考虑不同背景场低频降水的延伸期预测

doi:10.3969/j.issn.1673-503X.2018.05.008

气象与环境学报 ›› 2018, Vol. 34 ›› Issue (5): 57-65.doi: 10.3969/j.issn.1673-503X.2018.05.008

考虑不同背景场低频降水的延伸期预测

曲金华^1,2, 王一舒³, 谭桂容³

1. 吉林省气象局科技与预报处, 吉林长春 130062;
2. 长白山气象与气候变化吉林省重点实验室, 吉林长春 130062;
3. 南京信息工程大学气象灾害教育部重点实验室, 江苏南京 210044

收稿日期:2018-05-23 修回日期:2018-07-06 出版日期:2018-10-31 发布日期:2018-10-31
作者简介:曲金华,女,1969年生,高级工程师,主要从事短期气候异常及其预测研究,E-mail:759598574@qq.com
基金资助:
国家自然科学基金（41630424）和国家科技支撑计划子项目（2015BAC03B02）共同资助。

Extended range forecasting of low-frequency precipitation under different background circulations

QU Jin-hua^1,2, WANG Yi-shu³, TAN Gui-rong³

1. Department of Science and Technology and Forecast of Jilin Meteorological Service, Changchun 130062, China;
2. Jilin Provincial Key Laboratory of Changbai Mountain Meteorology & Climate Change, Changchun 130062, China;
3. Key Laboratory of Meteorological Disaster, Ministry of Education(KLME), Joint International Research Laboratory of Climate and Environment Change(ILCEC), Nanjing 210044, China

Received:2018-05-23 Revised:2018-07-06 Online:2018-10-31 Published:2018-10-31

摘要/Abstract

摘要： 基于中国国家气象观测站的逐日降水资料、NCEP逐日全球再分析资料和NOAA逐日向外长波辐射资料，选取与10—30 d低频降水相关显著的热带、中高纬环流作为影响因子，针对1979—2013年江南4—6月延伸期低频降水，依照不同背景场下低频降水与影响因子之间的相关性，进行了预测试验。结果表明：江南4—6月降水以10—30 d的低频周期最为显著。印度洋、印尼附近的热带对流和欧亚高纬度地区的大气环流共同影响着我国江南4—6月低频降水，可作为延伸期降水的预测因子。当欧洲及西西伯利亚地区位势高度出现负距平、北美及贝湖以西附近位势高度正距平，且热带对流异常偏弱时，对应江南低频降水异常偏少，异常中心主要位于长江中下游地区；30 d以上的大尺度500 hPa低频位势高度场主要表现为3种空间分布型，根据这3种分布型可将逐日降水个例的大尺度背景场划分为3类，每种背景场下低频降水与热带、中高纬度环流因子在前期30 d内的相关特征均不同；30 d以上时间尺度的500 hPa低频环流可为10—30 d延伸期变化提供相对稳定的大尺度背景场，不同背景场下区域低频降水与相应低频环流之间的关系演变不同。考虑不同背景，其相关性增强，且显著相关超前的时间更长。

关键词: 非滤波法, 热带-中高纬度共同作用, 低频背景场, 降水分型预测

Abstract: Based on daily precipitation observations from the national meteorological observatories,daily outgoing longwave radiation data from NOAA,and reanalysis data from the NCEP (National Centers for Environmental Prediction),an extended range forecasting for the low-frequency precipitation over south of the Yangtze River during April,May,and June (AMJ) from 1979 to 2013 was carried out under different background circulations.The atmospheric circulations over tropic or mid-high latitude,which are significantly correlated with the 10-30 d low-frequency precipitation,were selected as the influencing factors.The results show that the 10-30 d component of the AMJ precipitation over south of the Yangtze River is the most significant.The precipitation anomalies are significantly related to the tropical convection locates over the eastern Indian Ocean and Indonesia,as well as the atmospheric circulation anomalies over the high latitude of Europe and Siberia.They can be selected as the predictors for the extended range precipitation.When the geopotential height anomalies over the high latitude of Europe and Siberia areas appear to be suppressive,while over North America and west to the Lake Baikal appears to be active,moreover,the tropical convective appears to be suppressive,the rainfall over south of the Yangtze River turns scarce.The center of the rainfall anomaly locates in the middle and lower reaches of the Yangtze River.Based on the spatial distributions of geopotential height anomalies at the period longer than 30 days at 500 hPa,the low-frequency background of daily precipitation is divided into three categories.The correlation in -30 lag days between low-frequency precipitation and predictors exhibits their individual features in each background.The 500 hPa low-frequency circulation at the period longer than 30 days provides the stable background state for the 10-30 d extended range variations.The correlation between low-frequency precipitation and predictors turns out to be more significant and the lag days of correlation becomes longer,considering different backgrounds.

Key words: Non-filtering method, Extended range forecasting, Low-frequency background field, Forecasting of precipitation distribution

中图分类号:

P456

曲金华, 王一舒, 谭桂容. 考虑不同背景场低频降水的延伸期预测[J]. 气象与环境学报, 2018, 34(5): 57-65.

QU Jin-hua, WANG Yi-shu, TAN Gui-rong. Extended range forecasting of low-frequency precipitation under different background circulations[J]. Journal of Meteorology and Environment, 2018, 34(5): 57-65.

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参考文献

[1] 黄世成,杨秋明,李熠.基于时变关键区和时滞多变量的长江下游10~30 d低频降水实时预报[J].科学技术与工程,2014,14(22):1-6.
[2] 马浩,毛燕军,雷媛,等.10-30 d延伸期天气预报研究进展综述[J].干旱气象,2012,30(4):514-521.
[3] 周德平,陈力强,李辑.动力延伸预报产品在辽宁短期气候预测中的释用[J].气象与环境学报2006,22(6):11-15.
[4] 金荣花,马杰,毕宝贵.10-30 d延伸期预报研究进展和业务现状[J].沙漠与绿洲气象,2010,4(2):1-5.
[5] 丑纪范.短期气候预测的现状问题与出路(一)[J].沙漠与绿洲气象,2003,1(1):1-4.
[6] 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.
[7] 杨玮,何金海,孙国武,等.低频环流系统的一种统计预报方法[J].气象与环境学报,2011,27(3):1-5.
[8] Hsu P-C,Li T,You L,et al.A spatial-temporal projection model for 10-30day rainfall forecast in South China[J].Climate Dynamics,2015,44(5-6):1227-1244.
[9] 李恺心,姜晓艳模糊综合评价在汛期(6-8月)降水趋势预报中的应用[J].气象与环境学报,1990,6(2):13-15.
[10] 梁萍,丁一汇.基于季节内振荡的延伸预报试验[J].大气科学,2012,36(1):102-116.
[11] 孙国武,李震坤,信飞,等.延伸期天气过程预报的一种新方法-低频天气图[J].大气科学,2013,37(4):945-954.
[12] Borah N,Sahai A K,Chattopadhyay R,et al.A self-organizing map-based ensemble forecast system for extended range prediction of active/break cycles of Indian summer monsoon[J].Journal of Geophysical Research Atmospheres,2013,118(16):9022-9034.
[13] 杨秋明.基于20-30 d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.
[14] 杨秋明.2013年初夏长江下游降水低频分量延伸期预报的多变量时滞回归模型[J].气象,2015,41(7):881-889.
[15] Wheeler MC,Hendon HH.An all-season real-time multivariate MJO index:development of an index for monitoring and prediction[J].Monthly Weather Review,2004,132(8):1917-1932.
[16] Mo KC.Adaptive filtering and prediction of intraseasonal oscillations[J].Monthly Weather Review,2001,129(4):802-817.
[17] Love BS,Matthews A.Real-time localised forecasting of the Madden-Julian Oscillation using neural network models[J].Quarterly Journal of the Royal Meteorological Society,2009,135(643):1471-1483.
[18] Jones C,Carvalho LMV,Higgins W,et al.A statistical forecast model of tropical intraseasonal convective anomalies[J].Journal of Climate,2004,17(11):2078-2095.
[19] Maharaj EA,Wheeler MC.Forecasting an index of the Madden-oscillation[J].International Journal of Climatology,2005,25(12):1611-1618.
[20] Jiang XA,Waliser DE,Wheeler MC,et al.Assessing the skill of an all-season statistical forecast model for the Madden-Julian Oscillation[J].Monthly Weather Review,2008,136(6):1940-1956.
[21] Seo K-H,Wang WQ,Gottschalck J,et al.Evaluation of MJO forecast skill from several statistical and dynamical forecast models[J].Journal of Climate,2009,22(9):2372-2388.
[22] Kondrashov D,Chekroun MD,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):5305-5310.
[23] Cavanaugh N R,Allen T,Subramanian A,et al.The skill of atmospheric linear inverse models in hindcasting the Madden-Julian Oscillation[J].Climate Dynamics,2014,44(3-4):897-906.
[24] 丁一汇,梁萍.基于MJO的延伸预报[J].气象,2010,36(7):111-122.
[25] 黄海燕,何金海,朱志伟.大气季节内振荡的研究进展及其在延伸期预报中的应用[J].气象与减灾研究,2011,34(3):1-8.
[26] 陈官军,魏凤英.基于低频振荡特征的夏季江淮持续性降水延伸期预报方法[J].大气科学,2012,36(3):633-644.
[27] 万日金,王同美,吴国雄.江南春雨和南海副热带高压的时间演变及其与东亚夏季风环流和降水的关系[J].气象学报,2008,66(5):800-807.
[28] 刘宣飞,袁旭.江南春雨的两个阶段及其降水性质[J].热带气象学报,2013,29(1):99-105.
[29] 谭桂容,王一舒.中高纬度与热带大气的共同作用对江南4-6月低频降水的影响[J].气象学报,2016,74(3):335-351.
[30] Hsu P-C,Li T,Lin Y-C,et al.A spatial-temporal projection method for seasonal prediction of spring rainfall in northern Taiwan[J].Journal of the Meteorological Society of Japan,2012,90(2):179-190.

考虑不同背景场低频降水的延伸期预测

Extended range forecasting of low-frequency precipitation under different background circulations

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