤道资料效应。文章在前人研究的基础上，提出了散度和涡度修正方案，进而用对比试验方法讨论了该方案与其他方案的差异以及对同化的影响。初步试验表明，散度修正方案是半球资料同化中克服初值计算误差的一个较好的选择。     

东亚季风湿润区水分收支的气候特征

采用1958-2007年NCEP/NCAR月平均再分析资料,分别从水汽通量、水汽通量散度以及区域内降水量与蒸发量差计算东亚季风湿润区的水分收支,分析其差异特征,结果表明:用不同方法计算的水分收支距平年际变化的相关系数分别为0.91,0.71和0.81,误差ε百分率分别为17.4%,44.1%和44%,其中利用水汽通量和散度计算得到的季风湿润区水分收支结果很接近。总体上看,整个区域全年表现为水分收入,春季和夏季的水分收入贡献最大,秋季和冬季贡献较小。在水汽经向输送中,南边界为主要的水汽输入区。从水汽输送计算的水分收支垂直分布来看,多年平均气候态下整个区域除850 hPa存在水分支出外,其余各层均为水分收入,3种方法计算的水分收支在4个季节的年际变化明显。     

Influence of SST biases on future climate change projections

We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977?C1999 in the historical period and 2077?C2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean?Catmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.     

Normal mode initialization for simple models

Summary The normal mode initialization procedures of Phillips (1960) and Temperton (1988) are re-examined and applied to one-dimensional linear and non-linear primitive equations models. The linear method is obtained by setting the first and second substantial time derivatives of the divergence to zero. The resulting state is free of gravity waves. In the non-linear procedure, the substantial time derivatives calculated from the model equations of the gravitational modes are set to zero by implicitly adjusting these modes only. Five-day numerical integrations are performed using both models. The results clearly show the importance of proper initialization. Analytical solutions support the numerical results.With 2 Figures     

Impact of time sampling on atmospheric energy budget residuals

Summary Diagnostic time-mean budgets of energy and water are evaluated in many atmospheric process studies. The errors of budget-derived quantities like sub-gridscale fluxes or diabatic heating are governed by the errors of the budgets. Here we consider 3D-budgets on the meso-β scale over Europe. They are compiled from analyses of state quantities available from forecast centres. In the present study we found that the mandatory 6 hours sampling interval between synoptic observations is the main error source for routine time-mean budgets. The errors have been quantified (i) by first sampling forecast data of the German Europamodell every 5 minutes and averaging them over 12 hours (reference budget), and (ii) by sampling the same data only every 6 hours and averaging these also over 12 hours (routine budget). With this method we find that routine budgets in single atmospheric meso-β scale columns show relative random errors of typically 200% and systematic errors of up to 20%, exclusively due to undersampling. Thus routine budgets, if applied to specific days at individual locations, cannot be expected to yield useful results, except perhaps for cases with extremely strong signal. Compositing over several hundreds of columns with similar weather reduces the random budget error down to about 50%; this seems to be the best one can achieve for routine budgets. The systematic error of some budget quantities is caused by a correlation between the time of occurence of certain processes (mainly convection) and the sampling times. While this error cannot be reduced through compositing, we find that it can be crudely estimated by using different time averaging methods. As application for this method we determine sub-gridscale budget quantities over the BALTEX catchment (August-September 1995) for an ensemble of convectively active and an ensemble of rain-active columns. For the ensemble mean profiles we find, in terms of the diagnosed sub-gridscale test quantities diabatic heating and vertical moist enthalpy flux divergence, that their accuracy is sufficient to detect statistically significant differences between both ensembles. The diabatic heating is about the same for both ensembles, while the flux divergence in the convective ensemble is about three times as large as in the rain ensemble. Received November 7, 2001 Revised April 4, 2001