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Potential Skill Map of Predictors Applied to the Seasonal Forecast of Summer Rainfall in China
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摘要: 影响我国夏季汛期降水异常的因子繁多，不同因子之间复杂的相互作用制约我国夏季降水季节预测水平。目前动力模式对降水预测技巧水平较低，如何开发客观统计预报方法，提高我国夏季降水预报技巧依然存在挑战。该文基于最小二乘法拟合和交叉检验方法，提出一种搜索预测因子潜在预测技巧的方法（潜在技巧分布图），并基于该方法开发预测因子自动选择器，建立中国夏季降水异常自动统计预测模型。与传统线性相关分析相比，潜在技巧分布图不受极端气候事件影响，可直观展现具有显著预测技巧的前兆信号，而预测因子自动选择器则能从潜在技巧分布图中自动筛选最优预测因子，获得逐年不同的预测因子，更符合中国夏季降水异常影响因子多样性的客观事实。在完全剔除预测当年信息的回报试验中，该预测模型对1999—2019年中国夏季汛期降水异常的历史回报技巧明显高于动力模式。通过方差订正，历史回报降水的PS评分从71.00分提高到82.10分，显示了该模型的潜在预报潜力。
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关键词:
- 中国夏季汛期降水异常;
- 季节预测;
- 交叉检验;
- 潜在技巧分布图
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. -
图 1 基于1981—2019年EOF主模态和多元回归系数重构降水异常场和观测降水异常场(参考态)的泰勒图
(不同颜色的点表示基于不同数量EOF主模态和多元回归系数的重构结果)
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)
图 2 基于1981—2019年前12个EOF主模态和多元回归系数重构的降水异常场与观测逐站降水异常序列时间相关系数空间分布
(斜线和打点区分别表示达到0.05和0.01显著性水平)
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)
图 3 基于1981—2019年前12个EOF主模态和多元回归系数重构的降水异常场与观测降水异常场空间相关系数逐年时间序列
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
图 4 1999—2019年R2与不同季节预测因子的相关分布
(打点区表示达到0.05显著水平，预测因子包括30°S~30°N地区降水和南半球、北半球中高纬度地区200 hPa位势高度场)
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)
图 5 1999—2019年不同季节预测因子对R2的潜在技巧分布图
(打点区表示达到0.05显著性水平，预测因子包含30°S~30°N地区降水和南半球、北半球中高纬度地区200 hPa位势高度场)
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)
图 6 预测因子自动选择器提取的1999—2019年前4个多元回归系数的预测因子热度图
(预测因子包含30°S~30°N地区降水和南半球、北半球中高纬度地区200 hPa位势高度场)
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)
图 7 1999—2019年前4个多元回归系数的回报检验
(阴影区表示回归系数范围)
Fig. 7 Reforecast test of the first 4 multiple regression coefficients during 1999-2019
(the shaded denotes range of regression coefficients)
图 8 采用前12个多元回归系数和EOF模态的回报与观测降水站点相关系数空间分布
(打点区表示达到0.1显著性水平)
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)
图 9 预测模型对1999—2019年中国夏季汛期平均降水异常的回报检验
(a)采用前12个多元回归系数和EOF模态的回报结果相对于观测的同号率，(b)基于不同多元回归系数的回报结果相对于观测降水异常的空间相关系数的逐年变化(m表示前1~12个多元回归系数和EOF主模态的回报结果)
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)
图 10 基于1999—2019年不同多元回归系数的回报结果相对于观测降水异常的空间相关系数多年平均值
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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