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The Statistical Downscaling Method of Summer Rainfall Prediction over the Huang-Huai Valley
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摘要: 利用1991-2011年黄淮地区夏季降水、NCEP/NCAR再分析资料和国家气候中心第2代动力气候模式（BCC_CSM1.1m）夏季回报结果，研究黄淮地区夏季降水降尺度预测模型和可预报性来源。诊断发现，黄淮地区夏季降水与同期南亚高压、乌拉尔山附近阻塞高压、西风急流、西太平洋赤道上空200 hPa纬向风场呈明显正相关。分析BCC_CSM1.1m对夏季环流的回报结果发现，模式对200 hPa和500 hPa位势高度场、200 hPa纬向风场和850 hPa经向风场上影响黄淮地区夏季降水的部分关键区域有较好的模拟能力。利用模式预报技巧较高且对黄淮地区夏季降水的影响有物理含义的环流特征作为预测因子，对比预测因子进行独立性筛选前后分别建立的降尺度预测模型发现，黄淮地区夏季降水预测与实况的距平符号一致率由61%提高到72%。预测技巧来源分析发现，降尺度预测能力与BCC_CSM1.1m对影响黄淮地区夏季降水的3个关键因子乌拉尔山附近环流、南亚高压、西太平洋赤道上空西风强弱的预测技巧密切相关，尤其是模式对西太平洋赤道上空西风的模拟能力起到决定性作用。Abstract: The statistical downscaling method and predictability of summer rainfall anomaly over the Huang-Huai Valley (SRAHV) is studied based on station precipitation data, NCEP/NCAR reanalysis data and BCC_CSM1.1m hindcasts from 1991 to 2011.Firstly, correlation coefficients between SRAHV and seasonal circulations in troposphere are calculated. In the high troposphere, significant circulation patterns are the South Asia high, the westerly over Eurasia, 200 hPa zonal wind over the southern of South China Sea and the Philippines. In the middle level, significant predictors are blocking high over Ural and west Pacific subtropical high. In the low level, southern anomaly wind over South China is the key factor. These predictors show clearly positive relationship with SRAHV and may lead to more rainfall.Secondly, the performance of BCC_CSM1.1m is diagnosed on the basis of summer hindcast circulations. Skills of 200 hPa and 500 hPa potential heights, 200 hPa zonal wind, 850 hPa meridional wind by BCC_CSM1.1m are relatively high in some key regions which may affect the SRAHV in reasonable physical mechanism. Six key factors are selected based on the consistent anomaly ratio of factors between BCC_CSM1.1m and reanalysis data, as well as the ratio between SRAHV and predictors from reanalysis data. The optimal sub-tree regression (OSR) is used as transfer function in the statistical downscaling model. Six predictors are tested by one-year-out cross validation sample tests. The consistent ratio between observation of SRAHV and prediction is 61%. By deleting dependent factors, three independent predictors (200 hPa potential height over the Ural, 200 hPa potential height over the South Asia high region to South China, 200 hPa zonal wind over South China Sea to South Philippines) are used to make the statistical downscaling model again, and the accuracy is improved to 72%.Further studies show that the predictability of statistical downscaling model comes from the skill of three key predictors by BCC_CSM1.1m, representing the strength of blocking activity over the Ural, the strength and position of the South Asia high, and the strength of west anomaly wind over the west tropical Pacific. When model output show high skill on three factors, skills of downscaling model are also high and predictions of SRAHV are close to observations in the years of 1994, 1995, 1998, 2004 and 2010. In the years of 1991, 1996 and 1997, BCC_CSM1.1m performs poorly especially on west anomaly wind over the west tropical Pacific. The correlation coefficient of west anomaly wind over the west tropical Pacific and SRAHV is 0.55 which passing the test of 0.01 level, indicating BCC_CSM1.1m's important role in the statistical downscaling model, which determines the prediction skill of SRAHV.
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图 1 1991—2011年黄淮地区夏季降水与同期200 hPa位势高度场的相关分布
(阴影分别为达到0.05，0.01和0.001的显著性水平)
Fig. 1 The correlation of summer rainfall anomaly over the Huang-Huai Valley to 200 hPa geopotential height in 1991-2011
(shaded areas denote passing tests of 0.05, 0.01, 0.001 levels, respectively)
图 6 黄淮地区夏季降水实况距平百分率与6因子降尺度预测降水距平百分率结果比较 (单位：%)
Fig. 6 The observation and downscaling result from six factors of summer rainfall anomaly over the Huang-Huai Valley
图 7 黄淮地区夏季实况降水距平百分率与3因子降尺度预测降水距平百分率、模式直接输出降水距平百分率结果比较
Fig. 7 The observation, downscaling result from three factors and model output of summer rainfall anomaly over the Huang-Huai Valley
图 8 1996年夏季200 hPa位势高度场 (单位：gpm) 和200 hPa纬向风场 (单位：m/s) 距平分布
(a) 再分析资料高度场距平，(b) BCC_CSM1.1m模拟高度场距平，(c) 再分析资料纬向风场距平，(d) BCC_CSM1.1m模拟纬向风场距平
Fig. 8 200 hPa geopotential height anomaly (unit:gpm) and zonal wind anomaly (unit:m/s) in summer of 1996
(a) geopotential height anomaly of reanalysis data, (b) geopotential height anomaly of BCC_CSM1.1m, (c) zonal wind anomaly of reanalysis data, (d) zonal wind anomaly of BCC_CSM1.1m
图 9 夏季南海—菲律宾低纬度地区上空200 hPa纬向风预测因子区域平均 (u200-4) 和黄淮地区夏季降水距平百分率归一化值对比
Fig. 9 The normalized 200 hPa zonal wind over South China Sea to South Philippines (u200-4) and summer rainfall anomaly over the Huang-Huai Valley
表 1 模式与再分析资料环流因子符号一致率及黄淮地区夏季降水距平百分率与再分析资料环流因子物理意义一致率
Table 1 The consistent anomaly ratio of factors between BCC_CSM1.1m and reanalysis data, as well as the ratio between summer rainfall anomaly over the Huang-Huai Valley and predictors from reanalysis data
环流因子 模式与再分析资料环流因子符号一致率/% 与再分析资料环流因子物理意义一致率/% h200-1 62 67 h200-2 72 67 h200-3 67 57 h500-1 48 62 h500-2 38 81 h500-3 67 57 h500-4 67 72 u200-1 67 72 u200-2 38 38 u200-3 57 52 u200-4 67 76 u850-1 52 67 u850-2 48 33 v850-1 62 81 v850-2 52 57 
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表 2 预测因子之间的相关系数
Table 2 Correlation coefficients among circulation factors
因子名称 h200-1 h200-2 h500-5 u200-1 u200-4 v850-1 h200-1 1 0.31 -0.03 -0.35 -0.43 -0.53 h200-2 1 0.59 -0.65 -0.17 -0.48 h500-5 1 -0.04 -0.48 0.25 u200-1 1 0.40 0.46 u200-4 1 0.75 v850-1 1
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表 3 预报高低年份归一化的关键环流因子模拟结果与再分析资料比较
Table 3 Normalized predictors from BCC_CSM1.1m hindcast data and reanalysis data
年份 h200-1 h200-2 u200-4 模拟值 实况值 模拟值 实况值 模拟值 实况值 1992 -0.98 -2.18 -3.10 -1.57 1.24 -0.16 1996 -0.37 -0.13 -0.30 -0.41 -0.91 1.47 1997 -0.02 -1.34 -0.74 -1.63 -0.13 -0.77 1994 -1.22 -0.43 -0.24 -0.13 -0.74 -1.23 1995 -0.85 -0.48 -0.89 -1.66 0.71 1.06 1998 -0.91 2.17 0.24 1.94 2.94 2.24 2004 -0.07 0.50 0.46 -0.71 -0.21 -1.73 2010 -2.07 1.30 1.26 1.45 1.76 1.70
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