区域三维变分同化中背景误差协方差的模拟

背景误差协方差(B)是变分同化中的一个重要部分,极大地影响同化系统输出的分析场.由于计算和指定B中有关统计量需要巨大的资料存储量和计算量,因此进行相关的研究较为困难.本文首先论述了B在变分同化中的重要性以及进行模拟的必要性;接着介绍了美国NMC方法的原理,并研究将其应用到区域三维变分同化中的方法;然后利用WRF模式生成的预报场差值集合对有关统计量进行了估计.揭示了以下结论:通过使用平衡变换和回归系数,控制变量被限制在较小范围内,保证了分析场的质量;流函数第一全局特征向量在200 hPa附近的最大分量,表示了急流层中强西风误差;流函数前五个全局特征向量在低层与中高层之间是负相关的;非平衡温度和相对湿度的特征长度尺度比流函数和非平衡速度势的值要小,说明它们是局地性较强的量.流函数和非平衡速度势的特征长度尺度随垂直模态数的增大快速减小,而相对湿度和非平衡温度的特征长度尺度随垂直模态数的变化较为平缓.     

集合变分混合同化背景误差协方差流依赖性分析

通过单点观测试验的方法,对集合变分混合同化背景误差协方差的流依赖特征、流依赖性影响因子、产生原因,以及集合预报方法对流依赖性的影响进行了研究。结果表明:由于引入了集合信息,集合变分混合同化的分析增量与天气系统的分布有关,具有非均匀、各向异性的特征;这种流依赖特征对混合系数敏感,当集合协方差所占权重很小时,分析增量仍呈现出均匀、各向同性特征;混合同化背景误差协方差的流依赖特征不仅与集合样本有关,还与构造集合协方差的ETKF方法有关,只引入与环流形势密切相关的集合样本并不能使分析增量表现出显著的流依赖性,集合样本和ETKF方法共同作用才能将流依赖信息引入到混合协方差中,使分析增量出现流依赖特征;不同集合预报方法对混合协方差的流依赖特征有显著影响,考虑初值和物理过程的超级集合,以及在超级集合样本上再进行ETKF更新扰动后样本构造的混合协方差流依赖特征更加显著。     

非线性平衡方案在三维变分同化系统中的应用

文中考虑气流出现明显的弯曲和辐合时,资料变分同化方案中的风压场间动力学约束问题。在强涡旋、锋面等剧烈变化的系统中,地转平衡和线性平衡方程均不能很好地描述风压场关系,非线性平衡方程考虑了流场曲率的作用,能更恰当地反映风压场的关系。把非线性平衡方案应用于三维变分同化分析系统中,可以得到和天气形势相关联的模式变量背景误差协方差,对热带气旋进行同化试验后得到的分析场更协调。结果表明,非线性平衡方程的应用,可以提高强涡旋系统中三维变分同化分析场的质量,对热带气旋强度预报有一定的改善。     

GRAPES变分同化系统中动力平衡约束的统计求解

该文在GRAPES (Global/Regional Assimilation and PrEdiction System) 模式面三维变分 (3D_Var) 框架中引入了一种描述不同控制变量之间动力平衡约束的新方案。新方案采用统计得到的流函数和模式气压变量 ( π ) 之间的回归系数代替原方案中的线性平衡方程,来表达旋转风和质量场之间的平衡关系;采用流函数和势函数之间的回归系数,补充表达了原方案中所没有的旋转风和散度风之间的平衡关系。与原方案相比,新方案算法简单,避免了垂直方向的反复插值,减少了插值误差的引入。通过随机扰动试验和单点试验可以发现,在地转关系成立较好的区域,新方案中旋转风和质量场的耦合程度与原方案接近一致;而在地转关系不适用区域,新方案可以有效减小两者的耦合程度。此外,由于新方案中添加了旋转风和散度风之间的动力平衡约束,边界层的风场分析也更加接近大气真实状况。     

流依赖球面小波背景误差协方差模型设计和初步试验

基于资料同化集合设计了流依赖球面小波背景场误差协方差模型中背景误差方差和局地垂直相关协方差的统计计算方法。为了提高背景误差方差的估计精度,采用客观滤波技术来减少因集合样本个数不足而引入的随机取样噪声。最后在银河四维变分同化业务系统(YH4DVar)上设计了集合资料同化的试验系统,以流依赖背景误差方差为重点验证了模型的有效性。结果表明:基于流依赖球面小波背景误差协方差模型能够有效估计出随天气状态变化的背景场误差方差,对台风等剧烈变化的天气过程的同化分析和预报都具有一定的正效果。      

GRAPES全球变分同化背景误差协方差的改进及对分析预报的影响：背景误差协方差三维结构的估计

回顾并详细推导了估计背景误差协方差统计特征的美国国家气象中心(NMC)方法及其优缺点;采用NMC方法系统地估计了新版GRAPES全球模式的背景误差方差、水平相关特征尺度和垂直相关结构,并与欧洲中心模式结果进行了比较。结果表明,目前GRAPES全球模式的背景误差方差比以前有了显著减小;水平相关特征尺度随纬度和高度有显著变化;背景误差垂直相关结构与欧洲中心模式结果非常一致,相比经验公式结果更具物理意义,同时,单点试验结果也表明,更新后的垂直相关结构产生的分析增量更合理。通过与欧洲中心模式背景误差协方差三维结构的对比,分析了不同模式间背景误差协方差的异同及GRAPES全球同化分析系统目前存在的一些不足及可能原因。为新版GRAPES全球模式的三维变分系统提供了基本的背景误差协方差的三维结构。     

背景误差协方差变量平衡特征及其对台风同化和预报的影响

构造合理的背景场误差协方差是做好资料同化的关键。分析了背景误差协方差中变量相关关系在台风季节和非台风季节隐含的不同动力平衡特征,并讨论其对台风同化和预报的影响。分析发现,与非台风季节相比,在台风季节温度与非平衡速度势具有更强的动力相关性,拟相对湿度与其他控制变量的相关性也更显著。这些动力相关性在背景场误差中协方差的引入,将在同化分析过程中使得观测信息可以合理地对同化分析场产生影响。台风循环同化和预报的结果验证了对变量平衡特征的分析:背景误差协方差中新平衡关系的建立,对同化和预报有较大的正面影响,尤其是相对湿度和其他控制变量相关的建立,明显改善了台风路径、强度和降水的预报效果。      

三维变分同化中多变量平衡约束设计

在现代变分同化系统中,背景场误差协方差起着决定观测信息的空间分布特征、匹配不同变量间的关系和保证分析增量平衡的作用。基于NMC(National Meteorology Centre)方法,设计了一个新的多变量平衡约束算子:在物理变换中,构建相对湿度和其余控制变量间的平衡约束算子;同时分别采用经验正交函数方法和递归滤波器来模拟控制变量(ψ,χ_u,T_u,h_ru,p_su)^T的垂直误差协方差和水平误差协方差。利用2009年6月2日到8月9日间WRF模式的预报差值场,对新的背景场误差协方差进行模拟分析。单点观测试验表明,新的背景场误差协方差实现了观测信息在干湿变量之间的传递,而且相对湿度具有与温度相似的增量场分布。     

基于云依赖背景场误差协方差的雷达资料同化及对降雨预报影响研究

传统变分同化方法中使用各向同性和均质的背景场误差协方差,忽略了背景场误差协方差的天气系统依赖性,而在变分框架下引入集合流依赖的背景场误差协方差还需要额外的集合预报.为在变分同化中引入更合理的背景场误差协方差,通过引入云指数构建云依赖背景场误差协方差,提出了一种云依赖背景场误差协方差的同化方案,并应用于雷达等多源观测...     

背景场误差样本模拟对同化及数值预报效果的影响北大核心CSCD

合理估计背景场误差协方差矩阵(B)是做好变分同化的关键环节。利用控制变量随机扰动法(RandomCV)、增长模繁殖法(BGM)及NMC法等3种背景场样本模拟方法,基于WRFDA系统计算B矩阵,对B矩阵的特征及其对同化预报效果的影响进行了研究。B矩阵的特征分析和单点观测试验表明,NMC法与RandomCV法得到的B矩阵误差方差较大,在同化中观测的权重更大;RandomCV法得到的B矩阵,背景场误差中变量的长度尺度更大,说明同化中观测的水平影响范围更大。连续循环同化和预报试验表明:应用RandomCV法计算得到的B矩阵分析与预报的效果明显优于系统自带的以及BGM法得到的B矩阵,且效果与NMC法相当与NMC方法相比,采用RandomCV方法产生背景场样本具有时间和人力成本相对低的优点。     

Application of Nonlinear Balance Scheme in Three-Dimensional Variational Data Assimilation

The dynamical constrains in three-dimensional variational data assimilation are discussed when consid- ering the impact of stream divergence and convergence on the pressure and wind fields.For the analysis of severe tropical cyclone,frontal structures,and other rapidly changing structures,the geostrophic balance and linear balance cannot properly represent the relationship between wind and pressure fields.However,the nonlinear balance incremental equation takes into account the information of flow-dependent background, and makes response to the flow-dependent background covariance in the 3D-Var system.Results indicate that the application of the nonlinear balance equation to 3D-Var system improves the quality of severe trop- ical cyclone assimilation system,which has some positive effects on intensity prediction of tropical cyclones.     

Wind Speed and Altitude Dependent AMDAR Observational Error and Its Impacts on Data Assimilation and Forecasting

Aircraft Meteorological Data Relay (AMDAR) observations have been widely used in numerical weather prediction (NWP) because of its high spatiotemporal resolution. The observational error of AMDAR is influenced by aircraft flight altitude and atmospheric condition. In this study, the wind speed and altitude dependent observational error of AMDAR is estimated. The statistical results show that the temperature and the observational error in wind speeds slightly decrease as altitude increases, and the observational error in wind speed increases as wind speed increases. Pseudo single AMDAR observation assimilation tests demonstrate that the wind speed and altitude dependent observational error can provide more reasonable analysis increment. Furthermore, to assess the performance of wind speed and altitude dependent observational error on data assimilation and forecasting, two-month 3-hourly cycling data assimilation and forecast experiments based on the Weather Research and Forecasting Model (WRF) and its Data Assimilation system (WRFDA) are performed for the period during 1 September-31 October, 2017. The results of the two-month 3-hourly cycling experiments indicate that new observational error improves analysis and forecast of wind field and geo-potential height, and has slight improvements on temperature. The Fractions Skill Score (FSS) of the 6-h accumulated precipitation shows that new wind speed and altitude dependent observational error leads to better precipitation forecast skill than the default observational error in the WRFDA does.     

Background Error Covariance Statistics of Hydrometeor Control Variables Based on Gaussian Transform

Use of data assimilation to initialize hydrometeors plays a vital role in numerical weather prediction(NWP).To directly analyze hydrometeors in data assimilation systems from cloud-sensitive observations,hydrometeor control variables are necessary.Common data assimilation systems theoretically require that the probability density functions(PDFs)of analysis,background,and observation errors should satisfy the Gaussian unbiased assumptions.In this study,a Gaussian transform method is proposed to transform hydrometeors to more Gaussian variables,which is modified from the Softmax function and renamed as Quasi-Softmax transform.The Quasi-Softmax transform method then is compared to the original hydrometeor mixing ratios and their logarithmic transform and Softmax transform.The spatial distribution,the non-Gaussian nature of the background errors,and the characteristics of the background errors of hydrometeors in each method are studied.Compared to the logarithmic and Softmax transform,the Quasi-Softmax method keeps the vertical distribution of the original hydrometeor mixing ratios to the greatest extent.The results of the D′Agostino test show that the hydrometeors transformed by the Quasi-Softmax method are more Gaussian when compared to the other methods.The Gaussian transform has been added to the control variable transform to estimate the background error covariances.Results show that the characteristics of the hydrometeor background errors are reasonable for the Quasi-Softmax method.The transformed hydrometeors using the Quasi-Softmax transform meet the Gaussian unbiased assumptions of the data assimilation system,and are promising control variables for data assimilation systems.     

WRF模式三维变分中背景误差协方差估计

利用WRF模式2008年5-10月逐日预报结果,通过NMC方法进行背景误差协方差(B)估计.给出其结构特征,进行单点数值试验,并利用不同B进行1个月的数值模拟试验,检验模拟降水效果.结果表明:通过单点数值试验验证估算的B结构合理.不同的B,资料同化过程差别较大,应用重新统计的B,同化效率更高,目标函数收敛更稳定.模式模...     

资料同化中背景场位势高度误差统计分析的研究

在客观分析中,背景误差协方差对观测信息的传播和平滑、反映不同变量之间的关系有着非常重要的作用.构造合理的背景误差协方差矩阵对于同化系统至关重要,甚至会决定同化分析的好坏.作者主要利用观测余差方法,用T213预报资料和无线电探空观测资料统计我国区域的背景位势高度误差协方差样本,分析背景误差协方差场的结构特征和拟合误差场的空间分布.     

Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC (National Meteorological Center) method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.     

Rainfall Assimilation Using a New Four-Dimensional Variational Method: A Single-Point Observation Experiment

Accurate forecast of rainstorms associated with the mei-yu front has been an important issue for the Chinese economy and society. In July 1998 a heavy rainstorm hit the Yangzi River valley and received widespread attention from the public because it caused catastrophic damage in China. Several numerical studies have shown that many forecast models, including Pennsylvania State University National Center for Atmospheric Research’s fifth-generation mesoscale model (MM5), failed to simulate the heavy precipitation over the Yangzi River valley. This study demonstrates that with the optimal initial conditions from the dimension-reduced projection four-dimensional variational data assimilation (DRP-4DVar) system, MM5 can successfully reproduce these observed rainfall amounts and can capture many important mesoscale features, including the southwestward shear line and the low-level jet stream. The study also indicates that the failure of previous forecasts can be mainly attributed to the lack of mesoscale details in the initial conditions of the models.     

EnKF中误差协方差优化方法及在资料同化中应用

集合卡尔曼滤波 (the Ensemble Kalman Filter,简称EnKF) 中将预报集合的统计协方差作为预报误差协方差,但该估计可能严重偏离真实的预报误差协方差,影响同化精度。基于极大似然估计理论,发展了一种优化预报误差协方差矩阵的实时膨胀方法,即MLE (the Maximum Likelihood Estimation) 方法。利用蒙古国基准站Delgertsgot (简称DGS站) 观测资料,基于EnKF方法和MLE方法,在通用陆面模式 (the Common Land Model,简称CoLM) 中同化了地表温度和10 cm土壤温度观测资料,建立了土壤温度同化系统。结果表明:MLE方法对地表温度和各层土壤温度 (尤其深层土壤温度) 的估计比EnKF方法准确。考虑到浅层和深层土壤温度的差别,在实施MLE方法时对浅层和深层土壤温度采用了不同的膨胀因子。对比膨胀因子为单一标量时的结果,多因子膨胀能缓解深层土壤温度的不合理膨胀,改善同化效果。     

变分同化中使用背景场时尺度匹配的数值研究

分资料同化方法运用于中尺度系统的分析时，粗网格模式的输出经常被用作背景场，此时分析场和背景场之间存在尺度不一致的问题。为了让二者的尺度相匹配，文中提出可以在目标函数中增加一个过滤算子适当滤除分析场中的短波成分。应用浅水方程模式和模拟资料所作的数值试验表明，该方法可以明显地改善分析的效果。