Vol.29, NO.2, 2018
Turn off MathJax
Article Contents
Article Navigation >  Journal of Applied Meteorological Science  > 2018  >  29(2): 217-231
Li Chunhui, Pan Weijuan, Wang Ting. A multi-scale spatial-temporal projection method for monthly and seasonal rainfall prediction in Guangdong. J Appl Meteor Sci, 2018, 29(2): 217-231. DOI:  10.11898/1001-7313.20180208.
Citation: Li Chunhui, Pan Weijuan, Wang Ting. A multi-scale spatial-temporal projection method for monthly and seasonal rainfall prediction in Guangdong. J Appl Meteor Sci, 2018, 29(2): 217-231. DOI:  10.11898/1001-7313.20180208.
shu

A Multi-scale Spatial-temporal Projection Method for Monthly and Seasonal Rainfall Prediction in Guangdong

DOI: 10.11898/1001-7313.20180208
  • 1.

    Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction, China Meteorological Administration, Guangzhou 510080

  • 2.

    Guangzhou Climate Center of Guangdong Province, Guangzhou 510080

  • Received Date: 2017-05-08
  • Rev Recd Date: 2017-11-28
  • Publish Date: 2018-03-31
    Abstract
  • Guangdong Province is located in low latitude areas and affected by both tropical weather systems and high latitude weather systems.A multi-scale spatial-temporal projection (MSTP) method is developed to predict the monthly and seasonal precipitation in Guangdong.Multi-factor and multi-scale forecasting method can be used to seek for the forecast factor by quantity scale separation through scale decomposition conforming to the physical meaning, thus it can reduce climate non-stationary time series and improve the prediction accuracy.The key feature of MSTP mothod is that it considers not only spatially but also temporally varying large-scale field connection between the predictor and predictand.Based on main modes of the empirical orthogonal function (EOF) analysis, periods are gained from the wavelet analysis and decomposed by Lanczos filtering.According to the correlation between the precipitation and Climate Forecast Systems datasets provided by National Centers for Environmental Prediction dynamic model data (CFSv2), significant influencing factors are selected to predict precipitation with MSTP method.Using the least square error correction method, Guangdong monthly and seasonal precipitation predictions are obtained based on inter-annual increment approach.The test of independent samples from 2006 to 2015 shows the correction can improve the performance of prediction, making PS score of operational test change smoothly.After correction PS scores are improved greatly during 6 years of the hindcast period, and the monthly and seasonal rainfall account for 68.8%.For 87.5% of total samples, the forecast average score is over 70.The prediction effect is closely related to period changes of the precipitation main mode.If the inter-annual period is priority to other period, the prediction effect after correction is significantly higher than that before the correction, otherwise it is poor.The root mean square error within 0.5-1 standard deviation rate after correction is higher than that before corrections.Within 0.5 standard deviations, the monthly and seasonal rainfalls, of which the root mean square error of probability is more than 40%, account for 81.3% after correction comparing to 31.3% before correction.Within 1 standard deviation, the monthly and seasonal rainfalls, of which the root mean square error of probability is more than 70%, account for 56.3% comparing to 50% before correction.It suggests that most of rainfall prediction errors are within 1 standard deviation.Therefore, the prediction from MSTP method can offer important reference to operational prediction of short-term climate prediction for monthly and seasonal prediction in Guangdong.
    • FullText(HTML)
    References(37)

  • Fig. 1  The major steps of multi-scale spatial-temporal projection(MSTP) method

    DownLoad: Full-Size Img PowerPoint

    Fig. 2  Correlations to the interannual and interdecadal time serious of the first EOF mode to sea surface temperature and meteorological elements of CFSv2 dataset

    (dark and shallow shaded areas indicate positive and negative coefficients exceeding the test of 0.1 level)

    DownLoad: Full-Size Img PowerPoint

    Fig. 3  PS score of MSTP and multi-scale method of monthly and seasonal rainfall before(the bar) and after(the solid line) correction in Guangdong

    DownLoad: Full-Size Img PowerPoint

    Fig. 4  PS score of MSTP method of seasonal rainfall before(the bar) and after(the solid line) correction in Guangdong

    DownLoad: Full-Size Img PowerPoint

    Fig. 5  Distributions of the probability density(the bar) and probability(the solid line) of standard root mean square error between prediction and observation of monthly rainfall after correction in Guangdong

    DownLoad: Full-Size Img PowerPoint

    Fig. 6  Distributions of the probability density(the bar) and probability(the solid line) of standard root mean square error between prediction and observation of seasonal rainfall after correction in Guangdong

    DownLoad: Full-Size Img PowerPoint

    Fig. 7  Distributions of the probability density(the bar) and probability(the solid line) of the standard root mean square error between prediction and observation of monthly rainfall after correction in Guangdong

    DownLoad: Full-Size Img PowerPoint

    Fig. 8  Distributions of the probability density(the bar) and probability(the solid line) of the standard root mean square error between prediction and observation of seasonal rainfall after correction in Guangdong

    DownLoad: Full-Size Img PowerPoint

    Table  1  Ten-year averaged PS score of monthly and seasonal rainfall

    月和季节 无尺度分离 有尺度分离
    订正前 订正后 订正前 订正后
    1月 53.0 71.9 59.3 73.0
    2月 45.6 85.5 50.0 90.1
    3月 59.6 72.6 61.1 72.7
    4月 71.1 70.7 65.5 78.4
    5月 64.1 77.8 61.2 78.8
    6月 56.0 68.1 71.2 58.3
    7月 76.1 74.7 60.1 76.4
    8月 63.9 72.1 64.8 70.0
    9月 71.7 75.9 72.7 73.7
    10月 62.0 69.6 68.4 75.8
    11月 64.7 71.9 66.7 69.4
    12月 68.8 64.1 79.4 74.5
    春季 65.2 74.1 71.6 73.6
    夏季 68.2 74.8 72.4 72.8
    秋季 67.9 72.9 62.7 76.1
    冬季 52.4 80.0 44.5 77.1
    DownLoad: Download CSV

    Table  2  The probability(p) of standard root mean square error between prediction and observation before and after correction(unit:%)

    月和季节 -0.5 < p≤0.5 -1 < p≤1
    订正前 订正后 订正前 订正后
    1月 48 64 79 80
    2月 41 42 67 68
    3月 34 43 64 66
    4月 40 46 74 78
    5月 31 41 59 67
    6月 27 36 73 65
    7月 33 43 69 71
    8月 34 40 72 74
    9月 31 38 62 66
    10月 36 48 74 77
    11月 40 43 75 79
    12月 38 39 65 67
    春季 34 49 72 73
    夏季 29 31 65 66
    秋季 33 41 69 71
    冬季 87 99 99 99
    DownLoad: Download CSV
  • [1]
    贾小龙, 陈丽娟, 高辉, 等.我国短期气候预测技术进展.应用气象学报, 2013, 24(6):641-655. doi:  10.11898/1001-7313.20130601
    [2]
    宋连春, 肖风劲, 李威.我国现代气候业务现状及未来发展趋势.应用气象学报, 2013, 24(5):513-520. doi:  10.11898/1001-7313.20130501
    [3]
    芦新平, 陈星, 苗曼倩, 等.降尺度方法中的初始资料处理的研究.气象科学, 2002, 22(2):139-148. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=qxkx200202002
    [4]
    李维京, 陈丽娟.动力延伸预报产品释用方法的研究.气象学报, 1999, 57(3):338-345. doi:  10.11676/qxxb1999.032
    [5]
    陈丽娟, 李维京, 张培群, 等.降尺度技术在月降水预报中的应用.应用气象学报, 2003, 14(6):648-655. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20030682&flag=1
    [6]
    范丽军, 符淙斌, 陈德亮.统计降尺度法对华北地区未来区域气温变化情景的预估.大气科学, 2007, 31(5):887-897. http://rcg.gvc.gu.se/dc/PUBs/fanljetal07.pdf
    [7]
    丁裕国, 刘吉峰, 张耀存.基于概率加权估计的中国极端气温时空分布模拟试验.大气科学, 2004, 28(5):771-782. http://www.oalib.com/paper/4878938
    [8]
    Yuan X, Liang X Z.Improving cold season precipitation prediction by the nested CWRF-CFS system.Geophys Res Lett, 2011, 38:L02706. http://adsabs.harvard.edu/abs/2011GeoRL..38.2706Y
    [9]
    Gao H, Yang S, Kumar A, et al.Variations of the East Asian Meiyu and simulation and prediction by the NCEP Climate Forecast System.J Climate, 2011, 24(1):94-108. doi:  10.1175/2010JCLI3540.1
    [10]
    李春晖, 林爱兰, 谷德军, 等.基于CFS预报产品的广东省季节降水统计降尺度预测.热带气象学报, 2012, 28(6):797-808. http://www.cqvip.com/QK/92292A/201206/44479221.html
    [11]
    刘颖, 范可, 张颖.基于CFS模式的中国站点夏季降水统计降尺度预测.大气科学, 2013, 37(6):1287-1296. doi:  10.3878/j.issn.1006-9895.2012.12143
    [12]
    Saha S, Moorthi S, Wu X, et al.The NCEP climate forecast system version 2.J Climate, 2014, 27(6):2185-2208. doi:  10.1175/JCLI-D-12-00823.1
    [13]
    谷德军, 纪忠萍, 李春晖.南海夏季风爆发日期与海温的多尺度关系及最优子集回归预测.海洋学报, 2011, 33(6):55-63. http://d.wanfangdata.com.cn/Periodical_hyxb201106006.aspx
    [14]
    Trenberth K E.Signal versus noise in the Southern Oscillation.Mon Wea Rev, 1984, 112(2):326-332. doi:  10.1175/1520-0493(1984)112<0326:SVNITS>2.0.CO;2
    [15]
    Kug J S, Lee J Y, Kang I S.Global sea surface temperature prediction using a multi-model ensemble.Mon Wea Rev, 2007, 135:3239-3247. doi:  10.1175/MWR3458.1
    [16]
    Chen J M, Li T, Shih J.Asymmetry of the El Nio-spring rainfall relationship in Taiwan.J Meteor Soc Japan, 2008, 86:297-312. doi:  10.2151/jmsj.86.297
    [17]
    Hsu Pang-chi, Li T, Lin Y C, et al.A spatial-temporal projection method for seasonal prediction of spring rainfall in Northern Taiwan.Journal of the Meteorological Society of Japan, 2012, 90(2):179-190. doi:  10.2151/jmsj.2012-202
    [18]
    Zhu Z W, Li Tim, Hsu Pang-chi, et al.A spatial-temporal projection model for extended-range forecast in the tropics.Clim Dyn, 2014, DOI: 10.1007/s00382-014-2353-8.
    [19]
    Wang H J, Zhou G Q, Zhao Y.An effective method for correcting the seasonal-interannual prediction of summer climate anomaly.Adv Atmos Sci, 2000, 17(2):234-240. doi:  10.1007/s00376-000-0006-9
    [20]
    王会军, 周广庆, 赵彦.降水和大气环流距平年际预测的一个高效的修正方案.应用气象学报, 2000, 11(增刊Ⅰ):40-50. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yyqxxb2000Z1005
    [21]
    Chang C P, Zhang Y S, Li T.Interannual and interdecadal variations of the East Asian summer monsoon and tropical Pacific SSTs.Part Ⅰ:Roles of the subtropical ridge.J Climate, 2000, 13, 4310-4325. doi:  10.1175/1520-0442(2000)013<4310:IAIVOT>2.0.CO;2
    [22]
    Wang B, Wu R, Lau K M.Interannual variability of the Asian summer monsoon:Contrasts between the Indian and the western North Pacific-East Asian monsoon.J Climate, 2001, 14:4073-4090. doi:  10.1175/1520-0442(2001)014<4073:IVOTAS>2.0.CO;2
    [23]
    Wu B, Zhou T J, Li T.Contrast of rainfall-SST relationships in the western North Pacific between the ENSO developing and decaying summers.J Climate, 2009, 22:4398-4405. doi:  10.1175/2009JCLI2648.1
    [24]
    Lau N C, Nath M J.Impact of ENSO on the bariability of the Asian-Australian monsoons as simulated in GCM experiments.J Climate, 2000, 13:4287-4309. doi:  10.1175/1520-0442(2000)013<4287:IOEOTV>2.0.CO;2
    [25]
    Lau N C, Nath M J.Atmosphere-ocean variations in the Indo-Pacific sector during ENSO episodes.J Climate, 2003, 16:3-20. doi:  10.1175/1520-0442(2003)016<0003:AOVITI>2.0.CO;2
    [26]
    Chen J M, Li T, Shih J.Asymmetry of the El Nio-spring rainfall relationship in Taiwan.J Meteor Soc Japan, 2008, 86:297-312. doi:  10.2151/jmsj.86.297
    [27]
    Gong D Y, Ho C H.Arctic oscillation signals in the East Asian summer monsoon.J Geophys Res, 2003, 108(D2), 4066, DOI: 10.1029/2002JD002193.
    [28]
    Hung C W, Hsu H H, Lu M M.Decadal oscillation of spring rain in northern Taiwan.Geophys Res Lett, 2004, 31, L22206, DOI: 10.1029/2004GL021344.
    [29]
    Yang S, Lau K M, Yoo S H, et al.Upstream subtropical signals in preceding the Asian summer monsoon circulation.J Climate, 2004, 17:4213-4229. doi:  10.1175/JCLI3192.1
    [30]
    温之平, 吴乃庚, 冯业荣, 等.定量诊断华南春旱的形成机理.大气科学, 2007, 31(6):1223-1236. http://d.wanfangdata.com.cn/Periodical_daqikx200706018.aspx
    [31]
    郑菲, 李建平.前冬南半球环状模对春季华南降水的影响及其机理.地球物理学报, 2012, 55(11):3542-3557, DOI: 10.6038/j.issn.0001-5733.2012.11.004.
    [32]
    李宏毅, 林朝晖, 宋燕, 等.华南春季降水纬向非均匀分布及异常年大气环流特征分析.气象, 2013, 39(12):1616-1625. doi:  10.7519/j.issn.1000-0526.2013.12.010
    [33]
    李宏毅, 林朝晖, 陈红.我国华南3月降水年代际变化的特征.气候与环境研究, 2010, 15(3):311-321. http://d.wanfangdata.com.cn/Periodical_qhyhjyj201003011.aspx
    [34]
    李宏毅, 林朝晖, 陈红.我国华南4, 5月份降水年代际变化的特征及其与中西太平洋海温的可能关系.气候与环境研究, 2012, 17(4):481-494. doi:  10.3878/j.issn.1006-9585.2011.11040
    [35]
    梁建茵.6月西太平洋副高脊线的年际变化及其对华南降水的影响.热带气象学报, 1994, 10(3):274-279. http://www.oalib.com/paper/5075326
    [36]
    陶诗言, 张庆云.亚洲冬夏季风对ENSO事件的响应.大气科学, 1998, 22(4):399-407. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK804.001.htm
    [37]
    纪忠萍, 温晶, 方一川, 等.近50年广东冬半年降水的变化及连旱成因.热带气象学报, 2009, 25(1):29-36. http://www.oalib.com/paper/5074773
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中
  • -->

Catalog

    Figures(8)  / Tables(2)

    Get Citation
    PDF
    XML
    Article views (2527) PDF downloads(270) Cited by()
    • Received : 2017-05-08
    • Accepted : 2017-11-28
    • Published : 2018-03-31
    Journal Online
    Contact

    © 2020 Journal of Applied Meteorological Science   京ICP备15006967号-1

    Supported by Beijing Renhe Information Technology Co. Ltd

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return