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 were conducted to evaluate the effect of dropsonde data and CNOP sensitivity on TC forecasts in terms of track and intensity,using the Weather Research and Forecasting model.It is shown that the impact of assimilating all dropsonde data on both track and intensity forecasts is case-dependent.However,assimilation using only the dropsonde data inside the sensitive regions displays unanimously positive effects on both the track and intensity forecast,either of which obtains comparable benefits to or greatly reduces deterioration of the skill when assimilating all dropsonde data.Therefore,these results encourage us to further carry out targeting observations for the forecast of tropical cyclones according to CNOP sensitivity.

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。

条件非线性最优扰动(CNOP):简介与数值求解

介绍了条件非线性最优扰动(Conditional Nonlinear Optimal Perturbation,CNOP)的定义及其在大气和海洋等可预报性研究中的应用。根据研究对象不同,CNOP分为与初始扰动有关的CNOP(CNOP-I)方法、与模式参数扰动有关的CNOP(CNOP-P)方法和同时考虑初始扰动和模式参数扰动的CNOP方法。目前,CNOP-I方法已经应用于ENSO、黑潮和阻塞可预报性以及热盐环流和草原生态系统稳定性的研究。此外,CNOP-I方法也被应用于探讨台风目标观测的研究,利用CNOP-I方法能够识别出台风预报的初值敏感区,通过观测系统模拟试验表明在初值敏感区增加观测能够有效改进台风的预报技巧。CNOP-P方法也在ENSO和黑潮可预报性以及热盐环流和草原生态系统稳定性研究中得到了应用。为了将CNOP方法应用于更多的领域,本文利用一个简单的Burgers方程,介绍了如何通过建立Burgers方程的切线性模式和伴随模式,从而利用非线性最优化算法计算获得CNOP。这一数值试验为将CNOP方法应用于更多的领域提供了借鉴。

基于CNOP方法的台风目标观测研究进展

简要但系统地介绍了条件非线性最优扰动(CNOP)方法在台风目标观测方面的研究进展。CNOP方法是线性奇异向量(SV)方法在非线性领域的一个拓展。在台风目标观测的研究中,该方法主要用来识别对台风预报有重要影响的敏感区,从而可以在这些敏感区内增加观测,改进初始场以提高预报技巧。首先回顾了CNOP方法在台风目标观测中应用的理论基础,接着阐述了CNOP识别的敏感区受模式分辨率、验证区域的设计、优化时长的选取等因素的影响,并给出了利用观测系统模拟试验(OSSE)和观测系统试验(OSE)对CNOP识别的敏感区有效性检验的结果,进一步评述了将CNOP方法应用于实际天气业务预报中进行敏感区识别的可能性,最后对CNOP方法在台风目标观测中的深入应用进行了总结和讨论。

CHOP与CNOP治疗非何杰金氏淋巴瘤的对比观察

ＣＨＯＰ与ＣＮＯＰ治疗非何杰金氏淋巴瘤的对比观察张应潮，王桃仙，姚文秀，尹绪德，付彬玉，滕彩勋，余萍，侯梅四川省肿瘤医院（成都市６１００４１）１９９１年１月～１９９３年１２月我院用国产米托蒽醌（ＭｉｔｏｘａｎｔｒｏｎｅＮｏｖａｎｔｒｏｎｅＮＶＴ）与阿...

The Impact of Verification Area Design on Tropical Cyclone Targeted Observations Based on the CNOP Method

This study investigated the impact of different verification-area designs on the sensitive areas identified using the conditional nonlinear optimal perturbation （CNOP） method for tropical cyclone targeted observations.The sensitive areas identified using the first singular vector （FSV） method,which is the linear approximation of CNOP,were also investigated for comparison.By analyzing the validity of the sensitive areas,the proper design of a verification area was developed.Tropical cyclone Rananim,which occurred in August 2004 in the northwest Pacific Ocean,was studied.Two sets of verification areas were designed;one changed position,and the other changed both size and position.The CNOP and its identified sensitive areas were found to be less sensitive to small variations of the verification areas than those of the FSV and its sensitive areas.With larger variations of the verification area,the CNOP and the FSV as well as their identified sensitive areas changed substantially.In terms of reducing forecast errors in the verification area,the CNOP-identified sensitive areas were more beneficial than those identified using FSV.The design of the verification area is important for cyclone prediction.The verification area should be designed with a proper size according to the possible locations of the cyclone obtained from the ensemble forecast results.In addition,the development trend of the cyclone analyzed from its dynamic mechanisms was another reference.When the general position of the verification area was determined,a small variation in size or position had little influence on the results of CNOP.

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

In this study, the ilnpacts 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 kin, 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 optiflml 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.

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.

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.

CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm

Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.

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.

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。

CHOP与CNOP治疗非何杰金氏淋巴瘤的对比观察报告

ＣＨＯＰ（ＣＴＸ、ＡＤＭ、ＶＣＲ、ＰＤＮ）联合化疗方案是治疗非何杰金氏淋巴瘤（Ｎｏｎ—Ｈｏｄｇｋｉｎ′ｓＬｙｍｐｈｏｍａＮＨＬ）常用的、有效的方法之一。米托蒽醌是一有效的、广谱的、细胞周期非特异性抗癌药，化学结构与阿霉素相似，抗癌活性与ＡＤＭ相当或略高，它无氨基糖结构，心脏毒性较ＡＤＭ低。对确诊为ＮＨＬ３６例住院患者，随机分为ＣＨＯＰ组和ＣＮＯＰ组各１８例作临床对比观察。ＣＨＯＰ组ＣＲ２例、ＰＲ７例，有效率５０％（９／１８）；ＣＮＯＰ组ＣＲ４例、ＰＲ７例，有效率６１．１％（１１／１８），两组疗效相似（Ｐ＞０．０５）。

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 impact of sensitive area size on improving the 24-h forecast.In this study,three guidances are used along with the CNOP to find sensitive area for improving the 24-h prediction of sea level pressure and accumulated rainfall in the verification region.The three guidances are based on winds only;on winds,geopotential height,and specific humidity;and on winds,geopotential height,specific humidity,and observation error,respectively.The distribution and effectivity of the sensitive areas are compared with each other,and the results show that the sensitive areas identified by the three guidances are different in terms of convergence and effectivity.All the sensitive areas determined by these guidances can lead to improvement of the 24-h forecast of interest. The second and third guidances are more effective and can identify more similar sensitive areas than the first one.Further,the size of sensitive areas is changed the same way for three guidances and the 24-h accumulated rainfall prediction is examined.The results suggest that a larger sensitive area can result in better prediction skill,provided that the guidance is sensitive to the size of sensitive areas.

A Fast Algorithm for Solving CNOP and Associated Target Observation Tests

Conditional Nonlinear Optimal Perturbation （CNOP） is a new method proposed by Mu et al. in 2003, which generalizes the linear singular vector （LSV） to include nonlinearity. It has become a powerful tool for studying predictability and sensitivity among other issues in nonlinear systems. This is because the CNOP is able to represent, while the LSV is unable to deal with, the fastest developing perturbation in a nonlinear system. The wide application of this new method, however, has been limited due to its large computational cost related to the use of an adjoint technique. In order to greatly reduce the computational cost, we hereby propose a fast algorithm for solving the CNOP based on the empirical orthogonal function （EOF）. The algorithm is tested in target observation experiments of Typhoon Matsa using the Global/Regional Assimilation and PrEdiction System （GRAPES）, an operational regional forecast model of China. The effectivity and feasibility of the algorithm to determine the sensitivity （target） area is evaluated through two observing system simulation experiments （OSSEs）. The results, as expected, show that the energy of the CNOP solved by the new algorithm develops quickly and nonlinearly. The sensitivity area is effectively identified with the CNOP from the new algorithm, using 24 h as the prediction time window. The 24-h accumulated rainfall prediction errors （ARPEs） in the verification region are reduced significantly compared with the ＂true state,＂ when the initial conditions （ICs） in the sensitivity area are replaced with the ＂observations.＂ The decrease of the ARPEs can be achieved for even longer prediction times （e.g., 72 h）. Further analyses reveal that the decrease of the 24-h ARPEs in the verification region is attributable to improved simulations of the typhoon＇s initial warm-core, upper level relative vorticity, water vapor conditions, etc., as a result of the updated ICs in the sensitivity area.

The Influence of Arctic Sea Ice Concentration Perturbations on Subseasonal Predictions of North Atlantic Oscillation Events

The influence of Arctic sea ice concentration (SIC) on the subseasonal prediction of the North Atlantic Oscillation (NAO) event is investigated by utilizing the Community Atmospheric Model version 4. The optimal Arctic SIC perturbations which exert the greatest influence on the onset of an NAO event from a lead of three pentads (15 days) are obtained with a conditional nonlinear optimal perturbation approach. Numerical results show that there are two types of optimal Arctic SIC perturbations for each NAO event, with one weakening event (marked as type-1) and another strengthening event (marked as type-2). For positive NAO events, type-1 optimal SIC perturbations mainly show positive SIC anomalies in the Greenland, Barents, and Okhotsk Seas, while type-2 perturbations mainly feature negative SIC anomalies in these regions. For negative NAO events, the optimal SIC perturbations have almost opposite patterns to those in positive events, although there are some differences among these SIC perturbations due to different atmospheric initial conditions. Further diagnosis reveals that the optimal Arctic SIC perturbations first modify the surface turbulent heat flux and the temperature in the lower troposphere via diabatic processes. Afterward, the temperature in the low troposphere is mainly affected by dynamic advection. Finally, potential vorticity advection plays a crucial role in the 500-hPa geopotential height prediction in the northern North Atlantic sector during pentad 4, which influences NAO event prediction. These results highlight the importance of Arctic SIC on NAO event prediction and the spatial characteristics of the SIC perturbations may provide scientific support for target observations of SIC in improving NAO subseasonal predictions.

日本南部黑潮路径发生弯曲的最优前期征兆及其发展机制

基于正压出入流模式,利用条件非线性最优扰动(CNOP)方法研究初始异常的位置与模态对日本南部黑潮路径变异的影响。以模式模拟出的黑潮平直路径的平衡态作为参考态,计算CNOP,考察该扰动随时间的发展,并与随机扰动的发展进行对比。结果表明,CNOP能够导致黑潮弯曲路径发生,随机扰动则不能。因此,CNOP可以作为导致日本南部黑潮路径发生弯曲的一种最优前期征兆。通过分析CNOP和随机扰动的发展过程,可以得出:(1)CNOP使黑潮发展成弯曲路径的过程是一个气旋涡向下游传播并增长的过程。(2)气旋涡的向东传播都是非线性项的作用,也就是涡度平流造成的。(3)CNOP和随机扰动发展过程中所产生的气旋涡均会传播到下游区域,但是CNOP产生的气旋涡能够增强,最终导致弯曲路径发生,而随机扰动产生的气旋涡则会减弱,并不能导致弯曲路径发生。分析发现,在CNOP实验中,非线性作用使气旋涡增大;但在随机扰动实验中,非线性作用使气旋涡减弱,所以非线性作用对日本南部黑潮路径发生弯曲有重要影响。(4)底摩擦效应对日本南部黑潮路径变异影响较小。本文揭示的黑潮路径发生弯曲的最优前期征兆及其非线性发展机制,对提高黑潮路径变异的预报技巧具有重要意义。

初始误差对双环流变异可预报性的影响

使用球坐标下1.5层约化重力浅水模式模拟海洋风生双环流,结果显示双环流射流存在拉伸模态和收缩模态间的年际变化。以双环流从拉伸模态向收缩模态的转变过程为背景场,利用条件非线性最优扰动(CNOP)方法,考察初始误差对双环流变异可预报性的影响,得到两类初始误差:全局CNOP型和局部CNOP(LCNOP)型,两类初始误差对双环流变异的影响几乎相反。通过考察误差发展,发现在射流从拉伸模态向收缩模态转变过程中,CNOP型初始误差使射流弯曲程度变大,并在预报时刻导致涡脱落;而LCNOP型初始误差则使射流弯曲程度变小。相比LCNOP,CNOP型初始误差引起更大预报误差,导致双环流变异的预报技巧下降更多。两类误差得到较大发展的区域可能存在正压不稳定,使误差能够不断从背景场吸收能量进而得到快速发展。给出了两类使双环流变异预报技巧下降最大的初始误差,在实际的数值预报中减少这两种类型的误差,将有助于提高双环流变异的预报技巧。

条件非线性最优扰动方法在适应性观测研究中的初步应用

针对适应性观测中敏感性区域的确定问题,考虑初始误差对预报结果的影响,比较了条件非线性最优扰动(CNOP)与第一线性奇异向量(FSV)在两个降水个例中的空间结构的差异,考察了它们总能量范数随时间发展演变的异同。结合敏感性试验的分析,揭示了预报结果对CNOP类型的初始误差的敏感性要大于对FSV类型的初始误差的敏感性,因而减少初值中CNOP类型误差的振幅比减少FSV类型的收益要大。这一结果表明可以把CNOP方法应用于适应性观测来识别大气的敏感区。关于将CNOP方法有效地应用于适应性观测所面临的挑战及需要采取的对策等也进行了讨论。