地球科学进展

地球科学进展 ›› 2018, Vol. 33 ›› Issue (4): 385 -395. doi: 10.11867/j.issn.1001-8166.2018.04.0385

研究论文 上一篇    下一篇

长江下游夏季低频温度和高温天气的延伸期预报研究
杨秋明( )   
  1. 江苏省气象科学研究所,江苏 南京 210009
  • 收稿日期:2017-05-17 修回日期:2018-02-28 出版日期:2018-04-20
  • 基金资助:
    *国家自然科学基金项目“SCGT与夏季东亚ISO相互作用研究及其在长江下游强降水延伸期预报中的应用”(编号:41175082);江苏省气象科研基金面上项目“夏季长江下游地区低频降水和温度实时延伸期预报方法研究”(编号:KM201805)资助.

A Study of the Extended-range Forecast for the Low Frequency Temperature and High Temperature Weather over the Lower Reaches of Yangtze River Valley in Summer

Qiuming Yang( )   

  1. Jiangsu Meteorological Institute, Nanjing 210009, China
  • Received:2017-05-17 Revised:2018-02-28 Online:2018-04-20 Published:2018-05-24
  • About author:

    First author:Yang Qiuming(1963-), male, Changzhou City, Jiangsu Province, Professor. Research areas include weather and climate prediction.E-mail:yqm0305@263.net

  • Supported by:
    Project supported by the National Natural Science Foundation of China “Study on interaction between SCGT and ISO over East Asia in summer and its application to extended-range prediction of heavy precipitation over the lower reaches of Yangtze River valley” (No.41175082);The Scientific Research Foundation of Jiangsu Meteorological Bureau “Study on the real-time extended-range forecast method of low-frequency rainfall and temperature over the lower reaches of Yangtze River Valley in summer”(No.KM201805).
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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: 长江下游低频温度, 夏季, 高温天气, ECAR预报模型, 实时延伸期预报

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.

Key words: Low frequency temperature over the lower reaches of Yangtze River Valley, Summer, High temperature weather, Forecasting model of ECAR, Real-time extended-range weather forecast.

中图分类号: 

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
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
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 during the period from 1979 to 2011, in which values are multiplied by 100 and the significant levels of 95% are represented by shaded areas; (b) Spatial distribution of ratio of the variance for the 3060-day signal to the total seasonal variability, whose values (unit: %) are multiplied by 100 and the contours greater than or equal to 30 are shaded. Values are multiplied by 100
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 during the period from 1979 to 2011, in which values are multiplied by 100 and the significant levels of 95% are represented by shaded areas; (b) Spatial distribution of ratio of the variance for the 3060-day signal to the total seasonal variability, whose values (unit: %) are multiplied by 100 and the contours greater than or equal to 30 are shaded. Values are multiplied by 100
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 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
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
[1] Hoskins B.The potential for skill across the range of the seamless weather-climate prediction problem: A stimulus for our science[J]. Quarterly Journal of the Royal Meteorological Society,2013,139: 573-584.
doi: 10.1002/qj.1991     URL    
[2] Chou Jifan, Zheng Zhihai, Sun Shupeng.The think about 10-30 days extended range numerical weather prediction strategy facing the atmosphere chaos[J]. Scientia Meteorologica Sinica, 2010, 30(5):569-573.
[丑纪范,郑志海,孙树鹏.10-30 d延伸期数值天气预报的策略思考——直面混沌[J].气象科学,2010,30(5):569-573.]
doi: 10.3969/2012jms.00**     URL    
[3] Ding Yihui, Liang Ping.Extended range forecast based on MJO[J]. Meteorological Monthly, 2010,36(7):111-122.
[丁一汇,梁萍. 基于MJO的延伸预报[J].气象, 2010,36(7):111-122.]
doi: 10.7519/j.issn.1000-0526.2010.7.018     URL    
[4] He Jinhai, Liang Ping, Sun Guowu.Consideration on extended-range forecast and its application study[J]. Advances in Meteorological Science and Technology,2013,3(1):11-17.
[何金海,梁萍,孙国武.延伸期预报的思考及其应用研究进展[J].气象科技进展,2013,3(1):11-17.]
doi: 10.3969/j.issn.2095-1973.2013.01.002     URL    
[5] Kondrashov D, Chekroun M D, Robertson A W, et al. Low-order stochastic model and “past-noise forecasting” of the Madden-Julian Oscillation[J]. Geophysical Research Letters, 2013,40(19): 5 305-5 310.
doi: 10.1002/grl.50991     URL    
[6] Chen N, Majda A J, Giannakis D.Predicting the cloud patterns of the Madden-Julian Oscillation through a low-order nonlinear stochastic model[J]. Geophysical Research Letters, 2014,41(15):5 612-5 619.
doi: 10.1002/2014GL060876     URL    
[7] Chen N, Majda A J.Predicting the Real-Time Multivariate Madden-Julian Oscillation Index through a Low-Order Nonlinear Stochastic Model[J]. Monthly Weather Review, 2015,143:2 148-2 169.
doi: 10.1175/MWR-D-14-00378.1     URL    
[8] Wang L, Ting Mingfang, Chapman David, et al. Prediction of northern summer low-frequency circulation using a high-order vector auto-regressive model[J]. Climate Dynamics, 2016, 46: 693-709.DOI:10.1007/s00382-015-2607-0.
doi: 10.1007/s00382-015-2607-0     URL    
[9] Zhu Z, Li T.The statistical extended-range (10-30-day) forecast of summer rainfall anomalies over the entire China[J]. Climate Dynamics, 2016, 48(1/2): 209-224.
doi: 10.1007/s00382-016-3070-2     URL    
[10] Zhu Z, Li T.Statistical extended-range forecast of winter surface air temperature and extremely cold days over China[J]. Quarterly Journal of the Royal Meteorological Society, 2017, 143(704): 1 528-1 538.DOI: 10.1002/qj.3023.
doi: 10.1002/qj.3023     URL    
[11] Zhu Z, Li T.Extended-range forecasting of Chinese summer surface air temperature and heat waves[J]. Climate Dynamics, 2018, 50(516):2 007-2 021.
doi: 10.1007/s00382-017-3733-7     URL    
[12] Xia Z Y, Chen H B, Xu L S, et al. Extended range (1030 days) heavy rain forecasting study based on a nonlinear cross-prediction error model[J]. Advances in Atmospheric Science, 2015, 32(12): 1 583-1 591.
doi: 10.1007/s00376-015-4252-2     URL    
[13] Sze’kely E, Giannakis D, Majda A J.Extraction and predictability of coherent intraseasonal signals in infrared brightness temperature data[J]. Climate Dynamics,2016, 46(5):1 473-1 502.
doi: 10.1007/s00382-015-2658-2     URL    
[14] Sabeerali C T, Ajayamohan R S, Giannakis D, et al. Extraction and prediction of indices for monsoon intraseasonal oscillations: An approach based on nonlinear Laplacian spectral analysis[J]. Climate Dynamics,2017,49(9/10):3 031-3 050.
doi: 10.1007/s00382-016-3491-y     URL    
[15] Zheng Zhihai, Feng Guolin, Huang Jianping, et al. Predictability-based extended-range ensemble prediction method and numerical experiments[J]. Acta Physica Sinica, 2012,61(19),199-203.
[郑志海,封国林,黄建平,等.基于延伸期可预报性的集合预报方法和数值试验[J]. 物理学报,2012,61(19): 199-203.]
[16] Yu H, Huang J, Li W, et al. Development of the analogue-dynamical method for error correction of numerical forecasts[J]. Journal of Meteorological Research, 2014, 28(5): 934-947. DOI: 10.1007/s13351-014-4077-4.
doi: 10.1007/s13351-014-4077-4     URL    
[17] Yu H, Huang J, Chou J.Improvement of medium-range forecasts using the analogue-dynamical method[J]. Monthly Weather Review, 2014, 142(4): 1 570-1 587.
doi: 10.1175/MWR-D-13-00250.1     URL    
[18] Hu S, Qiu C, Zhang L, et al. An approach to estimating and extrapolating model error based on inverse problem methods: Towards accurate numerical weather prediction[J]. Chinese Physics B, 2014, 23(8). DOI: 10.1088/1674-1056/23/8/089201.
[19] Huang Qican, Hu Shujuan, Qiu Chunyu, et al. Numerical model error estiamtion by derivative-free optimization method[J]. Acta Physical Sinica, 2014, 63(14): 149203.
[黄启灿,胡淑娟,邱春雨,等.基于无导数优化方法的数值模式误差估计[J]. 物理学报, 2014, 63(14): 149203.]
doi: 10.7498/aps.63.149203     URL    
[20] Wang Xujia, Zheng Zhihai, Feng Guolin, et al. The role of boundary and initial conditions for extended range forecast[J]. Acta Meteorologica Sinica,2017, 75(1):111-122.
[汪栩加,郑志海,封国林,等.延伸期预报中的大气初值与海温边值相对作用[J].气象学报,2017, 75(1):111-122.]
doi: 10.11676/qxxb2017.007     URL    
[21] Chen Guanjun, Wei Fengying, Yao Wenqing, et al. Extended range forecast experiments of persistent winter low temperature indexes based on intra-seasonal oscillation over southern China[J]. Acta Meteorologica Sinica, 2017,75(3): 400-414.
[陈官军, 魏凤英, 姚文清, 等. 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]. 气象学报,2017, 75(3): 400-414.]
doi: 10.11676/qxxb2017.024     URL    
[22] Neena J M, Lee J Y, Waliser D E, et al. Predictability of the Madden-Julian Oscillation in the intraseasonal variability hindcast experiment (ISVHE)[J]. Journal of Climate, 2014,27:4 531-4 543.
doi: 10.1175/JCLI-D-13-00624.1     URL    
[23] Lee S S, Wang Bin, Waliser D E, et al. Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment[J]. Climate Dynamics, 2015,45(7/8): 2 123-2 135.
doi: 10.1007/s00382-014-2461-5     URL    
[24] Yang Qiuming.Prospects and progresses in the research of the methods for 10-30 days extended-range weather forecast[J]. Advances Earth Science, 2015, 30(9): 970-984.
[杨秋明. 10-30 d延伸期天气预报方法研究进展与展望[J]. 地球科学进展,2015,30(9):970-984.]
doi: 10.-11867/j.issn.10018166.2015.09.0970    
[25] Yang Qiuming.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]. Acta Meteorologica Sinica, 2014, 72(3): 494-507.
[杨秋明. 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].气象学报, 2014, 72(3): 494-507.]
doi: 10.11676/qxxb2014.028     URL    
[26] Yang Qiuming.Extended complex autoregressive model of low frequency rainfalls over the lower reaches of Yangtze River Valley for extended-range forecast in 2013[J]. Acta Physica Sinica,2014, 63(19):199202. DOI: 10.7498/aps.63.199202.
[杨秋明. 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].物理学报,2014,63(19):199202. DOI: 10.7498/aps.63.199202.]
doi: 10.7498/aps.63.199202    
[27] Yang Qiuming.Predictability and prediction of low frequency rainfall over the lower reaches of the Yangtze River valley on the time scale of 2030 days[J]. Journal of Geophysical Research, 2018,123:211-233. DOI:10.1002/2017JD027281.
doi: 10.1002/2017JD027281     URL    
[28] Mo K C.Adaptive filtering and prediction of intraseasonal oscillations[J]. Monthly Weather Review, 2001,129: 802-817.
doi: 10.1175/1520-0493(2001)1292.0.CO;2     URL    
[29] Yang Qiuming.The 20-30-day oscillation of the global circulation and heavy precipitation over the lower reaches of the Yangtze River Valley[J]. Science in China (Series D), 2009, 52(10):1 485-1 501.
[杨秋明. 全球环流20-30 d 振荡与长江下游强降水[J].中国科学:D辑,2009,39(11):1 515-1 529.]
[30] Yang Qiuming, Song Juan, Li Yi, et al. Review of impacts of the global atmospheric intraseasonal oscillation on the continuous heavy rainfall over the Yangtze River Valley[J]. Advance in Earth Science, 2012, 27(8):876-884.
[杨秋明,宋娟,李熠,等.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].地球科学进展,2012,27(8):876-884.]
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