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Short-term Climate Prediction for Summer Rainfall Based on Time-scale Decomposition
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摘要: 基于时间尺度分离，利用NCEP第2代气候预测系统 (CFSv2) 每年4月起报的夏季月平均预测资料, 结合实际观测资料和再分析资料，对江淮流域及华北地区夏季降水距平百分率进行降尺度预测。将预测量和预测因子分为年际分量和年代际分量，在两个时间尺度上分别建立降尺度模型，两个预测分量之和为总预测量。对1982—2008年拟合时段的夏季降水距平百分率的回报结果表明：降尺度预测结果相对于原始模式结果预测技巧显著提高。降尺度预测与实况降水在江淮流域和华北地区的空间相关系数最大值超过0.8，多年平均值也分别提高到0.53和0.51；时间相关在每个站点也显著增强，相关系数为0.38~0.65。对2009—2013年进行独立样本检验，结果表明：降尺度模型能较好地预测出该时段的降水异常空间型态。同时，该模型对2014年夏季降水长江以南偏多、黄淮地区偏少的分布形势也有一定预测能力。Abstract: By using one set of hindcasted integration of NCEP Climate Forecast System Version 2.0 (CFSv2) beginning from April during 1982-2008, for summer seasonal forecast, along with observations and reanalysis datasets, a downscaling scheme with time-scale decomposition is developed for summer rainfall prediction of the Yangtze-Huai Basins and North China. First, both the predictand and selected predictors are decomposed into inter-annual and decadal scales through Fast Flourier Transformation filtering. And then two downscaling models are separately built, predicted results for two timescales are combined to represent the total prediction. By using the scheme, the summer rainfall of 1982-2008 is hindcasted and compared with CFSv2 raw prediction first. A cross validation shows that skills in the present scheme are significantly improved with increased spatial and temporal correlation coefficients and decreased root mean square error, in comparison with the raw prediction. The spatial correlations with observations for both the Yangtze-Huai Basins and North China have the maximum exceeding 0.8 and a long-term average of 0.53, 0.51, greater than the original-0.06, -0.01 for two regions. The predicted rainfall temporal correlation at each station is also improved, with the regional mean increased from-0.2 to 0.2 in raw model prediction to about 0.5 after downscaling, significant at 0.01 level. The root mean square error exhibits a decrease with the rate of exceeding 10% at most of stations. Then a five-year hindcast from 2009 to 2013 is performed and used for validation as independent samples. Results suggest that spatial correlations of the predicted rainfall with the observed in five samples are significantly higher than the raw prediction, with the mean increased from 0.24, 0.08 to 0.37, 0.44 for two regions. Spatial patterns of rainfall anomaly percentage in two of these independent samples are reasonably closer to observations. Also, the predicted rainfall strength is much closer to the observation, comparing to the raw prediction. Finally, the scheme is applied for the real-time prediction of summer rainfall in 2014. The prediction result displays more rainfall over the mid-lower Reaches of the Yangtze and over the north region of the Yellow River valley, with an anomaly percentage of 20%, along with rainfall anomaly percentage of-10%. Compared with observations, the rainfall anomaly pattern can be predicted to some extent through the downscaling method, especially over the southern region of Yellow River.
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图 2 1982—2008年夏季实况降水距平百分率与原始模式预测和降尺度预测的空间相关 (断线为0.05显著性水平)
Fig. 2 Pattern correlations of observed rainfall anomaly percentage to CFSv2 outputs, downscaling results from 1982 to 2008(dashed line denotes passing the test of 0.05 level)
图 5 2009—2013年夏季实况降水距平百分率与原始模式预测、降尺度预测的空间相关
Fig. 5 The same as in Fig. 2, but for the independent validation periods from 2009 to 2013
图 6 2011年和2013年实况、原始模式预测和降尺度预测的降水距平百分率分布
Fig. 6 The rainfall anomaly percentage patterns of observations, CFSv2 outputs and downscaling predictions for 2011 and 2013
图 7 2014年实况 (a) 及降尺度预测 (b) 降水距平百分率分布
Fig. 7 The rainfall anomaly percentage patterns of observations (a) and the downscaling prediction (b) in 2014
表 1 两个时间尺度降尺度模型所选预测因子时段及关键区
Table 1 Periods and key regions of selected predictors in downscaling schemes on two timescales
时间尺度 预测因子 所选因子的时段及区域 对江淮地区预测 对华北地区预测 年代际 AMO指数 前一年11月 前一年11月 PDO指数 前一年12月 前一年12月 年际 NOAA海表温度 前一年冬季
(10°S~10°N, 80°~110°W)同年3月
(0°~10°N, 120°~140°E)ERA-interim 500 hPa高度场 前一年12月
(40.5°~60°N, 110°~140°E)CFSv2 500 hPa高度场 同年夏季
(30°S~60°N, 70°E~180°)同年夏季
(10°S~20°N, 0°~100°E)CFSv2 850 hPa经向风场 同年夏季
(10°S~20°N, 70°~110°E)同年夏季
(10°~30°N, 130°~150°E)ERA-interim海平面气压 同年1月
(20°~40°S, 100°~140°W)
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