2005年世界气象组织热带气象工作组会议综述

世界气象组织(WMO)大气科学委员会(CAS)热带气象研究工作组(WGTMR)会议,于2005年12月12-16日在深圳市举行。会议总结回顾过去4年热带气象领域所开展的研究工作,包括:热带气旋预报研究成果和进展,主要在热带气旋活动(频率、变化、强度等);热带气旋预报技术;确定热带气旋预报研究发展的优势领域等;季风预测的研究成果和进展,分别涉及东亚季风(含南海季风),南亚季风和南美季风等;热带干旱研究成果和进展主要在热带致雨天气系统的研究,对热带人工影响天气新方法新途径的探索,东非的降雨量季节变化特征、年代际雨量变化特征和雨季的开始和结束等;各国业务化运行的数值天气预报系统;以及热带气象和气候变化是在全球气候变化背景下开展研究。大会的特邀报告就利用科学技术进步不断提高气旋预警工作水平、全球变暖与热带气旋活动的关系、热带气旋强度变化共识预报的研究工作、我国新一代数值预报模式、以及目前我国业务使用的台风数值模式等专题进行学术报告。     

大气非绝热加热作用的研究进展与展望

大气非绝热加热与天气系统的发生发展有密切联系,与降水等天气过程密不可分,非绝热加热在大气运动中有着至关重要的作用。对非绝热加热的研究和理解,有助于改进数值预报模式,增强数值天气预报模式的预报能力。本文系统梳理了大气非绝热加热的基本内容,近几十年非绝热加热及其作用的研究成果,主要包括非绝热加热的概念及其表征、非绝热加热的时空分布特征、非绝热加热与季风、天气系统(如西太平洋副热带高压、热带气旋、温带气旋和急流)和降水之间的关系,以及非绝热加热在数值模式中的表征,进而指出有待于进一步研究的方面。     

热带西南季风对0214号热带气旋“黄蜂”的影响

采用中国数值预报创新基地最新开发的三维变分同化系统(Grapes 3DVAR)的分析场为模式初值场,利用广州热带海洋气象研究所开发的南海热带气旋业务预报模式,在模拟试验(控制试验)的基础上,对强、弱热带西南季风作对比试验(异常试验)研究.试验结果表明除环境引导气流外,"黄蜂"偏心结构变化引起的偏心运动变化对热带气旋移向、移速的变化起了一定的作用;西南季风对热带气旋"黄蜂"的动力结构、热力结构和移动路径都有一定的影响;热带西南季风通过改变热带气旋的不对称结构和削弱副热带高压西侧这两个途径,造成热带气旋路径右偏.     

2001年《气象》目录索引

综合评述近 5年大气科学外场试验及其重要成果 (1— 3)…树木年轮碳稳定同位素在气象中的应用 (1— 9)…青藏高原大气臭氧研究 (4— 3)……………………业务集合预报系统的现状及展望 (6— 3)…………高空急流与中纬度系统影响下台风暴雨的研究现状(8— 3)……………………………………………气象卫星探测资料在数值天气预报中的应用(9— 3)…………………………………………………能量与水分循环———气候研究的热点 (9— 9)……黑碳气溶胶及其在气候变化研究中的意义(11— 3)…………………………………………………研究论文热带气旋灾害…     

中国近几年热带气旋研究进展

总结了中国过去几年尤其是“八五”结束后热带气旋领域取得的研究成果。主要包括以下几个方面的内容：热带气旋的结构、数值预报研究、业务数值预报评估、热带气旋暴雨、热带气旋的气候特征以及热带气旋的灾害等等。     

中-澳气象科技合作联合工作组第十二次会议总结

2007年10月17日一21日，以中国气象局副局长宇如聪为团长的中国气象代表团一行七人（余勇、周恒、矫梅燕、刘扬、应宁、肖红宪）赴澳大利亚出席中澳气象科技合作联合工作组第十二次会议。会议期间，双方在友好、坦诚务实的气氛下围绕气象卫星和卫星气象、数值天气预报、全球大气观测、热带气象，包括热带气旋和季风、南极气象、教育与培训、气候和气候变化、新技术、     

数值模式的热带气旋强度预报订正及其集成应用

提供热带气旋强度预报产品的业务数值天气预报模式有很多,并已表现出一定的预报技巧,为提高对模式热带气旋强度预报产品的定量应用能力,分析2010—2012年7个业务数值模式的西北太平洋热带气旋强度预报,发现预报误差不仅受到模式热带气旋初始强度误差的显著影响,还与热带气旋及其所处环境的初始状况有密切关系,包括热带气旋初始强度、尺度、移速、环境气压、环境风切变、热带气旋发展潜势等。根据这些因子与各模式热带气旋强度预报误差之间的相关性,采用逐步回归方法建立热带气旋强度预报误差的统计预估模型,并通过逐个热带气旋滚动式建模来进行独立样本检验。检验结果表明,基于误差预估的模式订正预报比模式直接输出的热带气旋强度预报有显著改进,在此基础上建立的热带气旋强度多模式集成预报方案相对气候持续性预报方法在12 h有28%的正技巧,在24—72 h则稳定在15%—20%,具有业务参考价值。     

认识和预报亚洲季风气候：前沿突破点和展望

改进我们对季风变化和可预报性的认识以便促进季风气候预报的发展是当前世界气候研究计划下属的国际季风研究(IMS)和亚洲季风年(AMY2007-2011)研究的一个主要目的.本文从以下方面回顾了亚洲季风气候研究的最新进展:(1)季风变化的机制和数值模拟,(2)可预报性的物理基础和极限,(3)气候预报的现状.文章的讨论侧重于周尺度到年际尺度,特别提出了季风气候研究中存在的若干科学问题和研究前沿上的可能突破点,讨论了季风预报研究的未来发展方向,旨在促进未来的季风研究,加深我们对季风气候动力学的理解,以及提高对亚洲季风气候变化的预报能力.     

东海热带气旋路径的统计释用综合预报模式

以往热带气旋路径的多种客观定量统计预报模式由于受当时预报信息条件限制,在构造预报模式时,引入的预报因子均为预报初始时刻或之前的热带气旋参数、环境场参数及导出因子.由于热带气旋是在气旋本身内力、地球自转及外部环境场作用力综合作用下进行移动,而这些因素在气旋移动过程中又发生相当大的变化.这些变化往往是复杂的非线性变化过程.应用初始时刻预报因子的统计预报方法由于不能处理大气变化的主要非线性性质,严重影响了其预报精度与技巧水平的提高.尤其是预报时效超过48小时后,缺乏大气动力学与热力学基础的统计预报模式的预报误差的积累往往使预报结果失去意义.此外在应用这些预报模式时,许多预报因子的取得依赖于人工读数,费时费力,难以纳入自动化预报系统.     

2010—2019年ECWMF和NCEP集合模式对热带气旋路径预报的性能评估

中国气象局是全球大集合预报系统数据交换中心之一,自2010年以来,欧洲中期天气预报中心(ECMWF)和美国国家环境预报中心(NCEP)等国外全球集合预报(GEFS)先后进入国家气象中心(NMC)实时业务,多模式的应用促进了我国热带气旋预报业务的进步。利用2010—2019年ECMWF和NCEP两家主要的国外集合模式资料,从分强度、分移速、分月份、分海域、是否登陆、是否转向等方面开展了两家模式在热带气旋路径预报中的性能评估和系统性偏差分析。结果表明,两家模式集合平均的预报误差总体都呈下降趋势,并都存在一定的系统性偏差,但偏差方向几乎相反,ECMWF集合预报易偏向实况西南向,NCEP集合预报易偏向东北向,且后者数值更大。目前ECMWF集合预报性能要优于NCEP集合预报,特别是对于弱热带气旋和登陆热带气旋等情形优势更大。预报中当两家模式分歧较大的时候,性能评估和偏差分析结果也可为主观订正提供参考依据。     

多模式集成的概率天气预报和气候预测研究进展

基于大气的混沌特性,单一的确定性预报逐步向多值的不确定性概率预报转化已成为一种趋势。本文系统地评述了概率天气预报产生的背景,介绍了概率预报的相关概念及国内外的研究状况,着重讨论了多模式集成的概率预报的两种集成方法,即贝叶斯模式平均(Bayesian model averaging,BMA)和多元高斯集合核拟合法(Gaussian ensemble kernel dressing,GEKD),并给出了两个例子的概率预报试验结果。利用BMA方法制作的概率预报的方差较小,减小了预报的不确定性,因此预报结果更接近大气的真实值。作为另一种多模式集成方法,多元高斯集合核拟合法回报的地面气温距平均值及趋势的概率预测结果与实测结果基本一致。利用此方法建立了地面气温年代际变化的概率多模式集合预测模型,并从中提取年代际气候变化特征,对东亚季风区年代际预测具有重要应用价值。     

地面气象要素多模式集成预报研究进展

目前,集合预报已成为天气预报业务的主要支撑。然而,由于数值模式本身的限制与不完善以及集合系统存在初值扰动、集合大小等方面的局限,常存在预报偏差。不同预报模式通常具有不同的物理过程参数化方案、初始条件等,导致其预报能力各有不同。为此,如何纠正预报偏差以及如何充分有效地利用不同模式的预报信息以获得更加准确的天气预报广受关注。近年来,利用统计理论与预报诊断,基于多个集合预报系统的多模式集成预报技术得到快速发展,已成为有效消除预报偏差从而提高天气预报技巧的一种统计后处理方法。针对气温、降水和风3个最基本的地面气象要素,首先依据预报形式将应用范围较广的简单集合平均、消除偏差集合平均、超级集合、贝叶斯模式平均、集合模式输出统计等加权或等权平均多模式集成技术,分成确定性预报和概率预报两大类,并做系统介绍。最后,讨论使用和发展多模式集成技术需要关注的问题,包括考虑参与集成的模式个数、发展降水及风速分级预报模型和发展基于机器学习的多模式集成新技术。     

气候预测中的集合方法初探

介绍了气候预测中的集合方法。该文作者曾在1996年论证了在西太平洋暖池区海温异常与东亚夏季风的共同作用下存在一个可预测的气候异常区,部分地改变了气候不可预测的论断。如何从与大量不可预测结果混杂在一起的结果中提炼出可预测部分是集合方法的重要目的之一。文中也讨论了由于大气运动固有的动力学特性,其集合预测与经典的数学考虑有所区别,天气与气候预测有不同的特点,其集合方法、目的也应有所不同,由此对集合方法提出了一些新的建议。文中同时介绍了首次在气候预测中发现的多平衡态现象,建议了如何判定多平衡的出现,以及如何利用多平衡态来改善对不同区域的预测。     

Thrusts and Prospects on Understanding and Predicting Asian Monsoon Climate

Development of monsoon climate prediction through integrated research efforts to improve our understanding of monsoon variability and predictability is a primary goal of the Asian Monsoon Years （200-2011） and International Monsoon Study under the leadership of the World Climate Research Programme. The present paper reviews recent progress in Asian monsoon research focusing on （1） understanding and modeling of the monsoon variability, （2） determining the sources and limits of predictability, and （3） assessing the current status of climate prediction, with emphasis on the weekly to interannual time scales. Particular attention is paid to identify scientific issues and thrust areas, as well as potential directions to move forward in an attempt to stimulate future research to advance our understanding of monsoon climate dynamics and improve our capability to forecast Asian monsoon climate variation.     

The seasonal predictability of the Asian summer monsoon in a two-tiered forecast system

An extensive set of boreal summer seasonal hindcasts from a two tier system is compared with corresponding seasonal hindcasts from two other coupled ocean–atmosphere models for their seasonal prediction skill (for precipitation and surface temperature) of the Asian summer monsoon. The unique aspect of the two-tier system is that it is at relatively high resolution and the SST forcing is uniquely bias corrected from the multi-model averaged forecasted SST from the two coupled ocean–atmosphere models. Our analysis reveals: (a) The two-tier forecast system has seasonal prediction skill for precipitation that is comparable (over the Southeast Asian monsoon) or even higher (over the South Asian monsoon) than the coupled ocean–atmosphere. For seasonal anomalies of the surface temperature the results are more comparable across models, with all of them showing higher skill than that for precipitation. (b) Despite the improvement from the uncoupled AGCM all models in this study display a deterministic skill for seasonal precipitation anomalies over the Asian summer monsoon region to be weak. But there is useful probabilistic skill for tercile anomalies of precipitation and surface temperature that could be harvested from both the coupled and the uncoupled climate models. (c) Seasonal predictability of the South Asian summer monsoon (rainfall and temperature) does seem to stem from the remote ENSO forcing especially over the Indian monsoon region and the relatively weaker seasonal predictability in the Southeast Asian summer monsoon could be related to the comparatively weaker teleconnection with ENSO. The uncoupled AGCM with the bias corrected SST is able to leverage this teleconnection for improved seasonal prediction skill of the South Asian monsoon relative to the coupled models which display large systematic errors of the tropical SST’s.     

Seasonal climate prediction for South America with FSU Multi-model Synthetic Superensemble algorithm

Summary Objective combination schemes of predictions from different models have been applied to seasonal climate forecasts. These schemes are successful in producing a deterministic forecast superior to individual member models and better than the multi-model ensemble mean forecast. Recently, a variant of the conventional superensemble formulation was created to improve skills for seasonal climate forecasts, the Florida State University (FSU) Synthetic Superensemble. The idea of the synthetic algorithm is to generate a new data set from the predicted multimodel datasets for multiple linear regression. The synthetic data is created from the original dataset by finding a consistent spatial pattern between the observed analysis and the forecast data set. This procedure is a multiple linear regression problem in EOF space. The main contribution this paper is to discuss the feasibility of seasonal prediction based on the synthetic superensemble approach and to demonstrate that the use of this method in coupled models dataset can reduce the errors of seasonal climate forecasts over South America. In this study, a suite of FSU coupled atmospheric oceanic models was used. In evaluation the results from the FSU synthetic superensemble demonstrate greater skill for most of the variables tested here. The forecast produced by the proposed method out performs other conventional forecasts. These results suggest that the methodology and database employed are able to improve seasonal climate prediction over South America when compared to the use of single climate models or from the conventional ensemble averaging. The results show that anomalous conditions simulated over South America are reasonably realistic. The negative (positive) precipitation anomalies for the summer monsoon season of 1997/98 (2001/02) were predicted by Synthetic Superensemble formulation quite well. In summary, the forecast produced by the Synthetic Superensemble approach outperforms the other conventional forecasts.     

Improving multimodel weather forecast of monsoon rain over China using FSU superensemble

In this paper we present the current capabilities for numerical weather prediction of precipitation over China using a suite of ten multimodels and our superensemble based forecasts. Our suite of models includes the operational suite selected by NCARs TIGGE archives for the THORPEX Program. These are: ECMWF, UKMO, JMA, NCEP, CMA, CMC, BOM, MF, KMA and the CPTEC models. The superensemble strategy includes a training and a forecasts phase, for these the periods chosen for this study include the months February through September for the years 2007 and 2008. This paper addresses precipitation forecasts for the medium range i.e. Days 1 to 3 and extending out to Day 10 of forecasts using this suite of global models. For training and forecasts validations we have made use of an advanced TRMM satellite based rainfall product. We make use of standard metrics for forecast validations that include the RMS errors, spatial correlations and the equitable threat scores. The results of skill forecasts of precipitation clearly demonstrate that it is possible to obtain higher skills for precipitation forecasts for Days 1 through 3 of forecasts from the use of the multimodel superensemble as compared to the best model of this suite. Between Days 4 to 10 it is possible to have very high skills from the multimodel superensemble for the RMS error of precipitation. Those skills are shown for a global belt and especially over China. Phenomenologically this product was also found very useful for precipitation forecasts for the Onset of the South China Sea monsoon, the life cycle of the mei-yu rains and post typhoon landfall heavy rains and flood events. The higher skills of the multimodel superensemble make it a very useful product for such real time events.     

Validation of the experimental hindcasts produced by the GloSea4 seasonal prediction system

Using 14 year (1996–2009) ensemble hindcast runs produced with the Global Seasonal Forecasting System version 4 (GloSea4), this study evaluates the spatial and temporal structure of the hindcast climatology and the prediction skill of major climate variability. A special focus is on the fidelity of the system to reproduce and to forecast phenomena that are closely related to the East Asian climate. Overall the GloSea4 system exhibits realistic representations of the basic climate even though a few model deficiencies are identified in the sea surface temperature and precipitation. In particular, the capability of GloSea4 to capture the seasonal migration of rain belt associated with Changma implies a good potential for the Asian summer monsoon prediction. It is found that GloSea4 is as skillful as other state-of-the-art seasonal prediction systems in forecasting climate variability including the El-Nino/southern oscillation (ENSO), the East Asian summer monsoon, the Arctic Oscillation (AO), and the Madden-Julian Oscillation (MJO). The results presented in this study will provide benchmark evaluation for next seasonal prediction systems to be developed at the Korea Meteorological Administration.     

A multi-model study of atmosphere predictability in coupled ocean–atmosphere systems

Of great importance for guiding numerical weather and climate predictions, understanding predictability of the atmosphere in the ocean − atmosphere coupled system is the first and critical step to understand predictability of the Earth system. However, previous predictability studies based on prefect model assumption usually depend on a certain model. Here we apply the predictability study with the Nonlinear Local Lyapunov Exponent and Attractor Radius to the products of multiple re-analyses and forecast models in several operational centers to realize general predictability of the atmosphere in the Earth system. We first investigated the predictability characteristics of the atmosphere in NCEP, ECMWF and UKMO coupled systems and some of their uncoupled counterparts and other uncoupled systems. Although the ECMWF Integrated Forecast System shows higher skills in geopotential height over the tropics, there is no certain model providing the most precise forecast for all variables on all levels and the multi-model ensemble not always outperforms a single model. Improved low-frequency signals from the air − sea and stratosphere − troposphere interactions that extend predictability of the atmosphere in coupled system suggests the significance of air − sea coupling and stratosphere simulation in practical forecast development, although uncertainties exist in the model representation for physical processes in air − sea interactions and upper troposphere. These inspire further exploration on predictability of ocean and stratosphere as well as sea − ice and land processes to advance our understanding of interactions of Earth system components, thus enhancing weather − climate prediction skills.

     

北半球中纬度地区地面气温的超级集合预报

基于TIGGE资料中的ECMWF、JMA、NCEP和UKMO四个中心2007年6月1日-8月31日北半球中纬度地区地面气温24～168 h集合预报资料,分别利用固定训练期超级集合(SUP, Superensemble)和滑动训练期超级集合(R-SUP, Running Training Period Superensemble )对2007年8月8-31日预报期24 d进行超级集合预报试验.采用均方根误差对预报结果进行检验评估,比较了两种超级集合方法与最好的单个中心模式预报、多模式集合平均的预报效果.结果表明,SUP预报有效降低了预报误差,24～144 h的预报效果优于多模式集合平均(EMN, Ensemble Mean)和最好的单个中心预报,168 h的预报效果略差于EMN.R-SUP预报进一步改善了预报效果.对于24～168 h的预报,R-SUP预报效果都要优于EMN.尤其对于168 h的预报,R-SUP改进了预报效果,优于EMN.