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冬季长江下游地区气温低频振荡和低温天气的延伸期预报研究

杨秋明

杨秋明. 2021. 冬季长江下游地区气温低频振荡和低温天气的延伸期预报研究[J]. 大气科学, 45(1): 21−36 doi: 10.3878/j.issn.1006-9895.2007.19208
引用本文: 杨秋明. 2021. 冬季长江下游地区气温低频振荡和低温天气的延伸期预报研究[J]. 大气科学, 45(1): 21−36 doi: 10.3878/j.issn.1006-9895.2007.19208
YANG Qiuming. 2021. Extended-Range Forecast for the Low-Frequency Oscillation of Temperature and Low-Temperature Weather over the Lower Reaches of the Yangtze River in Winter [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(1): 21−36 doi: 10.3878/j.issn.1006-9895.2007.19208
Citation: YANG Qiuming. 2021. Extended-Range Forecast for the Low-Frequency Oscillation of Temperature and Low-Temperature Weather over the Lower Reaches of the Yangtze River in Winter [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 45(1): 21−36 doi: 10.3878/j.issn.1006-9895.2007.19208

冬季长江下游地区气温低频振荡和低温天气的延伸期预报研究

doi: 10.3878/j.issn.1006-9895.2007.19208
基金项目: 国家自然科学基金项目41175082
详细信息
    作者简介:

    杨秋明,男,1963年出生,研究员,主要从事天气气候预测研究。E-mail: yqm0305@263.net

  • 中图分类号: P456

Extended-Range Forecast for the Low-Frequency Oscillation of Temperature and Low-Temperature Weather over the Lower Reaches of the Yangtze River in Winter

Funds: National Natural Science Foundation of China (Grant 41175082)
  • 摘要: 用1979/1980~2017/2018年冬季逐日长江下游气温资料研究长江下游冬季低温日数与温度低频振荡的联系。结果表明,冬季长江下游逐日气温存在较显著的季节内振荡周期(15~25 d、25~40 d和50~70 d振荡),其中与12~2月低温日数关系最密切的是25~40 d振荡。基于2001~2018年逐日长江下游实时气温的25~40 d低频分量和东亚地区850 hPa低频温度主成分,建立了冬季长江下游气温低频分量的延伸期预测的时变扩展复数自回归模型(ECAR)。其中,采用基于T-EOF(temporal empirical orthogonal functions)延拓的实时奇异谱(SSA,singular spectrum analysis)滤波,很好地抑制经典SSA滤波的边界效应,得到较稳定的实时低频振荡信号。对2001/2002~2017/2018年12~2月长江下游温度低频分量进行独立的实时延伸期预报试验的结果表明,这种数据驱动的简化的复数预测模型对25~40 d时间尺度的长江下游冬季低频温度的预测时效可达26 d左右,预报能力显著优于经典自回归模型(AR),能为提前3~4周预报长江下游地区冬季持续低温过程提供有价值的预测背景信息。
  • 图  1  1979/1980~2017/2018年长江下游地区12~2月(a)逐日气温主要周期的年际变化,(b)低温日数的年际变化以及(c)低温日数与周期为10 d、11 d、……、71 d气温振荡强度的相关系数。图a中横坐标表示各个周期(对应非整数波功率谱),黑色等值线表示不同周期功率谱对应的回归方程统计量变化,阴影区表示通过99%信度水平的显著性检验,图c中红色水平虚线表示95%信度水平

    Figure  1.  (a) Interannual variations of the main periods for the daily temperature, (b) interannual variations of low temperature days, (c) correlation coefficients between low temperature days and oscillation intensity of temperature from 10 to 71 days periods over the lower reaches of the Yangtze River (LYR) in December–February of 1979/1980–2017/2018. In Fig. a, black contours represent the variations of the statistical parameter for the regression equation corresponding to the non-integer period of power spectra (with the individual periods (non-integer) as the x-axis), shadings areas indicate periods are significant at the 99% confidence level; In Fig. c, the horizontal red dashed lines represent the 95% confidence level

    图  2  (a)1979~2018年长江下游地区25~40 d低频气温与东亚850 hPa 25~40 d低频温度的相关系数分布,图中相关系数值是原始值×100后的结果,阴影表示通过95%信度水平显著性检验的区域;(b)850 hPa低频温度场距平25~40 d滤波序列与原始序列季节内方差比值的空间分布,图中数值是原始值×100后的结果(单位:%),阴影区表示方差比值≥30的区域

    Figure  2.  (a) Correlation coefficients between the low-frequency air temperature over the LYR and 850-hPa low-frequency temperature in eastern Asia on the 25–40-day time scale from 1979 to 2018, in which values are multiplied by 100 and shadings indicate the correlation coefficients above 95% confidence level; (b) spatial distribution of ratio of the variance for the 850-hPa low-frequency temperature anomalies on the 25–40-day time scale to the total seasonal variability, in which values are multiplied by 100 and shadings indicate ratio of the variance greater than or equal to 30

    图  3  1979~2000年东亚850 hPa 25~40 d低频温度场EOF分解的(a–d)第1~4空间模态,图中数值已乘以1000,实(虚)线表示正(负)值

    Figure  3.  Principal spatial modes of the 850-hPa low-frequency temperature on the 25–40-day time scale in eastern Asia from 1979 to 2000, Figs. a–d correspond to modes 1–4 of EOF (empirical orthogonal functions). Values are multiplied by 1000, and the solid (dashed) lines represent positive (negative) values

    图  4  2001~2003年长江下游25~40 d低频温度与东亚850 hPa 25~40 d低频温度EOF分析主要模态对应的时间系数的365天滑动相关系数:(a)PC1;(b)PC2;(c)PC3。水平虚线表示95%信度水平

    Figure  4.  Sliding correlation coefficients between the low-frequency temperature in the LYR and the principal components (PC) of 850-hPa low-frequency temperature in eastern Asia on the 25–40-day time scale with a window length of 365 days during 2001–2003: (a) PC1; (b) PC2; (c) PC3. The horizontal dashed lines represent the 95% confidence level

    图  5  2001~2018年长江下游地区逐日25~40 d低频温度(对长序列的经典SSA滤波,红线)与(a)基于T-EOF延拓的SSA滤波和(b)无T-EOF延拓的SSA滤波的实时低频温度(蓝线)的变化。r是它们之间的相关系数

    Figure  5.  Time series of the 25–40-day low-frequency temperature [SSA (singular spectrum analysis) filtered, red lines] in the LYR and real low-frequency temperature (blue lines) with the (a) T-EOF (temporal empirical orthogonal functions) extension and (b) without the T-EOF extension from 2001 to 2018. r indicates their correlation coefficients

    图  6  时变ECAR预测模式构建示意图。T850:实时东亚850 hPa温度;T1T2T3T4:实时东亚850 hPa温度低频主成分(PC);${t_{lcj}}$:实时长江下游低频温度;F:扩展资料矩阵,${t_0}$:初始时间;${M_0}$:子序列长度;TE1,TE2,TE3,TE4:1979年1月1日至2000年12月31日25~40 d东亚850 hPa温度主要模态前4个主模态V1V2V3V4的T-EOFs;TEt:1979年1月1日至2000年12月31日25~40 d长江下游温度的T-EOFs

    Figure  6.  Schematic representation of the time-varying ECAR (Extended complex autoregressive) forecasting model. T850: Real-time 850-hPa temperature in eastern Asia; T1, T2, $\cdots $, T4: Real-time low-frequency principal components (PC) of the 850-hPa temperature in eastern Asia; ${t_{lcj}}$: Real-time low-frequency temperature over the LYR; F: Extended data matrix; ${t_0}$: Initial time; ${M_0}$: Length of the subsequence; TE1, TE2, $\cdots $, TE4: Respective T-EOFs (temporal empirical orthogonal functions) of the principal components of the 850-hPa temperature in eastern Asia on the 25–40-day time scale for the first fourth modes V1, V2, $\cdots $, V4 from 1 January 1979 to 31 December 2000; TEt: T-EOFs of the daily temperature over the LYR on the time scale of 25–40 days from 1 January 1979 to 31 December 2000

    图  7  2001/2002~2017/2018年12~2月长江下游温度低频分量1~30 d预报与观测的(a)相关系数、(b)标准化均方根误差。实线:ECAR模型;虚线:AR模型。图a、b中水平实线分别表示95%的信度水平和1$\sigma $$\sigma $表示标准差)

    Figure  7.  (a) Correlation coefficients and (b) RMSE (root-mean-square error) between the observation and the 1- to 30-day forecast for the low-frequency temperature component over the LYR in December–February of 2001/2002–2017/2018. Solid line: ECAR model; dashed line: AR model. The horizontal solid lines in Fig. a and Fig. b represent the 95% confidence level and 1 $\sigma $($\sigma $indicates standard deviation), respectively

    图  8  2001/2002~2017/2018年12~2月长江下游温度低频分量预测和实况之间相关系数的年际变化。绿、红、蓝、紫线分别表示11 d、14 d、17 d和20 d预报,水平虚线表示95%信度水平

    Figure  8.  Interannual variations of the correlation coefficient between the forecast and the observation of the low-frequency temperature component over the LYR in December–February during 2001/2002–2017/2018. The lead times are 11 days (green line), 14 days (red line), 17 days (blue line), and 20 days (purple line). The horizontal dashed lines represent the 95% confidence level

    图  9  (a)2002/2003年、(b)2007/2008年、(c)2010/2011年、(d)2015/2016年12~2月长江下游地区25~40 d温度低频分量的实况(实线)和ECAR模型的20 d预报(虚线)。柱状表示逐日气温距平(单位:°C),r是预测和实况之间的相关系数,预报的初始时间分别是11月11日、11月12日……、2月8日

    Figure  9.  Observation (solid lines) and forecast (dashed lines) of the ECAR model at a lead time of 20 days for the low-frequency temperature component over the LYR from December to February of (a) 2002/2003, (b) 2007/2008, (c) 2010/2011, and (d) 2015/2016. The bars represent the daily temperature anomalies (units: °C), r is the correlation coefficient between the forecast and the observation, the initial date of forecast is 11 November, 12 November,..., 8 February, respectively

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出版历程
  • 收稿日期:  2019-09-05
  • 录用日期:  2020-07-24
  • 网络出版日期:  2020-05-22
  • 刊出日期:  2021-01-19

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