The Impact of Horizontal Resolution on the CNOP and on Its Identified Sensitive Areas for Tropical Cyclone Predictions

In this study,the impacts of horizontal resolution on the conditional nonlinear optimal perturbation (CNOP) and on its identified sensitive areas were investigated for tropical cyclone predictions.Three resolutions,30 km,60 km,and 120 km,were studied for three tropical cyclones,TC Mindulle (2004),TC Meari (2004),and TC Matsa (2005).Results show that CNOP may present different structures with different resolutions,and the major parts of CNOP become increasingly localized with increased horizontal resolution.CNOP produces spiral and baroclinic structures,which partially account for its rapid amplification.The differences in CNOP structures result in different sensitive areas,but there are common areas for the CNOP-identified sensitive areas at various resolutions,and the size of the common areas is different from case to case.Generally,the forecasts benefit more from the reduction of the initial errors in the sensitive areas identified using higher resolutions than those using lower resolutions.However,the largest improvement of the forecast can be obtained at the resolution that is not the highest for some cases.In addition,the sensitive areas identified at lower resolutions are also helpful for improving the forecast with a finer resolution,but the sensitive areas identified at the same resolution as the forecast would be the most beneficial.     

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.     

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

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。     

A Comparison Study of the Contributions of Additional Observations in the Sensitive Regions Identified by CNOP and FSV to Reducing Forecast Error Variance for the Typhoon Morakot

The sensitive regions of conditional nonlinear optimal perturbations (CNOPs) and the first singular vector (FSV) for a northwest Pacific typhoon case are reported in this paper. A large number of probes have been designed in the above regions and the ensemble transform Kalman filter (ETKF) techniques are utilized to examine which approach can locate more appropriate regions for typhoon adaptive observations. The results show that, in general, the majority of the probes in the sensitive regions of CNOPs can reduce more forecast error variance than the probes in the sensitive regions of FSV. This implies that adaptive observations in the sensitive regions of CNOPs are more effective than in the sensitive regions of FSV. Furthermore, the reduction of the forecast error variance obtained by the best probe identified by CNOPs is twice the reduction of the forecast error variance obtained by FSV. This implies that dropping sondes, which is the best probe identified by CNOPs, can improve the forecast more than the best probe identified by FSV. These results indicate that the sensitive regions identified by CNOPs are more appropriate for adaptive observations than those identified by FSV.     

Impact of Different Guidances on Sensitive Areas of Targeting Observations Based on the CNOP Method

The conditional nonlinear optimal perturbations(CNOPs) obtained by a fast algorithm are applied to determining the sensitive area for the targeting observation of Typhoon Matsa in 2005 using an operational regional prediction model-the Global/Regional Assimilation and PrEdiction System(GRAPES).Through a series of sensitivity experiments,several issues on targeting strategy design are discussed,including the effectivity of different guidances to determine the sensitive area(or targeting area) and the impa...     

Observation System Experiments for Typhoon Nida (2004) Using the CNOP Method and DOTSTAR Data

This study investigated the influence of dropwindsonde observations on typhoon forecasts. The study also evaluated the feasibility of the conditional nonlinear optimal perturbation (CNOP) method as a basis for sensitivity analysis of such forecasts. This sensitivity analysis could furnish guidance in the selection of targeted observations. The study was performed by conducting observation system experiments (OSEs). This research used the fifth-generation Mesoscale Model (MM5), the Weather Research and Forecasting (WRF) model, and dropsonde observations of Typhoon Nida at 1200 UTC 17 May 2004. The dropsondes were collected under the operational Dropsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) program. In this research, five kinds of experiments were designed and conducted:(1) no observations were assimilated; (2) all observations were assimilated;(3) observations in the sensitive area revealed by the CNOP method were assimilated;(4) the same as in (3), but for the region revealed by the first singular vector (FSV) method;and (5) observations within a randomly selected area were assimilated. The OSEs showed that (1) the DOTSTAR data had a positive impact on the forecast of Nida’s track;(2) dropsondes in the sensitive areas identified by the MM5 CNOP and FSV remained effective for improving the track forecast for Nida on the WRF platform;and (3) the greatest improvement in the track forecast resulted from the CNOP-based (third) simulation, which indicated that the CNOP method would be useful in decision making about dropsonde deployments.     

The Impact of Moist Physics on the Sensitive Area Identification for Heavy Rainfall Associated Weather Systems

The impact of moist physics on the sensitive areas identified by conditional nonlinear optimal perturbation(CNOP)is examined based on four typical heavy rainfall cases in northern China through performing numerical experiments with and without moist physics.Results show that the CNOP with moist physics identifies sensitive areas corresponding to both the lower-(850?700 hPa)and upper-level(300?100 hPa)weather systems,while the CNOP without moist physics fails to capture the sensitive areas at lower levels.The reasons for the CNOP peaking at different levels can be explained in both algorithm and physics aspects.Firstly,the gradient of the cost function with respect to initial perturbations peaks at the upper level without moist physics which results in the upper-level peak of the CNOP,while it peaks at both the upper and lower levels with moist physics which results in both the upper-and lower-level peaks of the CNOP.Secondly,the upper-level sensitive area is associated with high baroclinicity,and these dynamic features can be captured by both CNOPs with and without moist physics.The lower-level sensitive area is associated with moist processes,and this thermodynamic feature can be captured only by the CNOP with moist physics.This result demonstrates the important contribution of the initial error of lower-level systems that are related to water vapor transportation to the forecast error of heavy rainfall associated weather systems,which could be an important reference for heavy rainfall observation targeting.     

The Roles of Spatial Locations and Patterns of Initial Errors in the Uncertainties of Tropical Cyclone Forecasts

In this study,a series of sensitivity experiments were performed for two tropical cyclones (TCs),TC Longwang (2005) and TC Sinlaku (2008),to explore the roles of locations and patterns of initial errors in uncertainties of TC forecasts.Specifically,three types of initial errors were generated and three types of sensitive areas were determined using conditional nonlinear optimal perturbation (CNOP),first singular vector (FSV),and composite singular vector (CSV) methods.Additionally,random initial errors in randomly selected areas were considered.Based on these four types of initial errors and areas,we designed and performed 16 experiments to investigate the impacts of locations and patterns of initial errors on the nonlinear developments of the errors,and to determine which type of initial errors and areas has the greatest impact on TC forecasts.Overall,results from the experiments indicate the following:(1) The impact of random errors introduced into the sensitive areas was greater than that of errors themselves fixed in the randomly selected areas.From the perspective of statistical analysis,and by comparison,the impact of random errors introduced into the CNOP target area was greatest.(2) The initial errors with CNOP,CSV,or FSV patterns were likely to grow faster than random errors.(3) The initial errors with CNOP patterns in the CNOP target areas had the greatest impacts on the final verification forecasts.     

An Adjoint-Free CNOP–4DVar Hybrid Method for Identifying Sensitive Areas in Targeted Observations: Method Formulation and Preliminary Evaluation

This paper proposes a hybrid method, called CNOP–4 DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation(CNOP) and four-dimensional variational assimilation(4 DVar) methods. The proposed CNOP–4 DVar method is capable of capturing the most sensitive initial perturbation(IP), which causes the greatest perturbation growth at the time of verification; it can also identify sensitive areas by evaluating their assimilation effects for eliminating the most sensitive IP. To alleviate the dependence of the CNOP–4 DVar method on the adjoint model, which is inherited from the adjoint-based approach, we utilized two adjointfree methods, NLS-CNOP and NLS-4 DVar, to solve the CNOP and 4 DVar sub-problems, respectively. A comprehensive performance evaluation for the proposed CNOP–4 DVar method and its comparison with the CNOP and CNOP–ensemble transform Kalman filter(ETKF) methods based on 10 000 observing system simulation experiments on the shallow-water equation model are also provided. The experimental results show that the proposed CNOP–4 DVar method performs better than the CNOP–ETKF method and substantially better than the CNOP method.     

Preliminary Study of Sensitive Areas for Several Tropical Cyclone Track Prediction Cases in 2007

Conditional nonlinear optimal perturbation (CNOP) obtained by the ensemble-based calculation method is employed to find possible sensitive areas for improving 48-h or more than 48-h tropical cyclone (TC) track predictions in several cases affecting China in 2007. These sensitive areas are examined by observing system simulation experiments (OSSEs). Results show that these sensitive areas improve TC track predictions for 48 h or more to different extents. Further analysis is performed to determine the distribution characteristics of sensitive areas in these cases. Results show that areas south of Luzon and over surrounding oceans are significant for 48-h or more than 48-h TC track predictions, especially 60-h to 72-h track predictions. Areas over oceans north or east to Taiwan Island seem to be secondary sensitive for 48-h or more than 48-h TC track predictions.     

混合层深度对热带气旋强度的影响

利用建立的中尺度海气耦合模式进行一组敏感性试验,以考察初始混合层深度对热带气旋(TC)强度的影响。试验结果表明,初始混合层深度对TC最大强度和增强时间影响都较大。一般来说,初始混合层越深,模拟的TC最大强度越大,TC增强时间越长。另外,TC与混合层深度的关系并不是线性的。当混合层较浅时,TC强度对其变化更为敏感。     

登陆北上热带气旋的特大暴雨落区探讨

由台风降水资料分析发现,登陆北上热带气旋在我国北方地区造成的特大暴雨,集中出现在华北平原西北部和山东￣辽东半岛东南部两个相距较远的区域内。对比分析表明,这两个特大暴雨落区的形成,除地形增幅因素外,主要与前期副热带高压位置、热带气旋北上路径及物理量场的分布状况直接相关。同时,相应给出了判别上述两个特大暴雨落区的预报指标和方法。     

热带气旋预报性能及指标综合评述

热带气旋(TC)位居全球十大自然灾害之首,受到世界范围内的广泛重视。为了解当前国际上TC预报的整体水平,更好地审视我国TC预报的进展及在国际上的位置,基于主要国家和国际机构的TC预报性能评估项目,本文对近年来各预报中心的TC业务预报水平进行了客观对比分析。结果表明,过去十几年来,我国的TC路径预报水平有大幅度提高,已经跻身世界先进行列。各国对TC强度预报性能的提升预计还会经历一个漫长的过程,未来台风风雨预报的评估发展值得期待。     

Effects of Dropsonde Data in Field Campaigns on Forecasts of Tropical Cyclones over the Western North Pacific in 2020 and the Role of CNOP Sensitivity

Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones, namely Higos,Nangka, Saudel, and Atsani, over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weather Bureau in 2020. The conditional nonlinear optimal perturbation(CNOP) method has been utilized in real-time to identify the sensitive regions for targeting observations adhering to the procedure of real-time field campaigns for the first time. The observing system experiments...     

基于条件非线性最优扰动的目标观测中瞄准区 不同引导性变量的影响试验研究

基于GRAPES区域业务预报模式,采用一种快速算法计算出来的条件非线性最优扰动对实际台风个例麦莎(No.0509)开展了目标观测研究,应用数值模式,进行一系列的敏感性试验,讨论了与目标观测设计相关的一些问题,包括确定瞄准区时使用不同的引导性变量对目标观测效果的影响、及瞄准区范围变化对预报效果的影响。文中分别以提高麦莎在检验区(20.125°—35.3125°N,116.8125°—129.75°E)内的24 h海平面气压预报和24 h累积降水量预报为目的,基于条件非线性最优扰动使用了3种不同的引导性变量寻找敏感区(又称瞄准区),对这些敏感区的分布特点和有效性进行了比较和讨论。试验结果表明,在使用的3种引导性变量中,用不同的引导性变量识别的敏感区是有差别的,总体上说,文中使用的3种引导性变量识别的瞄准区对提高预报都是有效的,特别是第2和第3种的效果更好些,且两者识别的瞄准区常显示出类似的特点。文中进一步针对检验区内24 h累积降水量预报误差问题,将前面确定的瞄准区范围扩大相同的幅度,讨论瞄准区范围变化对改进预报的影响。试验结果表明,增加瞄准格点数,有可能使预报效果得到改善,但是试验结果同时也暗示了单纯靠扩大瞄准...     

华东地区热带气旋暴雨气候特征及其落区预报

作者分析了近十几年来华东热带气旋暴雨的主要特征，并以热力，动力参数相结合，采用合成对比方法，进行诊断分析，然后使用数值产品制作热带气旋暴雨落区的概率预报。     

Geometric Characteristics of Tropical Cyclone Eyes before Landfall in South China Based on Ground-Based Radar Observations

The geometric characteristics of tropical cyclone(TC) eyes before landfall in South China are examined using groundbased radar reflectivity. It is found that the median and mean eye area decrease with TC intensity, except for the severe typhoon category, and the eye size increases with height. The increasing rate of eye size is relatively greater in upper layers.Moreover, the ratio of eye size change in the vertical direction does not correlate with TC intensity. No relationship is presented between the ratio of eye size change in the vertical direction and the vertical wind shear. No relationship between the vertical change in eye size and the eye size at a certain level is found, inconsistent with other studies. No relationship exists between the vertical change in eye size and the intensity tendency. The eye roundness values range mainly from 0.5 to 0.7, and more intense TCs generally have eyes that are more circular.     

鞍型场对热带气旋移动路径影响的研究

鞍型场是一种特殊的流场,鞍点附近气压梯度很小,风场很弱,引导气流不明显。当热带气旋进入鞍型场时,会表现为停滞、打转、突然转向等异常路径。采用NCEP资料对2011年热带气旋"梅花"路径和大尺度形势场分析结果表明,在其整个生命史中出现三次移动路径转折,热带气旋进入鞍型场后,出现在鞍点附近停滞、移动缓慢、突然转向等异常路径。利用理想变形场和兰金涡旋相结合的鞍型场作为背景场,用梯度风平衡构造了理想热带气旋,分别考虑了科氏参数为常数和β平面近似两种情况,利用二维浅水模式对热带气旋处于鞍点和收缩轴附近不同位置的情况进行了数值对比试验。当科氏参数为常数时,处于鞍点位置的热带气旋在鞍点停滞;而当采用β平面近似时,处于鞍点位置的热带气旋向东北低压一侧移动,并和低压合并。当初始热带气旋处于鞍点南侧的收缩轴上以及偏东或偏西100 km时,会出现三种截然不同的移动路径。试验结果表明:鞍型场对热带气旋有"陷阱"作用。当热带气旋进入到鞍型场区并接近鞍点时,在β漂移和弱引导气流作用下,可能陷入鞍点附近区域,移动缓慢,出现异常路径,也可能沿着膨胀轴向偏东或偏西方向移动,使得热带气旋突然转向。热带气旋的移动对其和收缩轴的相对位置十分敏感,数十千米的偏差可能会导致截然相反的移动路径。     

MJO随机强迫对CNOP型初始误差演变的影响

用Zebiak-Cane模式和季节内振荡(Madden-Julian Oscillation,MJO)的参数化表述以及条件非线性最优扰动(Conditional Nonlinear Optimal Perturbation,CNOP)方法,分析了以ENSO事件为基态的CNOP型初始误差的空间结构增长规律。结果表明,参数化的MJO对CNOP型初始误差的发展影响较小,其影响主要是使中东太平洋的海表面温度异常增大。CNOP型初始误差比由MJO不确定性产生的模式误差的影响大,前者可能是造成ENSO事件预报不确定性的主要误差来源。由于CNOP型初始误差的局地性,本结论可用来指导ENSO的目标观测和适应性资料同化。     

台湾岛地形诱生次级环流系统对热带气旋异常运动的影响机制

对经过中国台湾岛和海南岛、吕宋岛、日本诸岛以及朝鲜半岛的热带气旋在过岛前后的运动、结构和强度的时空变化进行了统计诊断分析。研究结果表明,台湾岛附近热带气旋运动左偏（定义为偏于以前路径的左侧）机率最大,且台湾岛周围是产生诱生低压的高频区。采用理想东、西风环境场作为数值模拟背景场,数值研究了岛屿地形强迫与台湾附近的环境流场的相互作用及其对热带气旋运动偏转的影响,提出了岛屿地形强迫、背景场和热带气旋涡旋三者相互作用对热带气旋路径突然转折影响的观点,即台湾地形有利于环境场中诱生出一对偏差偶极涡,这对诱生偏差偶极涡将导致逼近岛屿的热带气旋产生运动方向的突然偏折,且在不同基本气流条件下,岛屿地形对热带气旋运动可能产生显著不同的影响。