综合评估环境场影响的热带气旋路径客观相似预报模式

提出一个综合评估环境场影响的热带气旋路径客观相似预报模式。模式应用热带气旋参数、初始和未来环境场，构造客观的相似判据。通过定义非线性的相似指数综合评估历史热带气旋样本在多元判据下的相似程度，从而找到相似样本。应用相似样本的历史路径进行坐标变换和相似指数的权重综合，得到预报路径。模式检验和预报试验表明该模式具有预报技巧。     

基于人工神经网络的暴雨预报方法探讨

探讨了基于人工神经网络模型的暴雨预报方法。该方法仿预报员的暴雨预报思路，在动力模式的降水预报产品、环流形势场和暴雨落区之间通过人工神经网络建立非线性的统计预报模型，该模型的输入是动力模式的降水预报和初始环流形势场的扩展正交分解主成份分量，输出是预报区域的暴雨落区预报。2000年的汛期试验表明该客观预报方法可明显改进数值预报模式的暴雨落区预报，因此可望在业务预报中有较好的应用前景。     

基于进化方向遗传算法的四维变分资料同化方法

数值天气预报模式初始时刻要素场的变分同化问题是一个非线性最优化问题。利用进化方向遗传算法（EDGA）求解该最优化问题，并对理想初始场作数值模拟，结果表明模拟的效果较好。     

不同初始场条件对新疆区域数值模式预报性能的影响

初始场条件直接影响到区域模式的预报性能。基于GRAPES_GFS和NCEP_GFS两种初始场，详细比较了两者之间的差异，随后分别利用两种初始场驱动新疆区域数值模式（RMAPS-CA V1.0），对2021年4月整月的数值预报结果以及2021年4月21日一次暴雨过程模拟结果进行了MET（Model Evaluation Tools）对比检验。结果表明：（1）两种资料位势高度扰动场、温度扰动场、湿度扰动场存在明显差异，其相关系数分别为0.26~0.60、0.05~0.24和0.01~0.12，导致次天气尺度上存在着较大差异，并由此造成了模拟结果之间的差异，反映了区域模式对初始场和边界条件的敏感性；（2）从高空位势高度、风速、温度的预报结果看，NCEP_GFS初始场在新疆区域模式中高空要素的预报效果均要优于GRAPES_GFS初始场，均方根误差分别降低35.5~37.2%、7.6~12.6%和6.0~17.2%。从地面常规预报量的检验看，GRAPE_GFS初始场对2 m温度和10 m风速的预报效果则要优于NCEP_GFS初始场，均方根误差分别降低14.3%和6.8%；（3）从降水检验评分看，两种初始场的降水预报整体为漏报现象，NCEP_GFS初始场针对各降水阈值及不同时效的预报降水评分要高于GRAPES_GFS，0.1 mm/6h、6.1 mm/6h和12.1 mm/6h的TS评分分别提高22.5%、16.1%和150.8%；（4）从一次暴雨过程预报的检验结果看，GRAPES_GFS对于24小时为小量级降水预报效果优于NCEP_GFS,准确率分别为61.4%和40.0%；而NCEP_GFS对于大量级的降水预报则要优于GRAPES_GFS,准确率分别为66.7%和33.3%。两种初始场对降水个例检验偏差以空报现象为主，NCEP_GFS的TS评分整体高于GRAPES_GFS。     

一次粤西南暴雨过程的预报误差来源分析

本文基于WRF模式研究了2015年5月16～17日广东西南地区的一次暴雨过程的预报误差来源。首先比较了以NCEP_FNL为初始资料的WRF模式的模拟预报（记为WRF_FNL）和ECMWF（European Centre for Medium-Range Weather Forecasts）关于该次暴雨过程的确定性预报。结果表明,ECMWF具有较高的预报技巧,因此,认为ECMWF的模式和初始场都较为准确。进一步,以ECMWF的初值作为初始场,选用相同的物理参数化方案,再次用WRF模式进行预报（预报结果记为WRF_EC）。结果表明相对WRF_FNL,WRF_EC的预报结果有明显改善。这表明,初始场的改进对预报有较大的影响,初始误差是预报误差的重要来源。进一步,分析了初始误差的主要来源区域和来源变量。结果表明,南海北部湾至广西西南区域为本次暴雨预报初始误差的主要来源区域,而初始温度场和初始湿度场则为此次暴雨预报初始误差的主要来源变量。同时改进初始温度场和湿度场可以较大程度提高本次暴雨过程的预报技巧。     

卫星资料反演湿度场改变模式初值对降水预报的影响

为探讨初始场资料在数值预报中的重要性，用高分辨率卫星T_BB资料反演的云内湿度场来改进模式初值，初步研究分析了改进的模式初值对降水预报的影响。以湿绝热过程的变态方程为积分方程，由卫星T_BB资料反演出大气中各等压面层湿度场。通过对比分析反演的湿度场与客观分析 (T106) 的水汽场，发现两者有较大区别，前者更加合理地反映出降水区域高空湿度场的分布。利用中尺度模式MM5将有限区域的常规探空资料和非常规资料进行同化，并对暴雨个例进行预报对比试验。不同初值的对比试验表明，在模式的初始场中引入卫星资料反演的湿度场后，明显地改善了模式降水预报的强度和落区，比仅使用常规探空资料更接近于实况。     

热带气旋路径客观预报的若干问题

对多时刻环境因子与热带气旋路径相关的统计分析表明，初始时刻环境场因子对热带气旋路径的预报能力随预报时效的增长而下降；未来时刻环境因子的预报能力高于初始时刻环境场因子；对于一定预报时效的热带气旋路径，具有较高预报能力环境场因子出现时刻大多不与预报时效同时刻；对于４８￣１２０小时的预报时效，初始时刻后４８－７２小时的环境场因子具有较高的预报能力。对ＮＷＰ产品进行统计释用的热带气旋路径预报模式优于用初如     

SVD方法在场分析和预测中的应用

由预报场与因子场的奇异值分解（SVD），可找到影响预报场的主要物理因子，能提取两个场相互作用的主要耦合信号，借助最优化技术，可实现由因子场对预报场的客观预报，以华中汛期降水场为左场，4月北半球500hPa高度场，海平面气压场，北太平洋海温场组成右场，进行SVD分析和预报试验，其结果令人满意。     

一维积云模式初始参优化的研究

福建省人工增雨指挥系统在预报积云降水和评估坪雨效果中曾采用一维时变积云模式。关于该模式初始场的确定，考虑到不同天气类型对模式初始参数的影响，并采用了以确定敏感参数为主，综合考虑其他影响参数为辅的方法。经分类调试，证明了一维模式初始场确实与天气类型有关，并得到早期降水预报率大于75％和可预报积云云顶高度的结果。通过对模拟云和实测云高度的线性回归分析，以及经拟合敏感参数初始扰动温度与平均地面最高气温，均存在较好的相关。     

伴随系统及非线性优化方法在REM模式可预报性研究中的实际个例应用

利用REM模式的伴随系统和非线性优化方法，通过三个实际天气个例，对REM模式的可预报性问题进行了研究。结果表明，REM模式在给定的实际应用中可接受的预报误差范围内，对三个天气个例都具有预报能力。对于个例一，利用现有的常规报文初始观测场，进行简单的插值处理（最优插值等），REM数值模式就可以得到比较满意的预报结果； 对于个例二和个例三，对现有的报文初始观测场进行处理（如四维变分资料同化）后，REM模式在给定的误差允许范围内，对这两个天气个例仍得到满意的预报。研究结果不仅对改进数值模式具有一定的指导意义，而且对如何改进数值模式的初值问题，特别是在中尺度天气预报中如何改进具有一定的参考价值。     

下投式探空资料对数值预报初始场影响的个例分析

通过对2000年8月加勒比海地区飓风Debby活动期间,连续2个时次中使用下投式探空资料同化分析后,利用英国气象局全球数值预报系统的数值预报分析场和背景场的对比分析,阐明了由于下投式探空资料的引入,较好地描述了实际大气的情况,为数值预报提供了较为接近实际的初始场,从而提高了数值预报的精度。其中以湿度场和风场的贡献为最大。     

GRAPES区域扰动预报模式动力框架设计及检验

设计了适用于四维变分同化系统的扰动预报模式GRAPES_PF。根据GRAPES的地形追随坐标非静力原始方程组,采用小扰动分离方法推导微分形式的线性扰动预报方程组,并利用与GRAPES非线性模式相似的数值求解方案求解线性扰动微分方程组。在设计扰动预报模式时采用了两个时间层半隐式半拉格朗日方案对动量方程、热力学方程、水汽方程和连续方程进行时间差分,空间差分方案的变量分布水平方向采用Arakawa C跳点网格,垂直方向采用Charney/Phillips跳层。利用代数消元法进一步推导得到只包含未来时刻扰动Exner气压的亥姆霍兹方程,进而通过广义共轭余差法（GCR）求解,在此基础上得到未来时刻扰动量的预报值。基于所开发的扰动模式开展了数值试验。首先在非线性模式中施加一个中尺度初始扰动高压,得到初始扰动在非线性模式中的后续演变,然后将相同的初始扰动作为扰动模式的初值进行时间积分,将扰动模式预报的结果与非线性模式的结果做了对比。结果表明,所开发的扰动模式GRAPES_PF较好地模拟了惯性重力内波的传播过程:初始高压扰动激发了一个迅速向外传播的惯性重力内波,在气压场向风场适应的过程中,水平风场、垂直运动、位温和湿度等变量均出现了扰动增量,与非线性模式得到的结果相当接近。GRAPES_PF作为GRAPES非线性模式的合理线性模式为建立基于线性扰动预报的区域四维变分同化系统奠定了科学基础。      

用一种新的同化方法同化降水量资料

Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard four- dimensional variational data assimilation at a much lower computational cost.     

The Time and Regime Dependencies of Sensitive Areas for Tropical Cyclone Prediction Using the CNOP Method

This study examines the time and regime dependencies of sensitive areas identified by the conditional nonlinear optimal perturbation(CNOP) method for forecasts of two typhoons.Typhoon Meari(2004) was weakly nonlinear and is herein referred to as the linear case,while Typhoon Matsa(2005) was strongly nonlinear and is herein referred to as the nonlinear case.In the linear case,the sensitive areas identified for special forecast times when the initial time was fixed resembled those identified for other forecast times.Targeted observations deployed to improve a special time forecast would thus also benefit forecasts at other times.In the nonlinear case,the similarities among the sensitive areas identified for different forecast times were more limited.The deployment of targeted observations in the nonlinear case would therefore need to be adapted to achieve large improvements for different targeted forecasts.For both cases,the closer the forecast time,the higher the similarities of the sensitive areas.When the forecast time was fixed,the sensitive areas in the linear case diverged continuously from the verification area as the forecast period lengthened,while those in the nonlinear case were always located around the initial cyclones.The deployment of targeted observations to improve a special forecast depends strongly on the time of deployment.An examination of the efficiency gained by reducing initial errors within the identified sensitive areas confirmed these results.In general,the greatest improvement in a special time forecast was obtained by identifying the sensitive areas for the corresponding forecast time period.     

数值天气预报和气候预测可预报性研究的若干动力学方法

简要回顾了数值天气预报和气候预测可预报性研究的若干动力学方法,包括用于研究第一类可预报性问题的线性奇异向量（LSV）和条件非线性最优初始扰动（CNOP-I）方法,以及Lyapunov指数和非线性局部Lyapunov指数方法。前两种方法用于研究预报或预测的预报误差问题,可以用于估计天气预报和气候预测的最大预报误差,而且根据导致最大预报误差的初始误差结构的信息,这两种方法可以用于确定预报或预测的初值敏感区。应该指出的是,LSV是基于线性化模式,对于描述非线性大气和海洋的运动具有局限性。因而,对于非线性模式,应该选择使用CNOP-I估计最大预报误差。Lyapunov指数和非线性局部Lyapunov指数可以用于研究第一类可预报性问题中的预报时限问题,前者是基于线性模式,不能解释非线性对预报时限的影响,而非线性局部Lyapunov指数方法则考虑了非线性的影响,能够较好地估计实际天气和气候的预报时限。第二类可预报性问题的研究方法相对较少,本文仅介绍了由我国科学家提出的关于模式参数扰动的条件非线性最优参数扰动（CNOP-P）方法,该方法可以用于寻找到对预报有最大影响的参数扰动,并可以进一步确定哪些参数最应该利用观测资料进行校准。另一方面,通过对比CNOP-I和CNOP-P对预报误差的影响,可以判断导致预报不确定性的主要误差因子,进而指导人们着力改进模式或者初始场。     

Reducing the prediction uncertainties of high-impact weather and climate events: An overview of studies at LASG

This paper summarizes recent progress at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences in studies on targeted observations, data assimilation, and ensemble prediction, which are three effective strategies to reduce the prediction uncertainties and improve the forecast skill of weather and climate events. Considering the limitations of traditional targeted observation approaches, LASG researchers have developed a conditional nonlinear optimal perturbation-based targeted observation strategy to optimize the design of the observing network. This strategy has been employed to identify sensitive areas for targeted observations of the El Niño–Southern Oscillation, Indian Ocean dipole, and tropical cyclones, and has been demonstrated to be effective in improving the forecast skill of these events. To assimilate the targeted observations into the initial state of a numerical model, a dimension-reducedprojection- based four-dimensional variational data assimilation (DRP-4DVar) approach has been proposed and is used operationally to supply accurate initial conditions in numerical forecasts. The performance of DRP-4DVar is good, and its computational cost is much lower than the standard 4DVar approach. Besides, ensemble prediction, which is a practical approach to generate probabilistic forecasts of the future state of a particular system, can be used to reduce the prediction uncertainties of single forecasts by taking the ensemble mean of forecast members. In this field, LASG researchers have proposed an ensemble forecast method that uses nonlinear local Lyapunov vectors (NLLVs) to yield ensemble initial perturbations. Its application in simple models has shown that NLLVs are more useful than bred vectors and singular vectors in improving the skill of the ensemble forecast. Therefore, NLLVs represent a candidate for possible development as an ensemble method in operational forecasts. Despite the considerable efforts made towards developing these methods to reduce prediction uncertainties, much challenging but highly important work remains in terms of improving the methods to further increase the skill in forecasting such weather and climate events.     

GRAPES区域集合预报尺度混合初始扰动构造的新方案

集合预报初始扰动能否准确反映预报误差的结构特征是决定区域集合预报质量的关键因素之一。本文针对GRAPES区域数值预报模式,发展设计了一种基于资料同化思想的混合尺度初始扰动构造新方案。该方案以全球大尺度信息为背景场,区域模式预报作为观测资料,借助GRAPES三维变分同化系统,将高质量的全球大尺度信息与区域模式预报中质量较高的中小尺度信息有效融合,构造混合尺度区域集合预报初始扰动,并通过个例试验和批量试验,比较分析了新方案和原区域集合预报的性能。试验结果表明,基于资料同化构造的初始扰动能够有效融合全球大尺度信息和中小尺度天气系统的信息,其降水概率预报更具参考价值。总体上看,区域集合预报混合初始扰动新方案能够较好地改进区域集合预报质量,尤其是对高度场和温度场效果更为显著,但对风场的集合预报性能影响略小。     

Nonlinear measurement function in the ensemble Kalman filter

ABSTRACT The optimal Kalman gain was analyzed in a rigorous statistical framework. Emphasis was placed on a comprehensive understanding and interpretation of the current algorithm, especially when the measurement function is nonlinear. It is argued that when the measurement function is nonlinear, the current ensemble Kalman Filter algorithm seems to contain implicit assumptions： the forecast of the measurement function is unbiased or the nonlinear measurement function is linearized. While the forecast of the model state is assumed to be unbiased, the two assumptions are actually equivalent. On the above basis, we present two modified Kalman gain algorithms. Compared to the current Kalman gain algorithm, the modified ones remove the above assumptions, thereby leading to smaller estimated errors. This outcome was confirmed experimentally, in which we used the simple Lorenz 3-component model as the test-bed. It was found that in such a simple nonlinear dynamical system, the modified Kalman gain can perform better than the current one. However, the application of the modified schemes to realistic models involving nonlinear measurement functions needs to be further investigated.     

New approach for optimal perturbation method in ensemble climate prediction with empirical singular vector

In this study, a new method is developed to generate optimal perturbations in ensemble climate prediction. In this method, the optimal perturbation in initial conditions is the 1st leading singular vector, calculated from an empirical linear operator based on a historical model integration. To verify this concept, this method is applied to a hybrid coupled model. It is demonstrated that the 1st leading singular vector from the empirical linear operator, to a large extent, represents the fast-growing mode in the nonlinear integration. Therefore, the forecast skill with the optimal perturbations is improved over most lead times and regions. In particular, the improvement of the forecast skill is significant where the signal-to-noise ratio is small, indicating that the optimal perturbation method is effective when the initial uncertainty is large. Therefore, the new optimal perturbation method has the potential to improve current seasonal prediction with state-of-the-art coupled GCMs.