中国地区NCEP再分析资料与台站观测的地表温度差异及其影响

作为时间序列非线性的一个重要指标,从NCEP再分析得到日气温异常的时间不可逆性(TI)与观测站的相比几乎一致地被高估了.因为非线性与可预报性/极端事件之间有着密切的关系,除了高估的TI外,这些大气测度也可能被高估.本文结果表明:NCEP再分析的日最低和最高气温异常序列的内在可预报性,预报技巧和极端事件发生次数也几乎一致...     

Progress in the Study of Nonlinear Atmospheric Dynamics and Predictability of Weather and Climate in China (2007--2011)

Recent progress in the study of nonlinear atmospheric dynamics and related predictability of weather and climate in China (2007-2011) are briefly introduced in this article. Major achievements in the study of nonlinear atmospheric dynamics have been classified into two types:(1) progress based on the analysis of solutions of simplified control equations, such as the dynamics of NAO, the optimal precursors for blocking onset, and the behavior of nonlinear waves, and (2) progress based on data analyses, such as the nonlinear analyses of fluctuations and recording-breaking temperature events, the long-range correlation of extreme events, and new methods of detecting abrupt dynamical change. Major achievements in the study of predictability include the following:(1) the application of nonlinear local Lyapunov exponents (NLLE) to weather and climate predictability; (2) the application of condition nonlinear optimal perturbation (CNOP) to the studies of El Nin o-Southern Oscillation (ENSO) predictions, ensemble forecasting, targeted observation, and sensitivity analysis of the ecosystem; and (3) new strategies proposed for predictability studies. The results of these studies have provided greater understanding of the dynamics and nonlinear mechanisms of atmospheric motion, and they represent new ideas for developing numerical models and improving the forecast skill of weather and climate events.     

Influence of the Arctic on the Predictability of Eurasian Winter Extreme Weather Events

The linkage between the Arctic and midlatitudes has received much attention recently due to the rapidly changing climate. Many investigations have been conducted to reveal the relationship between the Arctic and Eurasian extreme events from the perspective of climatological statistics. As a prediction source for extreme events in Eurasia, Arctic conditions are crucial for extreme event predictions. Therefore, it is urgent to explore the Arctic influence on the predictability of Eurasian extreme ...     

Discrepancies in surface temperature between NCEP reanalysis data and station observations over China and their implications: 中国地区NCEP再分析资料与台站观测的地表温度差异及其影响

Previous studies show that temporal irreversibility (TI), as an important indicator of the nonlinearity of time series, is almost uniformly overestimated in the daily air temperature anomaly series over China in NCEP reanalysis data, as compared with station observations. Apart from this highly overestimated TI in the NCEP reanalysis, some other important atmospheric metrics, such as predictability and extreme events, might also be overestimated since there are close relations between nonlinearity and predictability/extreme events. In this study, these issues are fully addressed, i.e., intrinsic predictability, prediction skill, and the number of extreme events. The results show that intrinsic predictability, prediction skill, and the occurrence number of extreme events are also almost uniformly overestimated in the NCEP reanalysis daily minimum and maximum air temperature anomaly series over China. Furthermore, these overestimations of intrinsic predictability, prediction skill, and the number of extreme events are only weakly correlated with the overestimated TI, which indicates that the quality of the NCEP reanalysis should be carefully considered when conclusions on both predictability and extreme events are derived.摘要作为时间序列非线性的一个重要指标, 从NCEP再分析得到日气温异常的时间不可逆性 (TI) 与观测站的相比几乎一致地被高估了.因为非线性与可预报性/极端事件之间有着密切的关系, 除了高估的TI外, 这些大气测度也可能被高估.本文结果表明:NCEP再分析的日最低和最高气温异常序列的内在可预报性,预报技巧和极端事件发生次数也几乎一致被高估.而且, 这些高估的测度与高估的TI只存在微弱的相关性, 这表明利用NCEP再分析研究可预测性和极端事件时, 需要仔细考虑其质量对结论的可能影响.     

集合敏感性在预报误差及可预报性研究中的应用进展综述

研究大气的可预报性和预报误差产生的原因,对于改进数值预报,提升业务预报技巧具有重要意义。集合敏感性基于具有流依赖特性的集合预报,通过建立预报与初始场或前期预报大气状态之间的统计关系,为揭示与预报对象可预报性相关的动力学特征及理解预报误差来源和传播机制提供了一种新方法。同时,介绍了集合敏感性的定义和度量,并综述了其针对典型天气系统和高影响天气事件研究的进展,并讨论了该方法的优势和局限性。     

Extended Range(10–30 Days) Heavy Rain Forecasting Study Based on a Nonlinear Cross-Prediction Error Model

Extended range(10–30 d) heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper,a nonlinear cross prediction error(NCPE) algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First,nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process,after which the local change characteristics of the attractors are analyzed. Second,the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5,and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently,without failure,based on the NCPE model; the prediction validity periods for 1–2 d,3–9 d and 10–30 d are 4,22 and 74 cases,respectively,without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10–30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability,and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.     

近二十年来暴雨和强对流可预报性研究进展

大气可预报性研究是开展天气、气候预测的基础科学问题。全球变暖背景下,近年暴雨和强对流等中小尺度灾害性天气频发,如何深入认识其可预报性问题成为了天气领域研究热点,也是制约数值天气预报模式能力提升的重要因素。本文在简要回顾国内外大气可预报性研究历程的基础上,重点对近二十年（1999～2018）国际上关于暴雨和强对流可预报性方面的最新研究进展进行了系统的综述和归纳。主要包括:中小尺度可预报性研究的主要方法和评估手段及其与传统大尺度天气可预报性研究的差异,初始误差增长机制的几种主要观点及其争论（误差升尺度、误差降尺度、升降尺度并存）,数值模式误差和对流环境误差对实际预报性的影响,以及最近的中尺度可预报性科学观测试验进展等。最后,对暴雨、强对流可预报性研究存在的问题、未来发展方向进行了简要的讨论和展望。     

中国极端天气气候研究——“地球系统与全球变化”重点专项项目简介及最新进展

全球变暖背景下,极端天气气候事件频发,并表现出群发性、持续性、复合性等特点,不可预测性增加;持续性强降水、极端低温、复合型极端高温干旱、群发性热浪和台风等极端天气气候事件对我国经济社会和可持续发展影响巨大。然而,上述极端天气气候事件的新特征、关键过程和机理尚不完全清楚,重大极端事件的预报预测水平亟待提升。文章首先简要介绍“地球系统与全球变化”重点专项项目“中国极端天气气候事件的形成机理及其预测和归因”的基本情况。项目拟在分析全球变化背景下对我国造成重大影响的极端天气气候事件新特征的基础上,深入研究多尺度海-陆-气耦合过程影响极端天气气候事件的机理,挖掘极端天气气候事件次季节-季节预测的前兆信号;发展动力与物理统计相结合的极端事件预测新方法,研制针对中国极端事件的新一代高分辨率数值预报与检测归因系统。文章重点总结了自2022年12月项目立项至今取得的最新研究成果和进展。     

AOGS第六届学术年会气象学研究报告综述

简要综述了第六届亚洲大洋洲地球科学学会（AOGS）学术年会有关天气和气候的可预报性、协同亚洲季风试验、季风变化及其模拟与预测、气候变化与热带气候灾害、中层大气研究、中尺度气象学与台风研究以及卫星资料应用等方面的报告内容。在全球气候变化的大背景下,季风的年代际变化特征,可能成因及其对天气气候的影响是目前研究的热点问题之一。对亚洲季风系统形成机制的探讨及其与气候系统关系的研究有助于提高数值模式对季风系统的模拟能力,从而能够更好地预测季风系统及其演变。集合预报,特别是多模式超级集合预报方法引起了人们广泛的兴趣。研究报告指出,多个积云对流参数化方案的集成、集合卡曼滤波、条件非线性最优扰动法以及滑动训练期超级集合预报技术对数值天气预报的准确率都有明显改善。＂可预报性障碍＂在数值模式对大气环流的预报中同样存在。＂可预报性障碍＂本质上反映了系统的季节变化对预报结果的影响。研究还表明,热带气旋频率和强度的变化取决于在其形成的海域是动力因子还是热力因子起主导作用,如热力因子起主导作用则气候变暖会使该区域的热带气旋活动频率和强度增加。一些研究指出,中高纬度阻塞形势持续异常及南海的热源异常可能是造成2008年初中国南方低温、雨雪和冰冻灾害的主要原因。Formosat-2和Formosat-3等卫星观测资料的广泛应用将极大地提高数值天气预报、气候监测和空间天气预报水平。     

10~30 d延伸期可预报性与预报方法研究进展

10~30 d延伸期的可预报性既依赖于初始条件,也与缓变的下垫面有关,寻找延伸期时段内可预报性较高的低频特征,识别延伸期的可预报性来源及影响的物理机制是提高延伸期预报水平的关键。近年延伸期可预报性来源、热带大气季节内振荡监测预测和影响等领域的研究取得较大进展,提出和应用了动力统计相结合以及大气低频信号释用等新的延伸期预报方法。对延伸期可预报性来源及其与初值和外强迫异常的关系分析表明,海气相互作用能提高亚洲和西太平洋区域延伸期时段大气环流和要素的可预报性。热带大气季节内振荡、平流层爆发性增温以及各种次季节尺度的海气、陆气耦合作用和大气响应均为延伸期预报提供了重要的可预报性来源。由于数值模式延伸期时段的预报性能与实际业务需求还存在一定距离,基于动力统计相结合和物理统计的延伸期预报方法被广泛应用于业务预报,表现出一定的预报技巧。     

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。     

气候变化对延伸期预报的影响

10～30 d时效的延伸期预报,作为无缝隙预报预测体系中至关重要的一环,连接着天气预报和短期气候预测。受不断加剧的气候变化的影响,延伸期预报将面临更为重大的挑战。首先概述国内外延伸期预报现状,然后分析了全球气候变化对极端天气气候事件分布特征、关键环流系统可预报性等方面的影响,发现气候变化将导致延伸期预报难度加大、需求更加旺盛,同时也更加突显延伸期预报在防灾减灾方面的作用。进一步展望延伸期预报将面临的新挑战以及未来业务发展的新动向,提出了适应气候变化的应对措施和建议,如大力发展数值预报模式、深入开展延伸期预报机理研究、大力发展动力—统计相结合的预报方法以及尝试多学科交叉协作等。     

Assessing the Impacts of Eurasian Snow Conditions on Climate Predictability with a Global Climate Model

On the basis of two ensemble experiments conducted by a general atmospheric circulation model (Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model, hereinafter referred to as IAP9L_CoLM), the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated. The predictive skill of sea level pressures (SLP) and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model. Furthermore, the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature (SAT) anomalies over China was improved by applying realistic (prescribed) Eurasian snow conditions. The predictive skill of the summer precipitation in China was low; however, when realistic snow conditions were employed, the predictability increased, illustrating the effectiveness of the application of realistic Eurasian snow conditions. Overall, the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China. When Eurasian snow conditions in the global climate model (GCM) can be more realistically represented, the predictability of summer climate over China increases.     

国家气象中心中期集合预报系统概况

随着对大气初值及数值模式存在不确定性的认识以及计算机技术的飞速发展，集合预报已经成为数值预报发展的重要方向之一。本文介绍集合预报的基本概念及初值扰动的特点和国家气象中心集合预报系统的概况。     

Long-range weather forecasts through numerical and empirical methods

There has been much improvement in numerical weather prediction since L.F. Richardson (1922, Weather Prediction by Numerical Process, Cambridge University Press, Cambridge, p. 236) wrote his famous book. NWP has primarily been successful in improving day-by-day forecasts starting from an observed detailed Initial Condition (IC) out to about a week. The purpose of this paper is to discuss first the state of the art in long-range NWP by presenting results of a new large numerical experiment (named DERF90; from Dynamical Extended Range Forecasting in 1990 out to 90 days) conducted at the National Meteorological Center (NMC) during the summer and autumn of 1990 (Section 2). One hundred and twenty eight 90-day global forecasts were made from successive daily initial conditions (IC), thus giving us ample opportunity to assess skill of forecasts at lead times beyond 1 week.We then move on to define the notion of a limit of predictability (LOP), and following a procedure by Lorenz (1982), give a numerical estimate of the LOP using the DERF90 data set. We then produce a list of reasons, as to why this estimate (LOP = 18 days) should not be taken too literally. In particular, we argue that the LOP varies as a function of the flow itself, and it would be (much) larger if we had, as we will ultimately, a coupled ocean-atmosphere model for making long-lead forecasts. Last, but not least, we present results of empirical forecasts that point to modest but significant skill well beyond the traditional LOP (a few weeks).A specific recent example of empirical forecasting is discussed. Through Canonical Correlation Analysis (CCA), experimental forecasts are being made for the United States surface temperatures at lead times of several seasons. While modest, the skill is significant in that it defies the existence or a 3-week LOP, and so demonstrates the potential for model improvements.     

Recent Advances in Predictability Studies in China （1999-2002）

Since the last International Union of Geodesy and Geophysics (IUGG) General Assembly (1999), the predictability studies in China have made further progress during the period of 1999-2002. Firstly, three predictability sub-problems in numerical weather and climate prediction are classified, which are concerned with the maximum predictability time, the maximum prediction error, and the maximum allowable initial error, and then they are reduced into three nonlinear optimization problems. Secondly, the concepts of the nonlinear singular vector (NSV) and conditional nonlinear optimal perturbation (CNOP) are proposed,which have been utilized to study the predictability of numerical weather and climate prediction. The results suggest that the nonlinear characteristics of the motions of atmosphere and oceans can be revealedby NSV and CNOP. Thirdly, attention has also been paid to the relations between the predictability and spatial-temporal scale, and between the model predictability and the machine precision, of which the investigations disclose the importance of the spatial-temporal scale and machine precision in the study of predictability. Also the cell-to-cell mapping is adopted to analyze globally the predictability of climate,which could provide a new subject to the research workers. Furthermore, the predictability of the summer rainfall in China is investigated by using the method of correlation coefficients. The results demonstrate that the predictability of summer rainfall is different in different areas of China. Analysis of variance, which is one of the statistical methods applicable to the study of predictability, is also used to study the potential predictability of monthly mean temperature in China, of which the conclusion is that the monthly mean temperature over China is potentially predictable at a statistical significance level of 0.10. In addition,in the analysis of the predictability of the T106 objective analysis/forecasting field, the variance and the correlation coefficient are calculated to explore the distribution characteristics of the mean-square errors.Finally, the predictability of short-term climate prediction is investigated by using statistical methods or numerical simulation methods. It is demonstrated that the predictability of short-term climate in China depends not only on the region of China being investigated, but also on the time scale and the atmospheric internal dynamical process.     

基于最新全球集合预报系统的可预报性评估

为了提供有价值且可靠的概率(或者不确定性)预报,最新的全球集合预报系统已在美国国家环境预报中心日常业务运行,以满足社会需求。通过对各个关键要素的概率预报统计检验,可为广大用户提供这些概率预报的信心指数。但是预报(或集合预报)能力不仅取决于我们使用的预测要素,而且与时间和空间分辨率,极端事件或者高影响天气,以及预报时效有关。以大尺度天气系统预报为例,通常选择北半球500 hPa位势高度距平相关指数或概率指数表征模式的预报能力。如参照北半球500 hPa位势高度的距平相关指数(60%AC)或概率预报技巧指数(25%CRPSS),美国全球集合预报系统能够提供大约10 d的技巧预报。从全球集合预报系统输出的各预报要素,满足不同时空尺度需求的角度进行讨论,其可预报性(或预报极限)能够为模式研发人员、一线预报员和用户提供参考。尤其是对大气可预报性的深入研究,对于从科学与技术角度全面提升数值预报系统水平非常重要。当能够确定可预报性(或是预报误差)的真实来源时,科学家(包括模式研发人员)就能够有针对性地修改与完善。将传统的可预报性研究与改进的能够更客观地表述预报不确定性的集合预报相结合,所得可预报性将提供另一种有价值的参考。可预报性研究总体表明,全球集合预报系统对行星波、大尺度和天气尺度的系统(或者过程)可能分别具备约15、12、10 d的预报能力。对于热带天气过程的预报,如果进一步改善模式偏差和物理参数化过程,其MJO(Madden-Julian Oscillation)预报技巧可以延长至32.5 d。     

Contribution of realistic soil moisture initial conditions to boreal summer climate predictability

Seasonal climate forecasts mainly rely on the atmospheric sensitivity to its lower boundary conditions and on their own predictability. Besides sea surface temperature (SST), soil moisture (SM) may be an additional source of climate predictability particularly during boreal summer in the mid-latitudes. In this work, we investigate the role of SM initial conditions on near-surface climate predictability during ten boreal summer seasons using three complementary ensembles of AMIP-type simulations performed with the Arpège-Climat atmospheric general circulation model. First we have conducted an assessment of the SM predictability itself through a comparison of simple empirical SM models with Arpège-Climat. The statistical and dynamical models reveal similar SM prediction skill patterns but the Arpège-Climat reaches higher scores suggesting that it is at least suitable to explore the influence of SM initialization on atmospheric predictability. Then we evaluate the relationships between SM predictability and some near surface atmospheric predictability. While SM initialization obviously improves the predictability of land surface evaporation, it has no systematic influence on the precipitation and near surface temperature skills. Nevertheless, the summer hindcast skill is clearly improved during specific years and over certain regions (mainly north America and eastern Europe in the Arpège-Climat model), when and where the SM forcing is sufficiently widespread and strong. In this case, a significant impact is also found on the occurrence of heat waves and heavy rains, whose predictability at the seasonal timescale is a crucial challenge for years to come.     

定量降水预报技术进展

采用ECMWF interim再分析资料、ECMWF集合预报资料、自动站降水资料,运用“集合异常预报法”和“EFI（Extreme Forecast Index）阈值法”对浙江东部一次台风远距离极端降水的成因和可预报性进行探讨,结果表明：（1）台风倒槽长期存在,在北侧冷空气入侵作用下在浙江沿海地区引起锋生,并在倒槽顶端诱生低压环流,是造成浙江东北部大暴雨的主要原因。（2）36 h时效的集合预报显示,大气可降水量、850 hPa南风分量、925 hPa水汽通量和200 hPa辐散都超过气候平均值3~4个标准差,且超过3个标准差的概率也达到了70%~90%,表明浙江沿海有优越的动力和充沛的水汽条件,异常强烈的天气信号,预示发生极端降水为高概率事件。（3）随着预报时效的延长,各物理量异常概率会明显减小,因此在判断极端天气发生可能性时应降低预警的概率阈值。（4）不同时效降水的EFI对95%和99%百分位事件的预报效果均很好,可以比确定性预报提前3~4 d提供极端降水信号。EFI指数所反映的极端降水信息较确定性模式更加可靠且稳定,在决策服务中具有更高的参考价值。（5）“集合异常预报法”可以获得与极端天气相关的形势场和物理量的异常概率,而EFI在长时效的预报更具优势,两者结合、优势互补,可以为预报员提供更全面的极端天气信号。

     

不完整气象资料下基于作物模型的产量预报方法

针对基于作物模型开展产量实时预报后期气象资料的获取问题,提出通过相似类比,从历史气象资料库中获取替代资料的方案,基于CERES-Rice模型系统评估了平均值处理方案和历史相似类比方案的可预报性和误差分布特征。结果表明:水稻产量对成熟前2个月内的气象条件较为敏感,基于气象资料和作物模型开展产量预测,在5%误差范围内可获得60%以上的预测概率;以多年气候平均值替代起报日后期气象资料,在成熟前2个月起报预测概率约为60%,成熟前1个月约为70%,但预报误差系统性偏高;采用气候相似类比方法,从历史气象资料中获取起报日后期替代资料,可有效降低预报误差的系统偏差,若引入后期气候趋势信息,成熟前2个月起报预测概率可达80%以上,较采用历史平均值有显著提高。研究结果为基于作物模型和气象观测及气候预测信息开展产量预报提供了技术方案。