Vol.31, NO.5, 2020
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Liu Boqi, Zhu Congwen. Potential skill map of predictors applied to the seasonal forecast of summer rainfall in China. J Appl Meteor Sci, 2020, 31(5): 570-582. DOI:  10.11898/1001-7313.20200505.
Citation: Liu Boqi, Zhu Congwen. Potential skill map of predictors applied to the seasonal forecast of summer rainfall in China. J Appl Meteor Sci, 2020, 31(5): 570-582. DOI:  10.11898/1001-7313.20200505.
shu

Potential Skill Map of Predictors Applied to the Seasonal Forecast of Summer Rainfall in China

DOI: 10.11898/1001-7313.20200505

  • Chinese Academy of Meteorological Sciences, Beijing 100081

  • Received Date: 2020-04-25
  • Rev Recd Date: 2020-06-03
  • Publish Date: 2020-09-30
    Abstract
  • Anomalous summer rainfall in China is affected by many factors, whose complex interaction restricts the predictability of Chinese summer rainfall (CSR). The predicting skill of the state-of-the-art dynamic models on the CSR is still limited, leaving challenges in developing objective statistical predicting methods. A method for searching potential predicting skill of predictors (i.e., potential skill map, PSM) is proposed, which can be used to select predictors automatically based on the PSM, and a new automatic statistical prediction model of the CSR is established.Compared with traditional linear correlation analysis, the PSM using the cross-validation concept not only reflects the potential predicting skills of predictors on predictands, but is free from effects of extreme events. It is completely based on real-time statistical predicting procedure, which aims to find sufficient conditions for predictands in logical. The PSM is an important supplement to the traditional correlation coefficient map. They work together to provide potential predictors with necessary and sufficient conditions. The predictor automatic selector takes advantage of the idea of ensemble forecasting. It selects predictors with the most significant potential forecasting skill from the PSM, and then generates final forecast products by averaging a large number of predicting members. The year-by-year automatic selection of the predicators is thus realized. This solution doesn't rely on subjective experiences of foreasters, and also provides a new way to further investigate the predictability of the interannual variability of the East Asian summer monsoon. This new automatic statistical prediction model of the CSR based on the PSM and the predictor automatic selector shows a high reforecast skill for the CSR. In the 21-year reforecasting experiment from 1999 to 2019, predictors in the previous autumn and winter seasons are used to predict the CSR. Results show an average symbol agreement rate of 60% and the mean anomaly correlation coefficient of 0.436 between the reforecast and the observed CSR. As to the predicting skill (PS) score in the National Climate Center, the reforecast CSR reaches 71.00 in average. After variance correcting, the PS score further increases to 82.10, which is much higher than predicting skills of current dynamical models. It is noteworthy that the reforecast experiment in the present uses the first 12 multiple regression coefficients and EOF modes of the CSM, of which the first 4 multiple regression coefficients and EOF modes play a dominant role in the overall distribution of the CSM. By contrast, higher-order modes could further improve the reforecast skill by increasing the diversity of the reforecasting CSM, which represent their potential physical implications.
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  • Fig. 1  Taylor diagram of the reconstructed rainfall anomaly field based on EOF modes and multiple regression coefficients referring to the observation during 1981-2019

    (dots in different colors denote reconstructed results using different numbers of EOF modes and principle components)

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    Fig. 2  Correlation coefficient of reconstructed rainfall anomaly using the first 12 EOF modes and multiple regression coefficients to observed rainfall anomaly at each station during 1981-2019

    (areas with significance exceeding 0.05 and 0.01 levels are slashed and stippled, respectively)

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    Fig. 3  Anomaly correlation coefficient of reconstructed rainfall anomaly using the first 12 EOF modes and multiple regression coefficients to observed rainfall anomaly during 1981-2019

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    Fig. 4  Spatial distribution of temporal correlation coefficient between the second regression coefficient(R2) and the predictors in different seasons during 1999-2019

    (the stippled denotes passing the test of 0.05 level, predictors include the rainfall in 30°S-30°N and 200 hPa geopotential height in the mid-high latitude)

    DownLoad: Full-Size Img PowerPoint

    Fig. 5  Potential skill map of the second regression coefficient(R2) referring to the predictors in different seasons during 1999-2019

    (the stippled denotes passing the test of 0.05 level, predictors include rainfall in 30°S-30°N and 200 hPa geopotential height in the mid-high latitude)

    DownLoad: Full-Size Img PowerPoint

    Fig. 6  Heat map of predictors of the first 4 multiple regression coefficients during 1999-2019 obtained by the predictor automatic selection scheme

    (predictors include the rainfall in 30°S-30°N and 200 hPa geopotential height in the mid-high latitude)

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    Fig. 7  Reforecast test of the first 4 multiple regression coefficients during 1999-2019

    (the shaded denotes range of regression coefficients)

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    Fig. 8  Correlation coefficient of reforecast anomalous rainfall using the first 12 multiple regression coefficients and EOF modes to observed rainfall anomaly

    (the stippled denotes passing the test of 0.1 level)

    DownLoad: Full-Size Img PowerPoint

    Fig. 9  Reforecast test of Chinese summer rainfall anomaly during 1999-2019 using new predicting method

    (a)the same sign rate between reforecast and observation using the first 12 multiple regression coefficients and EOF modes, (b)anomaly correlation coefficients between observation and reforecast based on different numbers of multiple regression coefficients(m, ranging from 1 to 12, indicates the reforecast generated by different numbers of multiple regression coefficients and EOF modes)

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    Fig. 10  Mean value of anomaly correlation coefficients between observation and reforecast based on different numbers of multiple regression coefficients during 1999-2019

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    Fig. 11  Response PS score curve of the reforecast percentage of rainfall anomaly to the variance correction parameter(a) and PS score before and after variance corrected(b)

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    • Received : 2020-04-25
    • Accepted : 2020-06-03
    • Published : 2020-09-30
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