杨秋明
江苏省气象科学研究所,江苏 南京 210009
Yang Qiuming
中图分类号: P456.9
文献标识码: A
文章编号: 1001-8166(2018)04-0385-11
收稿日期: 2017-05-17
修回日期: 2018-02-28
网络出版日期: 2018-04-20
版权声明: 2018 地球科学进展 编辑部
基金资助:
作者简介:
First author:Yang Qiuming(1963-), male, Changzhou City, Jiangsu Province, Professor. Research areas include weather and climate prediction.E-mail:yqm0305@263.net
作者简介:杨秋明(1963-),男,江苏常州人,研究员,主要从事天气气候预测研究.E-mail:yqm0305@263.net
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摘要
用1979—2011年逐日长江下游气温资料研究长江下游夏季高温日数与温度低频振荡的联系和变化特征。结果表明,长江下游夏季逐日气温主要有1525, 3060 和6070 d 的周期振荡,其中长江下游气温的3060 d 振荡强度年际变化和78月高温日数之间有显著的正相关。采用1979—2000年逐日长江下游气温3060 d低频分量和东亚850 hPa 低频温度主成分,构建了长江下游温度低频分量的延伸期预测的扩展复数自回归模型(ECAR)。对2001—2011年58月长江下游温度低频分量进行独立的实时延伸期逐日预报试验结果表明, 这种数据驱动的预测模型对3060 d时间尺度的长江下游低频温度分量的预测时效可达23 d左右, 对于提前2025 d预报长江下游地区夏季持续高温过程很有帮助,预报能力明显优于自回归模型(AR)。
关键词：
Abstract
Based on the observational data, the variations of Intraseasonal Oscillation (ISO) of the daily temperatures and its relationships to the high temperature in summer over the lower reaches of the Yangtze River Valley (LYRV) were studied for the period of 1979-2011. It is found that the daily temperatures over LYRV in May-August was mainly of periodic oscillations of 1525, 3060 and 6070 days, and the interannual variation of the intensity of its 3060-day oscillation had a strongly positive correlation with the number of days with daily highest temperature over 35 ℃ in July-August. Low frequency components of daily temperature in the LYRV, and the principal components of the Eastern Asian 850 hPa low frequency temperature, over a time period ranging from 1979 to 2000, were used to establish the Extended Complex Autoregressive model (ECAR) on an extended-range forecast of the 3060-day low frequency temperature over the LYRV. A 11-year independent real-time extended-range forecast was conducted on the extended-range forecast of low frequency component of the temperature over the LYRV in May-August, for the period ranging from 2001 to 2011. These experimental results show that this ECAR model, which is based on a data-driven model, has a good forecast skill at the lead time of approximately 23 days, with a forecast ability superior to the traditional autoregressive (AR) model. Hence, the development and variation of the leading 3060-day modes for the Eastern Asian 850 hPa low frequency temperatures and temporal evolutions of their relationships to low frequency components of the temperature over the LYRV in summer are very helpful in predicting the persistent high temperature over the LYRV at a 20 to 25 days lead.
Keywords：
长江下游地区(30.5°32.0°N, 118.0°122.5°E)是我国东部沿海和沿江产业密集带的交汇部, 发达城市多,人口稠密,夏季受东亚季风区各种环流(主要是西太平洋副高等)多时间尺度变化的影响,大部分城市经常出现持续性或间断性的异常高温酷暑天气, 对能源、经济和人们的正常生活带来严重影响。因此,分析该区域夏季温度低频变化特征,研究该区域高温事件的1030 d延伸期预报, 对于决策部门做出合理安排,有效降低灾害性事件带来的损失,开展防灾减灾工作具有重要的现实意义,同时为推进现代天气气候业务向无缝隙、精准化、智慧型方向发展提供有力支撑。
由于夏季东亚季风区各环流系统(如西太平洋副热带高压等)之间相互作用、互为反馈,并且共处于非线性系统之中,其大尺度低频环流型和温度型异常活动常常导致长江下游地区出现洪涝和干旱高温灾害。每年东亚季风环流的季节性转换都有不同特点,每次变异都有各自的机制,这些季风系统主要成员(如副热带高压)的形态突变与异常进退是一个极其复杂的过程,难以精确建立能够刻画季风系统主要成员活动与变异的“普适性”的动力学解析模型, 从而难以精确预测较长时效的1030 d延伸期的系统行为。本质上难以对其直接控制,只能间接地对宏观现象(如不同时间尺度的大尺度低频流型的变化等)进行计算、解析、建模实现知识的合成和预测。而大量观测数据可以近似代表全局,揭示出全局特有的宏观信息,这些综合信息是过去较小规模数据难以挖掘的。因此,充分利用长序列气象数据资源,进一步识别有效关键数据,由数据驱动构建各种更好地描述大气低频信息的简化的预测模型进行延伸期预测,是有效提高低频分量的延伸期预报精度的重要途径之一。另外,注重有质量的信息片段,并不断更新关键数据,深入研究抑制滤波边界效应的综合数据分析方法,可以明显减小初始时间滤波值的误差,精确地识别实时低频变化信号,进一步延长实时预报时效。
1030 d延伸期预报是近20多年来的国际大气科学研究的难点[1],很多气象专家从不同的角度对延伸期预报方法进行了研究[221], 主要有集合数值模式[15]、动力—统计[1619,21]、统计[914]和大数据[24]等方法,其中使用集合数值模式[22]或数据驱动的简化随机动力模式[5,6],热带大气MJO[22] 的预测时效已延长到2530 d,北半球夏季热带季节内振荡(Boreal Summer Intraseasonal Oscillation, BSISO)[23]的预测技巧达到25 d左右。关于夏季长江下游延伸期预报方法,目前国内外大量的研究主要集中于各种时间尺度(1020, 2030, 3050和5080 d等)的季节内振荡(Intraseasonal Oscillation, ISO)的低频降水和强降水(暴雨)过程预测, 采用动力、统计和大数据等多种方法[24],取得了很多有重要意义的成果,预报时效达到30 d左右,对于2030 d振荡显著年份,已延长到50 d左右[25]。但是, 对于长江下游夏季温度低频变化和持续高温特征分析和延伸期预报较少,尚未采用长序列数据详细分析高温天气和低频变化特征的联系及其延伸期预测研究。本文将用长序列(1979年1月1日至2011年12月31日)研究季节内时间尺度的长江下游气温低频振荡和78月高温日数的关系, 分析东亚850 hPa温度的低频主成分和长江下游地区气温主要低频分量之间的联系和时间变化特征;并由动态数据来驱动复杂低频变化过程与系统的构建,建立低频分量预测模型LFCF2.0[24], 即扩展复数自回归模型(Extended Complex Autoregressive model, ECAR)[26,27], 对2001—2011年58月逐日长江下游地区气温主要低频分量进行实时的独立延伸期预测试验, 同时还讨论了可预报性的年际变化。
东亚地区850 hPa温度场选用 NCEP/NCAR逐日再分析资料(2.5°×2.5°格点),逐日气温采用长江下游地区(图1a,绿色矩形区域: 30.5°32.0°N, 118.0°122.5°E) 中25个站平均值,以上资料时间是1979年1月1日至2011年12月31日(12 053 d),低频温度的独立预报试验时间是2001—2011年每年5月1日至8月31日。首先,对每年58月长江下游逐日气温进行非整数波功率谱分析,研究主要季节内周期振荡,分析不同时间尺度的温度ISO与78月长江下游地区高温日数(25个站日最高温度均大于35 ℃的日数)之间的联系,并分析东亚850 hPa温度场主要模态的时空变化及其与长江下游主要温度低频分量的关系。然后,用奇异谱分析(Singular Spectrum Analysis, SSA)[28],对长江下游逐日气温原始序列和由主成分分析(Principal Component Analysis, PCA)得到的东亚850 hPa温度场(15°60°N, 90°E180°)主要空间模态的时间系数进行低通滤波,重建对应于主要季节内振荡信号的分量序列,得到观测的长江下游温度低频分量序列和东亚850 hPa逐日温度场低频主成分,构建扩展复数自回归模型(ECAR)[26,27]进行延伸期独立预测试验,预测温度低频分量季节内变化(ECAR, 即低频分量预测模型LFCF2.0[24])。文中选择适当的子序列长度,滑动进行动态建模,适应低频分量之间相关的时间变化,提高模型的预测稳定性。另外,在实时SSA滤波(对应于低频重建分量)中,采用基于T-EOF预测的延拓方法[27]显著抑制滤波边界效应。这种全面反映原始序列信息的改进的SSA滤波可以得到更精确的边界附近的1025 d的滤波值,十分适合于实时延伸期业务预报。此外,在相关系数检验中还考虑滤波序列持续性的影响, 采用有效自由度进行显著性检验。
为了精确分析 58月长江下游逐日温度的低频振荡变化特征, 对1979—2011年逐年5月 1 日至8 月 31 日的逐日温度序列做非整数波功率谱分析, 并将每年各个周期(非整数)上的功率谱对应回归方程统计量F值以各周期作横坐标, 时间(年)为纵坐标, 做二维F值的时间—周期图(图1b, 当F>3.50 时, 其显著性是0.05)。从图1b可见, 能通过0.05显著性水平检验的主要周期为 1525, 3060和6070 d。3060 d振荡除在 20世纪 80 年代初期和中期表现不显著外, 其余时间均显著存在; 1525 d的周期振荡主要明显存在于1984年以后;3060 d周期, 主要出现在1986年以后,特别是1991年以来明显加强, 大多能通过 0.05 显著性水平检验;而6070 d周期,1984—1991年振荡不明显,其余年份均较显著。因此, 58 月长江下游逐日温度主要表现为 1525, 3060和6070 d的周期振荡,且存在明显的年际变化。此外, 10 d左右的高频振荡很弱。以上分析出的长江下游逐日温度多时间尺度振荡的变化,是夏季持续高温产生的基本条件之一,揭示其关键周期的作用和机制对于长江下游地区高温过程的延伸期预测非常重要。
图1c是1979—2011年盛夏78月长江下游地区高温日数(25个站每站日最高温度均大于35 ℃)的变化,其中1988年和2010年最多 (7 d),而且1979—1982年,1984—1987年,1989年,1991年,1993年,1996年,1997年和1999年无区域高温日数。图1d是高温日数与2,3,…,71 d温度振荡强度(对应周期(非整数)上的功率谱对应的回归方程统计量F值)的相关。从图1可以发现,长江下游地区高温日数与温度的3060 d振荡存在显著的稳定正相关,当这种45 d左右的振荡增强时,盛夏78月长江下游地区持续高温日数偏多。这种3060 d的温度振荡型可预报性较大,而且其周期明显长于夏季长江下游强降水过程显著相关的28 d 降水ISO的周期[29], 表明影响长江下游高温变化的ISO与影响强降水的ISO变化特性有显著差异。此外, 对于1025 d时间尺度, 长江下游地区高温日数与温度振荡强度的相关不显著,因而1525 d振荡与长江下游地区持续高温日数相关复杂或不确定, 但长江下游强降水与18 d 周期的ISO存在较强的正相关[29],它表明长江下游盛夏高温和夏季强降水与1020 d的ISO相互作用的机制有显著差异。由于3060 d 温度低频振荡与长江下游高温日数关系最密切, 对1030 d 时间尺度的延伸期高温天气预报具有更好的指示意义。所以, 本文主要针对3060 d时间尺度,构建长江下游地区低频温度分量的预测模型并提高其预测精度,为78月长江下游地区持续高温延伸期预测提供主要预报信号。
图1 夏季长江下游低频温度和高温变化
(a)长江下游地区; (b) 1979—2011年长江下游地区58 月逐日气温主要周期的年际变化,阴影区表示通过0.05显著性水平检验;(c)1979—2011 年78月高温日数的年际变化;(d)不同周期ISO之间的相关性,直方图表示78月高温日数与2,3,…,71 d振荡强度的相关,红色水平虚线表示通过0.05 显著性检验
Fig.1 Variations of low frequency temperature and high temperature over the LYRV
(a) The lower reaches of the Yangtze River valley (LYRV, green rectangle);(b) Interannual variations of the periods for the daily temperature over the LYRV in May-August during the period of 1979-2011,shaded values are significant at 95% confidence level; (c) The number of days with daily highest temperature over 35 ℃ in July-August during the period from 1979 to 2011; (d) Correlations beween the the number of days with daily highest temperature and oscillations from 2 to 71 days, and the significant level of 95% is represented by horizontal red dashed line
东亚季风区各环流系统之间相互作用、互为反馈,形成大尺度环流型和温度型异常活动(主要是反气旋和西风带高压脊等),导致长江下游地区极端降水[30]和温度事件的形成和异常发展,尤其是与夏季西太平洋副热带高压复杂变化对应的低层大气大尺度温度型的多时间尺度低频变化,是引起盛夏长江下游持续高温天气的主要原因之一。图2a给出了长序列(1979年1月1日至2000年12月31日)逐日长江下游地区3060 d低频温度与东亚850 hPa低频温度场的相关空间分布图,其中阴影区表示通过0.05的显著性检验(考虑滤波序列持续性的影响,当相关系数大于0.15时,其显著性为0.05)。图2a中显示出显著的欧亚中纬度地区经过长江下游地区向西太平洋热带地区传播的波列结构,类似于环流的EUP低频波列,最显著的强正相关区域在长江下游附近(显著性是0.001),同时东亚大陆中纬度和副热带西太平洋地区是负相关区(这些地区也是方差贡献大值区,图2b), 反映了东亚中高纬度和副热带西太平洋低频系统对长江下游温度低频变化的作用,它的影响过程具有多样性, 其机制需要进一步研究。为了分析3060 d时间尺度的东亚850 hPa低频温度场的时空变化特征,图3给出了东亚850 hPa 3060 d低频温度场前7个主要空间模态(资料时间是1979—2000年,序列长度是8 036 d, 由Butterworth滤波得到东亚850 hPa 3060 d低频温度场), 解释方差分别是14.9%,14.2%,8.9%,7.6%,7.1%,5.8%和5.0%。第17模态表现为7种不同的纬向(第3, 6模态,图3c和3f)和经向(第1,2,4,5,7模态;图3a,3b,3d,3e,3g))传播的东亚850 hPa低频温度波列, 它们与东亚季风各种环流低频波列异常传播和相互反馈和作用关系密切, 可以通过相关区域云—辐射反馈和不同的低频温度平流过程对影响区域表面气温的变化。这7个东亚850 hPa温度场低频主成分(PC)与长江下游温度低频分量的相关系数分别是0.40,0.42,0.27,0.17,-0.06,-0.15和 -0.12, 除了与PC5相关略低外,其余6个PC对应相关均达0.10的显著性(考虑序列持续性的影响,当相关系数为0.11和0.09时,其显著性可达0.05和0.10), 其中与第2模态(图3b)的正相关最为显著,显著性为0.001。这表明长江下游3060 d温度ISO与东亚季风区850 hPa温度模态存在较显著的相互作用,反映了在大气加热异常下,与东亚季风环流有关的各种温度低频波列传播中通过云—辐射反馈和温度平流过程影响对长江下游地区温度低频变化的高温过程的不同影响。这些低频温度模态是东亚地区热带内外各种季风环流系统相互作用反馈的结果,导致了西太平洋副热带高压复杂低频变化和夏季长江下游地区低频温度和持续高温变化的多样性。其中第1模态表现为从欧亚中高纬向热带西太平洋传播的低频波列传播(图3a),类似于长江下游温度与东亚850 hPa 温度的低频相关的低频波列(图2a), 反映了欧亚中高纬地区和热带太平洋地区环流的相互作用,与长江下游低频温度变化的正相关很显著(相关系数为0.40, 显著性达0.01),是影响长江下游温度低频变化的最重要的低频系统之一。这7种东亚850 hPa温度ISO型可以通过不同的方式直接或间接影响长江下游地区3060 d温度低频分量变化和盛夏高温干旱过程的形成,是长江下游地区温度延伸期预测的可预报性主要来源之一。
图2 长江下游和东亚850 hPa低频温度变化
(a)1979—2011年长江下游地区3060 d低频温度与东亚850 hPa低频温度场的相关分布;(b)东亚850 hPa低频温度场距平3060 d滤波 序列与原始序列季节内标准差比值的空间分布;(a)中相关系数已乘以100, 阴影表示通过0.05的显著性检验的区域;(b)中数值已乘以100, 单位: %, 阴影区表示≥30 的区域
Fig.2 Low frequency variations of temperature in the LYRV and Eastern Asian 850 hPa temperature
(a) Correlation between the daily temperature over LYRV and Eastern Asian 850 hPa temperature anomaly on the time scale of the 3060-day duringthe period from 1979 to 2011, in which values are multiplied by 100 and the significant levels of 95% are represented by shaded areas; (b) Spatialdistribution of ratio of the variance for the 3060-day signal to the total seasonal variability, whose values (unit: %) are multiplied by100 and the contours greater than or equal to 30 are shaded. Values are multiplied by 100
本文用1979年1月1日至2000年12月31日的资料(8 036 d),得到东亚850 hPa 3060 d低频温度场的前7个低频主成分和长江下游地区逐日低频温度,建立ECAR[26,27]对2001—2011年每年5月1日至8月31日(1 353 d)的长江下游地区逐日低频温度进行历史回报试验。文中将实时的东亚850 hPa温度场低频主成分和逐日长江下游温度分别经SSA后,由各自对应T-EOF各分量重建得到对应的3060 d振荡信号的分量序列作为预测试验的基本资料,构建ECAR模型预测2001—2011年58月长江下游地区 3060 d温度分量的季节内变化。这些独立预测用限定记忆法,保持子序列M0不变, 构建时变系数的ECAR滑动进行独立样本预测试验(回报时间是2001—2011年每年5月 1日至8月31日,预报未来 30 d,共1 353次预报),其中复数自回归模型的阶数p=2, 预报时间K=30 d, 子序列长度M0= 250 d。这种建模方案有利于适应复空间中各个低频分量之间的联系随时间的改变, 更好地反映气候系统分量之间时滞相关的一些非预期的时间变化。
在独立预测试验中,将2001—2011年逐日观测的东亚850 hPa温度场投影到前7个低频空间分布型(图3ag,由1979—2000年的逐日资料计算)得到前7个主成分PC1PC7的观测值(包含逐日高频扰动); 然后将这7个观测的PC和同期的长江下游逐日气温投影到各自经SSA后的对应于3060 d振荡的T-EOF上, 得到2001—2011年的逐日PC1,PC2,…,PC7和长江下游逐日气温的3060 d重构分量ti,1,ti,2,…,ti,7,tlcj (PC1,…,PC7和长江下游温度各自的T-EOF由1979—2000年的逐日资料计算), 构建扩展资料阵MFL+1=(ti,1,
图4a给出了用观测的东亚850 hPa温度场的低频主成分PC1PC7和长江下游地区3060 d温度低频分量构成的扩展实数据阵,通过Fourier变换构造扩展复数矩阵(Extended Complex Matrix,ECM), 建立时变ECAR模型做的2001—2011年每年58月逐日长江下游地区温度低频分量的130 d预报与观测的相关系数(共1 353 d次预报)。从图4可以看出, 长江下游地区温度低频分量的预报时效达23 d (观测和预报的低频分量之间相关系数大于0.5,其显著性是0.02,考虑序列持续性的影响)。这表明时变ECAR能有效预测与夏季东亚850 hPa温度场3060 d振荡传播有关的长江下游地区低频温度分量未来2025 d的变化。图4b给出了11, 14, 17和20 d预报技巧(对应于绿、红、蓝和紫色实线)逐年的变化,表明了大部分年份ECAR的预报技巧在未来20 d预报时效内均呈现显著的正相关(预报技巧大于0.50),仅2006年11 d以后的预报技巧出现明显降低。此外, 用观测的长江下游地区温度低频分量直接建立经典自回归模型(Autoregressive model,AR) 滑动进行独立样本预测试验(子序列长度 M0=250 d),预报时效只有7 d左右(图4a中虚线), 即大约一周以后的预报技巧明显下降,表明AR模型只反映了长江下游低频温度分量自身的变化信息(预测的稳定性减小),不能反映多种东亚低频温度模态的共同作用。
图3 1979—2000年东亚850 hPa 3060 d低频温度场的主要空间模态
(a)(g)对应于第17模态,图中数值已乘以1 000,虚线表示负值
Fig.3 Principal spatial modes of the Eastern Asian 850 hPa 30~60 day low frequency temperature field from 1979 to 2000
(a)(g) Corresponds to mode 1~7, the values in the figure has been multiplied by 1 000, the dashed lines represent negative values
图4 2001—2011年58月长江下游温度低频分量预报技巧
(a)130 d预报与观测的相关系数,实线 ECAR:模型,虚线:AR模型, 水平实线表示达到95%的显著性水平; (b) 年际变化,绿、红、蓝、紫线分别表示11, 14,17和20 d预报, 水平虚线表示达到95%的显著性水平
Fig.4 Forecast skills of low-frequency temperature component over LYRV in May-August during the period of 2001-2011
(a) Correlation coefficients between the observation and the 1~30-day forecast of low frequency temperature component, solid line: ECAR model; Dashed line: AR model; The horizontal solid line in the figure represents the significance level of 95%; (b) Interannual variations of forecast skills, the lead times are 11 days (green line), 14 days (red line), 17 days (blue line) and 20 days (purple line), respectively. The horizontal dashed line in the figure represents the significance level of 95%
图5进一步给出2003年、2004年、2006年和2009年的长江下游逐日低频温度分量20 d预报 (虚线)和观测的低频分量(实线)的变化曲线, 其中3060 d振荡较强的2003年(图5a), 2004年(图5b)和2009年(图5d)的相关系数分别达到0.72,0.83和0.73 (123次预报,初始时间分别是4 月11 日,4月12日,…, 8月11日),其显著为0.02。从图5可以看出,与上述年份中78月的23个主要长江下游持续高温集中期对应的显著的低频温度正位相变化和干旱少雨期均能较准确地预测,尤其是与2004年5月中旬到8月上旬的3次持续正温度距平(图5b,其中78月伴随2次持续高温过程)对应的低频温度分量正位相变化能很好地预测,其预报的正位相变化与实况基本一致,预测的峰值也与实况十分接近。此外, 2009年8月中旬后期到8月下旬前期的持续高温(连续正温度距平过程)也能提前20 d左右预报(图5d)。但7月下旬的负温度过程位相明显滞后,温度振幅预报偏小。此外, 2006年的20 d预报技巧明显降低,相关系数只有0.40(图5c),主要原因是这一年夏季3060 d振荡处于减弱时段,ECAR预测模型出现一些不稳定。以上大量独立预报试验表明, ECAR能有效描述这11年中的大部分年份(夏季3060 d振荡显著年份)观测数据中温度主要低频分量之间多种时滞变化,从而能较精确地预测这些低频变化。因此,这种LFCF2.0(时变复系数,ECAR)通过数据驱动的SSA, 从时间序列的动力重构出发,滤去了序列中的高频噪声和非周期的弱信号,可以较好地识别长江下游气温和东亚温度主要低频模态的非均匀时间变化分量; 并通过基于T-EOF的序列延拓显著减弱滤波的边界效应,从而使实时分量的重建序列分别成为单一的稳定的信号序列,显著增强了可预报性。然后构造复数空间中的虚拟数据扩展有效数据规模,基于实虚数据之间的可能联系和低频分量协同演化新规律,由数据驱动构建能更好地描述大气低频分量相互作用的简化的复数自回归延伸期预测模型,可以较好地提前20 d以上预测长江下游地区夏季高温干旱少雨时段的变化。
图5 58月长江下游地区3060 d温度低频分量的ECAR模型的20 d预报
(a),(b),(c)和(d)分别是2003年、2004年、2006年和2009年,图中实(虚)线分别表示实况(预报),直方图表示长江下游地区逐日气温距平变化(单位:℃); r是预测和实况之间的相关系数,预报的初始时间分别是4月11日,…, 8月11日
Fig.5 The forecast of ECAR model at a lead time of 20 days from May to August
(a) 2003, (b) 2004, (c)2006 and (d) 2009 over the LYRV. Solid (dashed) line represents the observation (the forecast) of low-frequency temperature, the histogram represents the daily temperature anomalies over LYRV, unit: ℃; r is the correlation coefficient between the forecast and the observation; The initial date of forecast is April 11,…, August 11
此外,这些大量历史数据的独立预报试验还表明, 夏季长江下游地区温度3060 d振荡低频分量的ECAR模型的延伸期预报技巧的年际变化不大,大部分年份20 d左右预报技巧在0.5左右(图4b),仅2006年略低,但仍达到0.4(图5c), 可以为长江下游地区夏季高温干旱过程的实时延伸期预测提供稳定的预报信息。它体现了LFCF2.0(时变复系数ECAR)可以很好地描述在大气加热异常作用下(如海温,雪盖和土壤湿度等)东亚地区各种季风低频环流相联系的温度空间模态变化,以及通过云—辐射反馈和低频温度平流过程对长江下游地区夏季表面气温低频变化和持续高温期形成的影响过程,但这些ISO型强度的变化机制仍需深入分析研究。
本文用1979—2011年逐日长序列资料,研究长江下游地区温度主要季节内变化周期与78月高温日数的联系及其与东亚850 hPa温度模态变化的关系,基于大量观测资料中提取的季节内时间尺度长江下游夏季气温和东亚850 hPa温度主要低频分量,构建扩展复数自回归模型(ECAR),即LFCF2.0,进行夏季长江下游低频温度分量延伸期预测研究,得到如下结论:
长江下游地区夏季逐日气温主要有1525, 3060 和6070 d 的周期振荡,其中长江下游气温的3060 d 振荡强度年际变化和78月高温日数之间有显著的正相关。这种3060 d 振荡与东亚850 hPa 温度主要分布型变化密切相关,其中与第2模态的正相关最为显著。它反映了夏季东亚地区大气加热异常激发的东亚地区各种低频环流和温度空间模态变化通过云—辐射反馈和低频温度平流过程对长江下游地区夏季表面气温低频变化的影响。另外, 1525 d 振荡与78月高温日数之间相关不显著。
基于东亚850 hPa 温度场3060 d低频主要模态,在复空间上建立的时变ECAR预报模型(LFCF2.0)可以有效预测2001—2011年58月长江下游逐日温度3060 d低频分量未来23 d的变化,能将与温度ISO变化对应的长江下游持续高温事件的预报时效延长到2025 d,可以为长江下游地区夏季持续高温(干旱)过程的实时延伸期预测提供稳定的预报信号;而用长江下游地区温度低频分量直接建立经典AR模型的预报时效只有7 d左右。
经典的SSA重构的低频分量序列右边界附近(1025 d)误差较大, 与其他常用滤波方法(如Butterworth滤波, 小波分析, EEMD等)一样, 右边界附近1025 d滤波值误差明显,会导致未来10 d以上的实时预测结果不可靠,不能用于实时1030 d延伸期预测。 但本文用经T-EOF延拓序列(扩展序列,右端用T-EOF预测数据(虚拟数据)适当延长序列长度)投影得到的SSA低频重构分量, 显著提高边界附近滤波值精度(只使用初始时间t0以前的数据), 因而这种全面反映原始序列信息的改进的SSA滤波方法十分适合于实时延伸期预测。
本文对长序列观测资料进行分解、变换, 得到描述长江下游地区夏季气温低频变化和东亚850 hPa温度低频变化主要模态,将这些主要分量视为多样化的气候动态数据变化过程的一系列数据解,反演出可预报性较大的简化数据模型ECAR, 显著减小了计算误差,延长预报时效。这种数据驱动的气候预测方法表现为大量多种类型的数据分解、变换、逼近历史数据和实时数据中的变化规律, 并用于解析、预测和评估的各方面的集合。同时用T-EOF预测数据(虚拟数据)适当延长右边界序列进行SSA滤波,可以较好地抑制滤波边界效应得到稳定的实时ISO信号,较精确识别复杂气候系统自然变化中的很丰富同时很严谨的结构,体现了数据处理、简化模型的构建、解析、预测、评估和信息实时更新的一体化。这些方法减少人们处理数据时带入的主观假设的影响,它不预设物理条件,可以不受数值预报时效的可预报性限制。它基于扩展的复数空间,构建低频分量预测的解空间,从观测和虚拟数据中提取更多隐藏在数据规律里的有价值的低频信息,优化数据分析方法。这种数据驱动ISO系统构建的预测方法完全依靠数据间的多种时滞相关性(实数和复数空间)及其时间变化来阐述,通过不同角度评估不同的低频分量对于极端天气事件时间变化的相对重要性,可以建立预测稳定性更好的数据模型,提高实时延伸期预报精度。
The authors have declared that no competing interests exist.
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The statistical extended-range (10-30-day) forecast of summer rainfall anomalies over the entire China [J].
The extended-range (10-30-day) rainfall forecast over the entire China was carried out using spatial-temporal projection models (STPMs). Using a rotated empirical orthogonal function analysis of intraseasonal (10-80-day) rainfall anomalies, China is divided into ten sub-regions. Different predictability sources were selected for each of the ten regions. The forecast skills are ranked for each region. Based on temporal correlation coefficient (TCC) and Gerrity skill score, useful skills are found for most parts of China at a 20-25-day lead. The southern China and the mid-lower reaches of Yangtze River Valley show the highest predictive skills, whereas southwestern China and Huang-Huai region have the lowest predictive skills. By combining forecast results from ten regional STPMs, the TCC distribution of 8-year (2003-2010) independent forecast for the entire China is investigated. The combined forecast results from ten STPMs show significantly higher skills than the forecast with just one single STPM for the entire China. Independent forecast examples of summer rainfall anomalies around the period of Beijing Olympic Games in 2008 and Shanghai World Expo in 2010 are presented. The result shows that the current model is able to reproduce the gross pattern of the summer intraseasonal rainfall over China at a 20-day lead. The present study provides, for the first time, a guide on the statistical extended-range forecast of summer rainfall anomalies for the entire China. It is anticipated that the ideas and methods proposed here will facilitate the extended-range forecast in China.
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| [10] |
Statistical extended-range forecast of winter surface air temperature and extremely cold days over China [J].URL 摘要
react-text: 238 Hawaiian surface air temperature (HST) during summer 2015 (from July to October) was about 1.5掳C higher than the climatological mean, which was the hottest since records began in 1948. In the context of record-breaking seasonal-mean high temperature, 98 exceptional local heatwave days occurred during summer 2015. Based on diagnoses and simulations, we demonstrate in this paper that the... /react-text react-text: 239 /react-text [Show full abstract]
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| [11] |
Extended-range forecasting of Chinese summer surface air temperature and heat waves [J].
Because of growing demand from agricultural planning, power management and activity scheduling, extended-range (5–30-day02lead) forecasting of summer surface air temperature (SAT) and heat waves over C
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| [12] |
Extended range (1030 days) heavy rain forecasting study based on a nonlinear cross-prediction error model [J].
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.
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| [13] |
Extraction and predictability of coherent intraseasonal signals in infrared brightness temperature data [J].
This work studies the spatiotemporal structure and regime predictability of large-scale intraseasonal oscillations (ISOs) of tropical convection in satellite observations of infrared brightness temperature (T_b). Using nonlinear Laplacian spectral analysis (NLSA), a data analysis technique designed to extract intrinsic timescales of dynamical systems, the T_b field over the tropical belt 15掳 S-15掳 N and the years 1983-2006 (sampled every 3 h at 0.5掳 resolution) is decomposed into spatiotemporal modes spanning interannual to diurnal timescales. A key advantage of NLSA is that it requires no preprocessing such as bandpass filtering or seasonal partitioning of the input data, enabling simultaneous recovery of the dominant ISOs and other patterns influenced by or influencing ISOs. In particular, the eastward-propagating Madden-Julian oscillation (MJO) and the poleward-propagating boreal summer intraseasonal oscillation (BSISO) naturally emerge as distinct families of modes exhibiting non-Gaussian statistics and strong intermittency. A bimodal ISO index constructed via NLSA is found to have significantly higher discriminating power than what is possible via linear methods. Besides MJO and BSISO, the NLSA spectrum contains a multiscale hierarchy of modes, including the annual cycle and its harmonics, ENSO, and modulated diurnal modes. These modes are used as predictors to quantify regime predictability of the MJO amplitude in T_b data through a cluster-based framework. It is found that the most predictable MJO regimes occur before the active-MJO season (November-December), when ENSO has a strong influence on the future statistical behavior of MJO activity. In forecasts initialized during the active-MJO period (February), both ENSO and the current state of MJO are significant predictors, but the predictive information provided by the large-scale convective regimes in T_b is found to be smaller than in the early-season forecasts.
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| [14] |
Extraction and prediction of indices for monsoon intraseasonal oscillations: An approach based on nonlinear Laplacian spectral analysis [J].
An improved index for real-time monitoring and forecast verification of monsoon intraseasonal oscillations (MISOs) is introduced using the recently developed nonlinear Laplacian spectral analysis...
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| [15] |
Predictability-based extended-range ensemble prediction method and numerical experiments [J].基于延伸期可预报性的集合预报方法和数值试验 [J]. |
| [16] |
Development of the analogue-dynamical method for error correction of numerical forecasts [J]. |
| [17] |
Improvement of medium-range forecasts using the analogue-dynamical method [J].
AbstractThis study further develops the analog-dynamical method and applies it to medium-range weather forecasts. By regarding the forecast field as a small disturbance superimposed on historical analog fields, historical analog errors can be used to estimate and correct forecast errors. This method is applied to 10-day forecasts from the Global and Regional Assimilation and Prediction System (GRAPES). Both the distribution of atmospheric circulation and the pattern of sea surface temperature (SST) are considered in choosing the analog samples from a historical dataset for 2001鈥10 based on NCEP Final (FNL) data. The results demonstrate that the analog-dynamical method greatly reduces forecast errors and extends the period of validity of the global 500-hPa height field by 0.8 days, which is superior to results obtained using systematic correction. The correction effect at 500 hPa is increasingly significant when the lead time increases. Although the analogs are selected using 500-hPa height fields, the for...
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| [18] |
An approach to estimating and extrapolating model error based on inverse problem methods: Towards accurate numerical weather prediction [J].
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| [19] |
Numerical model error estiamtion by derivative-free optimization method [J].基于无导数优化方法的数值模式误差估计 [J].
初始场误差和模式误差是制约数值预报准确率的两个关键因素,本文主要考虑利用历史观测资料实现时空演变的模式误差的估计问题. 通过把模式误差综合考虑成为准确模式中的未知项,把历史资料看作是带有未知项的准确模式的特解,构造了求解时空演变的模式误差项的反问题及其最优控制问题. 给出了一个解决最优控制问题的无导数优化方法,该方法的优点是不需要建立原数值模式的切线性模式与伴随模式,它只需在增加一个外强迫项的基础上运行原数值模式即可实现模式误差项的最优估计. 关于Burgers方程的算例表明,无论模式的初始状态是否准确已知,无导数优化方法都能有效解决时空演变的模式误差的最优估计问题,它为实际业务模式利用历史数据提取模式误差信息并显著地改进预报效果提供了一种方便可行的数值方法与理论依据.
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| [20] |
The role of boundary and initial conditions for extended range forecast [J].
延伸期预报中的大气初值与海温边值相对作用 [J].
初始条件记忆和下垫面条件是延伸期可预报性的主要来源,它们在不同时段、不同区域的相对作用存在明显不同。利用中国国家气候中心最新一代大气环流模式BCC_AGCM,设计了由4组不同大气初值与海温边值构成的组合试验,研究了(大气)初值与(海温)边值对全球不同区域延伸期预报的相对作用。结果显示,模式预测技巧在3周以内强烈依赖于初值。在相同的边值条件强迫下,不同初值在月内尺度上的预测技巧差异非常明显,且在更长的时间尺度上,初值仍然能够提供一定的预测信息。从全球来看,边值强迫对预测技巧的影响从一周左右开始。在低层的850 hPa高度场上,边值的作用在热带地区于第4—5候与初值相当,其他区域的边值影响达到与初值相当的时间滞后于热带地区;在北半球500 hPa高度场上,边值的作用在热带地区第5候前后达到与初值作用相当,其他区域这个时间则推迟至第6候前后,比对低层的影响时间滞后1—2候;对于东亚地区而言,边值的贡献在第2候就已显现,对预测技巧产生了明显的改进。在延伸期尺度上,边值强迫主要作用的区域为低纬度区域,且对500 hPa高度场的影响更为稳定,在该区域第5候以后有较为明显的改进。初值与边值对延伸期预报都具有相当重要的作用,认识初、边值条件的相对作用能够为延伸期预报的改进奠定基础。
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| [21] |
Extended range forecast experiments of persistent winter low temperature indexes based on intra-seasonal oscillation over southern China [J].基于低频振荡信号的中国南方冬半年持续性低温指数延伸期预报试验 [J].
利用1961-2009年36°N以南、108°E以东中国大陆191个站点逐日最低气温和NCEP/NCAR再分析日平均格点资料,研究与区域持续性低温事件有关的大气低频振荡信号,寻找可以在一定程度上表征不同类型区域持续性低温事件的指数,并尝试结合DERF2.0系统的预报产品进行持续性低温指数的延伸期预报试验.结果表明:(1)在研究范围内的区域持续性低温事件可以归纳为江北型、江南型和全区域型3类,其中江北型和江南型事件的环流背景差异体现在异常环流中心的纬度位置上,而全区域型事件属于增强型的江北型事件;(2)江北型和江南型区域平均最低气温时间序列的10-30 d低频分量的位相和强度变化与区域持续性低温事件的发生有显著关系,可以作为表征区域持续性低温事件指数和预报量;(3)100°-120°E范围内850 hPa温度场距平的经验正交函数分解前两个主模态具有显著的10-30 d变化周期,并且其空间结构分别与江北型和江南型事件的典型环流特征一致,前两个主模态时间系数能够作为持续性低温指数的预报因子;(4)检验结果表明, DERF2.0系统对上述预报因子有一定的预报能力.在延伸期预报时效内,利用统计学和动力学相结合的方法制作的持续性低温指数的预报效果好于模式直接预报的2 m气温,该预报方法有助于提升区域持续性低温事件的延伸预报能力.
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| [22] |
Predictability of the Madden-Julian Oscillation in the intraseasonal variability hindcast experiment (ISVHE) [J].
ABSTRACT The Madden-Julian Oscillation (MJO) represents a primary source of predictability on the intraseasonal time scales and its influence extends from seasonal variations to weather and extreme events. While the last decade has witnessed marked improvement in dynamical MJO prediction, an updated estimate of MJO predictability from a contemporary suite of dynamic models, in conjunction with an estimate of their corresponding prediction skill, is crucial for guiding future research and development priorities. In this study, the predictability of the boreal winter MJO is re-visited based on the Intraseasonal Variability Hindcast experiment (ISVHE), a set of dedicated extended range hindcasts from eight different coupled models. Two estimates of MJO predictability are made, based on single member and ensemble mean hindcasts, giving values of 20-30 days and 35-45 days, respectively. Exploring the dependence of predictability on the phase of MJO during hindcast initiation reveals a slightly higher predictability for hindcasts initiated from MJO phases 2, 3, 6 or 7 in three of the models with higher prediction skill. Given the estimated predictability of MJO initiated in phases 2 and 3 (i.e. convection in Indian Ocean with subsequent propagation across Maritime Continent), being equal to or higher than other MJO phases, implies that the so-called 鈥淢aritime Continent prediction barrier鈥 may not actually be an intrinsic predictability limitation. For most of the models, the skill for single member (ensemble mean) hindcasts is less than the estimated predictability limit by about 5-10 days (15-25 days)., implying that significantly more skillful MJO forecasts can be afforded through further improvements of dynamical models and ensemble prediction systems (EPS).
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| [23] |
Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment [J].
Boreal summer intraseasonal oscillation (BSISO) is one of the dominant modes of intraseasonal variability of the tropical climate system, which has fundamental impacts on regional summer monsoons, tropical storms, and extra-tropical climate variations. Due to its distinctive characteristics, a specific metric for characterizing observed BSISO evolution and assessing numerical models’ simulations has previously been proposed (Lee et al. in Clim Dyn 40:493–509, 2013 ). However, the current dynamical model’s prediction skill and predictability have not been investigated in a multi-model framework. Using six coupled models in the Intraseasonal Variability Hindcast Experiment project, the predictability estimates and prediction skill of BSISO are examined. The BSISO predictability is estimated by the forecast lead day when mean forecast error becomes as large as the mean signal under the perfect model assumption. Applying the signal-to-error ratio method and using ensemble-mean approach, we found that the multi-model mean BSISO predictability estimate and prediction skill with strong initial amplitude (about 1002% higher than the mean initial amplitude) are about 45 and 2202days, respectively, which are comparable with the corresponding counterparts for Madden–Julian Oscillation during boreal winter (Neena et al. in J Clim 27:4531–4543, 2014a ). The significantly lower BSISO prediction skill compared with its predictability indicates considerable room for improvement of the dynamical BSISO prediction. The estimated predictability limit is independent on its initial amplitude, but the models’ prediction skills for strong initial amplitude is 602days higher than the corresponding skill with the weak initial condition (about 1502% less than mean initial amplitude), suggesting the importance of using accurate initial conditions. The BSISO predictability and prediction skill are phase and season-dependent, but the degree of dependency varies with the models. It is important to note that the estimation of prediction skill depends on the methods that generate initial ensembles. Our analysis indicates that a better dispersion of ensemble members can considerably improve the ensemble mean prediction skills.
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| [24] |
Prospects and progresses in the research of the methods for 10-30 days extended-range weather forecast [J].10-30 d延伸期天气预报方法研究进展与展望 [J].
<p>10~30 d延伸期预报是国际大气科学关注的研究热点。这一时间段的预报对开展防灾、救灾工作具有极其重要的价值和意义,该工作需要结合初始气象条件、海洋、大气以及气候的影响因素,其中观测资料具有复杂性、综合性、全球性等,这些科学大数据反映和表征着复杂的自然现象与关系,具有高度数据相关性和多重数据属性,预测过程十分复杂。分析了延伸期预报的各种主流方法, 其中重点介绍了动力模式、经典统计和大数据方法3类预报方法的研究现状,并探讨了各种方法的优势和不足,在此基础上对目前延伸期预报领域存在的问题进行了讨论和总结。对延伸期预报方法的未来发展方向和应用前景给以展望。</p>
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| [25] |
Study of the method of the extended-range forecast for the low frequency rainfall over the lower reaches of the Yangtze River in summer based on the 20-30 day oscillation [J].基于20-30d振荡的长江下游地区夏季低频降水延伸期预报方法研究 [J].
用长江下游降水低频分量和环流低频主成分，构造多变量时滞回归模型（MLR）和主成分复数自回归模型（PC-CAR）的混合预报模型（MLR/PC-CAR），对长江下游降水低频分量进行延伸期逐日变化预报,延长预报时效。通过2011年6—8月预测试验表明，20—30 d时间尺度的长江下游低频降水预测时效可达50 d左右，采用南半球中高纬度地区850 hPa 低频经向风的主成分作为预测因子的模型的预测精度明显高于东亚地区低频经向风作为预测因子的模型。这表明在20—30 d时间尺度上，长江下游降水与南半球中纬度绕球遥相关（SCGT）型有关的主分量的时滞相关更加密切。进一步对于较强20—30 d振荡的多年资料构建的MLR/PC-CAR混合模型预测试验表明，SCGT是预测夏季长江下游低频降水未来50 d变化的显著信号。基于SCGT的发展和演变，对于把握类似长江下游地区2011 年6月初旱涝急转和7月中旬持续降水和强降水过程异常变化过程很有帮助，SCGT可以作为夏季长江下游20—30 d低频降水和强降水过程进行延伸期预报的主要可预报性来源之一。
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| [26] |
Extended complex autoregressive model of low frequency rainfalls over the lower reaches of Yangtze River Valley for extended-range forecast in 2013 [J].2013年长江下游降水低频分量延伸期预报的扩展复数自回归模型 [J].
<p>用长江下游降水低频分量和全球850 hPa低频经向风主成分,建立扩展复数自回归模型（ECAR）,对2013年1–12月长江下游降水低频分量进行延伸期逐日变化预报试验.结果表明,20–30 d 时间尺度的长江下游低频降水预测时效可达43 d左右,能较好地预测与暴雨过程对应的低频分量的非线性增长过程,预报能力明显优于自回归模型（AR）.这种通过构造主要低频序列组成的扩展复数矩阵（ECM）进行复数自回归（CAR）建模的ECAR方法,也为展现气候系统内部分量之间相互作用的动力学过程提供了崭新的描述. 基于全球环流主要20–30 d振荡型的发展和演变,对于提前27 d预报长江下游地区2013年10月上旬后期大暴雨过程很有帮助,其中南半球热带外环流20–30 d振荡是影响2013年夏秋季长江下游地区延伸期强降水变化的一个主要因子.</p>
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| [27] |
Predictability and prediction of low frequency rainfall over the lower reaches of the Yangtze River valley on the time scale of 2030 days [J].
Abstract This paper presents a predictability study of the 20-30-day low-frequency rainfall over the lower reaches of the Yangtze River valley (LYRV). This study relies on an extended complex autoregressive (ECAR) model method, which is based on the principal components of the global 850 hPa low-frequency meridional wind. ECAR is a recently advanced climate forecast method, based on data-driven models. It not only reflects the lagged variations information between the leading low-frequency components of the global circulation and rainfall in a complex space, but also displays the ability to describe the synergy variations of low-frequency components of a climate system in a low dimensional space. A 6-year forecast experiment is conducted on the low-frequency rainfall over the LYRV for the extended-range daily forecasts during 2009-2014, based on the time-varying high-order ECAR. These experimental results demonstrate that the useful skills of the real-time forecasts are achieved for an extended lead-time up to 28 days with a fifth-order model, and are also shown to be 27-day lead for forecasts which are initiated from weak intraseasonal oscillation (ISO). This high-order ECAR displays the ability to significantly improve the predictions of the ISO. The analysis of the 20-30-day ISO predictability reveals a predictability limit of about 28-40 days. Therefore, the forecast framework used in this study is determined to have the potential to assist in improving the real-time forecasts for the 20-30-day oscillations related to the heavy rainfall over the LYRV in summer.
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| [28] |
Adaptive filtering and prediction of intraseasonal oscillations [J].
Develops a statistical model for monitoring and forecasting convection patterns as represented by outgoing longwave radiation anomalies (OLRA) in the intraseasonal band over the Indian-Pacific and in the pan-American region. Derivation of the singular spectrum analysis (SSA) for the statistical method; Prediction based on the SSA-maximum entropy method combination; Comparison with a method for forecasting Madden-Julian oscillation.
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| [29] |
The 20-30-day oscillation of the global circulation and heavy precipitation over the lower reaches of the Yangtze River Valley [J].全球环流20-30 d 振荡与长江下游强降水 [J]. |
| [30] |
Review of impacts of the global atmospheric intraseasonal oscillation on the continuous heavy rainfall over the Yangtze River Valley [J].全球大气季节内振荡对长江流域持续暴雨影响的研究进展 [J].
<p>在引证论述大气季节内振荡(ISO)对暴雨(强降水)重要作用的基础上，概括性地回顾影响长江流域持续暴雨的大气ISO 基本特征及其形成机制的主要成果。重点针对全球热带内外不同时间尺度的大气ISO的空间变化和年际变化与长江流域持续暴雨之间联系的研究工作进行总结评述，较为完整地总结长江流域夏季降水季节内变化的气候特征和全球不同空间和时间尺度的ISO对东亚副热带地区持续暴雨影响的已有认识，并结合2个半球的ISO与长江流域持续暴雨关系的研究现状,对未来暴雨(强降水)与不同时尺度ISO相互作用及其在10~30 d延伸期预报中的应用中有价值的科学问题和研究热点进行探讨，以期强调南半球ISO变化在全球和东亚副热带气候系统中的重要地位,提高夏季长江流域持续暴雨10~30 d延伸期预报和旱涝气候预测准确率。</p>
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