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基于WRF模式（Weather Research and Forecasting Model）分析2020年超长梅汛期内11次强降水事件的预报误差来源。分别以FNL（Final Global Data Assimilation System）、TIGGE_EC（THORPEX Interactive Grand Global Ensemble from European Centre for Medium-Range Weather Forecasts）作为初始场进行预报,对比预报结果发现,TIGGE_EC初始场的预报结果普遍优于FNL,这说明初始条件的不确定性对预报结果有重要影响。进一步探究初始条件不确定性（初始误差）来源的区域（敏感区）和变量（敏感变量）发现,敏感区集中分布于降水区西侧上游,相对应的敏感变量为水汽场。分别考察动能、有效位能以及比湿能在初始误差总能量中的占比,结果表明,扰动比湿能占比最小,但敏感性试验 表明比湿场扰动对预报效果的影响最大。选取比湿场扰动对预报效果影响最大且WRF_EC具有更好预报效果的6个暴雨事件,通过HYSPLIT后向轨迹模式分别追踪其累计降水量最大值点的水汽来源及路径发现,有6个事件均有向降水区西侧上游延伸的水汽来源通道,进一步表明了敏感区的初始水汽场的准确性对暴雨预报的影响。因此降水区西侧上游的水汽场的误差是这11次梅汛期暴雨过程重要的预报误差来源,对其准确描述可有助于预报效果的提升。 相似文献
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加强目标观测,服务防灾减灾 总被引：3，自引：0，他引：3
以深入浅出的方式介绍了目标观测的定义、历史、确定目标观测敏感区的方法,以及实施目标观测与防灾减灾的关系;并以欧洲中期天气预报中心目标观测业务化为例,简明地概括了目标观测的实施过程;结合国内目标观测研究现状,对中国目标观测的未来提出了看法。 相似文献
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The mesoscale balance and imbalance and the corresponding potential vorticity inversion from the view of helicity 
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下载免费PDF全文 The static balance and the geostrophic balance are the common balances in meteorology.All the synoptic systems and most of the mesoscale systems satisfy the above two balances.However,due to the strong convection and non-geostrophic feature,many mesoscale systems usually present as static imbalance,and the quasi-geostrophic approximation is no longer attainable.This paper tried to find out a kind of balance that exists for mesoscale convective system.To do this,the concrete mathematics definitions for balance and imbalance equations were defined.Then,it is proposed that the new balance equation should include the divergence,vorticity,and vertical motion simultaneously,and the helicity equation was a good choice for the basis.Finally,the mesoscale balance and imbalance equations were constructed,as well as a new balance model that was based on the helicity,horizontal divergence,vertical vorticity,continuity,and thermal dynamic equations under same approximations.Moreover,the corresponding potential vorticity(PV)inversion technique was introduced.It was pointed out that by using the PV conservation and the potential temperature conservation,the flows of the mesoscale balance model can be deduced,and their comparison with the real fields would give the degree of the imbalance. 相似文献
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基于CNOP方法的台风目标观测中三种敏感区
确定方案的比较研究 总被引：1，自引：0，他引：1
在目标观测中,敏感区的确定是个关键性的问题。本文详细研究了如何用条件非线性最优扰动（CNOP）方法确定敏感区。提出了三种确定敏感区的方案:水平投影方案、单点能量投影方案以及垂直积分能量方案。比较了三种方案确定的敏感区的差异,分析了它们所阐释的物理意义,讨论了它们的优缺点,并通过理想回报试验考查了不同方案确定的敏感区的有效性。对六个台风个例的应用结果显示,单点能量投影方案与垂直积分能量方案下识别的敏感区较为相似,二者与水平投影方案确定的敏感区则有较大的区别。两种能量方案确定的敏感区更多地反映了环境场对台风的影响,而水平投影方案则反映了台风自身对流不对称性结构对台风发展变化的影响。理想回报试验结果表明,由两种能量方案确定的敏感区对预报误差能量的减小程度以及路径预报的改善程度都要大于水平投影方案确定的敏感区的效果,且垂直积分能量方案确定的敏感区的有效性最高。而在强度预报方面,三种方案对预报效果的改善程度相当。因此,总的说在台风目标观测研究中,利用CNOP方法确定敏感区时,垂直积分能量方案是较佳的方案。 相似文献
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This study presents a new way to identify the sensitive areas for typhoon adaptive observations, that is, sensitive areas are determined by invoking the negative anomalies of moist potential vorticity (MPV). It is found that the areas of negative MPV are the symmetric instability areas and can be taken as sensitive areas for typhoon adaptive observations. Three typhoons in 2008, Nuri, Fung-wong, and Fengshen, were simulated with the help of MM5 model. It is shown that these typhoons are well simulated in the first 12 hours. Based on these investigations, the calculations of MPV are carried out sequentially. The result shows that the negative maxima of MPV are always around the typhoon eyes for all the cases, which means that the sensitive areas are also near them all the time. 相似文献
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为了提高南海台风的数值预报,利用条件非线性最优扰动(CNOP)方法寻找对南海台风预报影响最大的区域(敏感区),并对该区域中会导致较大预报误差的初始扰动场进行了详细地分析.对两个南海台风个例地研究发现,CNOP的风场和温度场结构在各σ层上不完全一致.计算了CNOP各σ层上的干能量,并在垂直方向上做了积分,将积分后的能量大值区确定为敏感区.进一步对敏感区内温度场的垂直剖面进行分析,发现敏感区内,温度场呈现一定的斜压结构.研究还发现,敏感区与台风的对流活动区域不完全重合,这有利于台风适应性观测地实施. 相似文献
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利用条件非线性最优扰动(CNOP)方法,对2002年发生在西太平洋上的台风“风神”和“凤凰”之间的相互作用进行研究。CNOP方法可揭示出“风神”对“凤凰”单向引导作用的过程,表现为若将“凤凰”所在区域作为验证区域,用CNOP方法识别的敏感区主要位于“风神”所在区域,呈现出环绕“风神”的半环状结构;若将“风神”所在区域作为验证区域,则CNOP方法所识别的敏感区主要位于“风神”与副高交界的地方,远离“凤凰”所在的区域,可见,“风神”主要受副高的影响。敏感性试验表明,CNOP所识别的敏感区内误差的发展要大于台风中心周围区域内初始误差的发展,且在全场误差的发展中占有较大的比重,说明CNOP所识别的敏感区对验证区域的预报有较大的影响。 相似文献
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Some intelligent algorithms (IAs) proposed by us, including swarm IAs and single individual IAs, have been applied to the Zebiak-Cane (ZC) model to solve conditional nonlinear optimal perturbation (CNOP) for studying El Ni?o – Southern Oscillation (ENSO) predictability. Compared to the adjoint-based method (the ADJ-method), which is referred to as a benchmark, these IAs can achieve approximate CNOP results in terms of magnitudes and patterns. Using IAs to solve CNOP can avoid the use of an adjoint model and widen the application of CNOP in numerical climate and weather modeling. Of the proposed swarm IAs, PCA-based particle swarm optimization (PPSO) obtains CNOPs with the best patterns and the best stability. Of the proposed single individual IAs, continuous tabu search algorithm with sine maps and staged strategy (CTS-SS) has the highest efficiency. In this paper, we compare the validity, stability and efficiency of parallel PPSO and CTS-SS using these two IAs to solve CNOP in the ZC model for studying ENSO predictability. The experimental results show that CTS-SS outperforms parallel PPSO except with respect to stability. At the same time, we are also concerned with whether these two IAs can effectively solve CNOP when applied to more complicated models. Taking the sensitive areas identification of tropical cyclone adaptive observations as an example and using the fifth-generation mesoscale model (MM5), we design some experiments. The experimental results demonstrate that each of these two IAs can effectively solve CNOP and that parallel PPSO has a higher efficiency than CTS-SS. We also provide some suggestions on how to choose a suitable IA to solve CNOP for different models. 相似文献
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本文通过深入分析伴随敏感性（ADS）方法、第一奇异向量（LSV）方法、以及条件非线性最优扰动（CNOP）方法在目标观测敏感区识别方面的原理,提出了非线性程度的概念和计算方法,考察了转向型和直线型台风的非线性程度,分析了上述三种方法在不同非线性程度下识别的敏感区的异同,同时对比了转向型和直线型台风的敏感区的差异,并通过敏感性试验探讨了在不同非线性程度下以及在转向型与直线型台风中,预报对敏感区内初值的敏感性程度,进而探讨台风目标观测在不同情况下的有效性。结果表明,转向型台风的非线性程度差别比较大,或者特别强,或者特别弱;而直线型台风非线性程度居中,不同台风个例之间的非线性程度差别较小。对于非线性较弱的台风,三种方法识别的敏感区较为相似,而对于非线性较强的台风,LSV方法与ADS方法识别的敏感区较为相似,但是与CNOP方法识别的敏感区具有较大的差别。对于转向型台风,敏感区主要位于行进路径的右前方,而对于直线型台风,敏感区主要位于初始台风位置的后方。敏感性试验表明,不论台风非线性强弱,转向还是直行,CNOP敏感区内的随机扰动发展最大,而LSV敏感区内叠加的随机扰动发展次之,ADS敏感区内叠加的扰动发展最小;此外,非线性弱的台风,扰动的发展大于非线性强的台风的扰动的发展,表明非线性弱的台风预报受初值影响更大,目标观测的效果可能会更明显。 相似文献