Influence of Sea Surface Temperature on the Predictability of Idealized Tropical Cyclone Intensity

The role of sea surface temperature (SST) forcing in the development and predictability of tropical cyclone (TC) intensity is examined using a large set of idealized numerical experiments in the Weather Research and Forecasting (WRF) model. The results indicate that the onset time of rapid intensification of TC gradually decreases, and the peak intensity of TC gradually increases, with the increased magnitude of SST. The predictability limits of the maximum 10 m wind speed (MWS) and minimum sea level pressure (MSLP) are ~72 and ~84 hours, respectively. Comparisons of the analyses of variance for different simulation time confirm that the MWS and MSLP have strong signal-to-noise ratios (SNR) from 0-72 hours and a marked decrease beyond 72 hours. For the horizontal and vertical structures of wind speed, noticeable decreases in the magnitude of SNR can be seen as the simulation time increases, similar to that of the SLP or perturbation pressure. These results indicate that the SST as an external forcing signal plays an important role in TC intensity for up to 72 hours, and it is significantly weakened if the simulation time exceeds the predictability limits of TC intensity.     

A New Strategy for Solving a Class of Constrained Nonlinear Optimization Problems Related to Weather and Climate Predictability

There are three common types of predictability problems in weather and climate, which each involve different constrained nonlinear optimization problems: the lower bound of maximum predictable time, the upper bound of maximum prediction error, and the lower bound of maximum allowable initial error and parameter error. Highly efficient algorithms have been developed to solve the second optimization problem. And this optimization problem can be used in realistic models for weather and climate to study the upper bound of the maximum prediction error. Although a filtering strategy has been adopted to solve the other two problems, direct solutions are very time-consuming even for a very simple model, which therefore limits the applicability of these two predictability problems in realistic models. In this paper, a new strategy is designed to solve these problems, involving the use of the existing highly efficient algorithms for the second predictability problem in particular. Furthermore, a series of comparisons between the older filtering strategy and the new method are performed. It is demonstrated that the new strategy not only outputs the same results as the old one, but is also more computationally efficient. This would suggest that it is possible to study the predictability problems associated with these two nonlinear optimization problems in realistic forecast models of weather or climate.     

SMIP2试验对亚洲夏季风的模拟能力及其可预报性的分析

利用参加第二次季节预测模式比较计划（Phase 2 of the Seasonal Prediction Model Intercomparison Project,简称SMIP2）的五个大气环流模式的输出结果,比较了这些模式对亚洲夏季降水的模拟能力,并讨论了大气环流模式在季节尺度上对亚洲夏季风的可预报性。结果表明,各模式能够较好地模拟亚洲夏季降水的大尺度分布特征。除了模拟的东亚夏季降水异常经验正交函数分解第一模态（EOF1）的时间系数与观测之间的相关系数较低之外,多数模式可以大致再现东亚、南亚和西太平洋夏季降水异常EOF1及其对应的时间系数。分析表明,热带地区有很好的可预报性,北半球副热带地区的可预报性尽管也较好,但比热带地区要低,南半球热带以外地区的可预报性较差,陆地上的可预报性比海洋上低。在热带和北半球副热带地区,由海温强迫所产生的较大外部方差对此地高可预报性有很大的贡献。与正常年份相比,模式在强El Ni?o年和强La Ni?a年表现出较高的可预报性,在南亚和印度洋地区大多数模式在El Ni?o年比La Ni?a年产生了更好的可预报性,在中西太平洋区域El Ni?o年可预报性的高值中心较La Ni?a年位置偏西。     

Contributions of Fuqing ZHANG to Predictability,Data Assimilation,and Dynamics of High Impact Weather: A Tribute

This article reviews Fuqing ZHANG’s contributions to mesoscale atmospheric science,from research to mentoring to academic service,over his 20-year career.His fundamental scientific contributions on predictability,data assimilation,and dynamics of high impact weather,especially gravity waves and tropical cyclones,are highlighted.His extremely generous efforts to efficiently transmit to the community new scientific knowledge and ideas through mentoring,interacting,workshop organizing,and reviewing are summarized.Special appreciation is given to his tremendous contributions to the development of mesoscale meteorology in China and the education of Chinese graduate students and young scientists.     

Nonlinear Atmospheric and Climate Dynamics in China （2003-2006）： A Review

Recent advances in the study of nonlinear atmospheric and climate dynamics in China （2003 2006） are briefly reviewed. Major achievements in the following eight areas are covered： nonlinear error dynamics and predictability; nonlinear analysis of observational data; eddy-forced envelope Rossby soliton theory; sensitivity and stability of the ocean＇s thermohaline circulation; nonlinear wave dynamics; nonlinear analysis on fluctuations in the atmospheric boundary layer; the basic structures of atmospheric motions; some applications of variational methods.     

非线性大气动力学的进展

总结了自新中国成立以来中国科学院大气物理研究所在非线性大气动力学领域的进展,其中主要介绍了在非线性适应过程、非线性稳定与不稳定、全局分析理论、可预报性、低阶谱方法与多平衡态动力学、非线性波动、波流相互作用、阻塞高压非线性动力学、中小尺度非线性动力学等几个方面的研究成果及最新进展.     

时空投影模型(STPM)的次季节至季节(S2S)预测应用进展

随着数值天气预报技术和季节动力预报系统的发展,短期天气预报及长期气候预测的能力持续提高,然而介于两者之间的次季节至季节(S2S,两周至三个月)预测技巧偏低,成为当今气象学界和业务服务的难题。南京信息工程大学国家特聘专家李天明教授团队于2012年研发了基于时空投影技术的统计预报模型(STPM),成功地对中国大陆降水和气温距平,以及区域极端降水、夏季高温、冬季低温和西太平洋台风群发事件等高影响天气进行提前10~30 d的预报,并在国家气候中心及多个省份开展了业务应用。STPM也成功应用于台湾春雨预报、南海季风爆发和ENSO预测等季节至年际变化的预测。本文对S2S预测的理论基础、STPM的发展和应用进行了完整的介绍,并讨论了S2S预测业务中所面临的挑战和未来展望。     

中国季降水量的气候噪声和潜在可预报性估计

利用中国130个测站1961—2004年的日降水量资料,使用低频白噪声延伸法和方差分析法估计了中国季降水量的气候噪声方差和潜在可预报性。结果表明：中国季降水量的气候噪声方差由南向北、由沿海向内陆逐渐减小,且有明显的季节变化,夏季最高,其次是春秋季,冬季最小,而且内陆的季节变化比东南沿海的季节变化显著。季降水量的潜在可预报性有较大的季节和区域差异,但总体来说,全国大部分地区的季降水量是潜在可预报的。以绝对误差小于均方差0.68倍作为预测正确标准,全国大部分地区季降水量的预报正确率上限为50%-60%。     

大气初始场对短期气候数值预测的影响

利用中国科学院大气物理研究所发展的九层大气环流模式(简称IAP 9L2°×2.5°-AGCM)分析了大气初始场对短期气候数值预测的影响,分别从实际观测海温、海冰的外强迫和气候态海温、海冰的外强迫出发,进行两组集合回报试验,每组包含3个试验,分别将实时NCEP-Ⅱ资料和对NCEP-Ⅱ资料经5 d平滑、11 d平滑后的资料作为大气初始场,进行17 a(1988—2004年)集合回报试验,采用相关分析方法对试验结果进行对比分析。结果表明,比较相关系数定量检验出大气初始场对热带地区可预报性影响较小,而对中、高纬度地区影响很大。通过对6个试验中对应气象要素在对流层各层距平时间相关系数以及17 a空间异常相关系数均值比较分析发现对东亚(中国)地区夏季气候而言,NCEP-Ⅱ资料经5 d平滑后生成的大气初始场对应回报试验结果相对最好。     

The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model

Initial errors and model errors are the source of prediction errors. In this study, the authors compute the conditional nonlinear optimal perturbation （CNOP）-type initial errors and nonlinear forcing singular vector （NFSV）- type tendency errors of the Zebiak-Cane model with respect to El Nifio events and analyze their combined effect on the prediction errors for E1 Nino events. The CNOP- type initial error （NFSV-type tendency error） represents the initial errors （model errors） that have the largest effect on prediction uncertainties for E1 Nifio events under the perfect model （perfect initial conditions） scenario. How- ever, when the CNOP-type initial errors and the NFSV- type tendency errors are simultaneously considered in the model, the prediction errors caused by them are not am- plified as the authors expected. Specifically, the predic- tion errors caused by the combined mode of CNOP-type initial errors and NFSV-type tendency errors are a little larger than those caused by the NFSV-type tendency er- rors. This fact emphasizes a need to investigate the opti- mal combined mode of initial errors and tendency errors that cause the largest prediction error for E1 Nifio events.