Vol.29, NO.2, 2018
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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.
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  • Fig. 1  The major steps of multi-scale spatial-temporal projection(MSTP) method

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    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)

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    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

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

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    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

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    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

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    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

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    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

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    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
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    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
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    • Received : 2017-05-08
    • Accepted : 2017-11-28
    • Published : 2018-03-31
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