A statistical downscaling approach was developed to improve seasonal-to-interannual prediction of summer rainfall over North China by considering the effect of decadal variability based on observational datasets and d...A statistical downscaling approach was developed to improve seasonal-to-interannual prediction of summer rainfall over North China by considering the effect of decadal variability based on observational datasets and dynamical model outputs.Both predictands and predictors were first decomposed into interannual and decadal components.Two predictive equations were then built separately for the two distinct timescales by using multivariate linear regressions based on independent sample validation.For the interannual timescale,850-hPa meridional wind and 500-hPa geopotential heights from multiple dynamical models＇ hindcasts and SSTs from observational datasets were used to construct predictors.For the decadal timescale,two well-known basin-scale SST decadal oscillation （the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation） indices were used as predictors.Then,the downscaled predictands were combined to represent the predicted/hindcasted total rainfall.The prediction was compared with the models＇ raw hindcasts and those from a similar approach but without timescale decomposition.In comparison to hindcasts from individual models or their multi-model ensemble mean,the skill of the present scheme was found to be significantly higher,with anomaly correlation coefficients increasing from nearly neutral to over 0.4 and with RMSE decreasing by up to 0.6 mm d-1.The improvements were also seen in the station-based temporal correlation of the predictions with observed rainfall,with the coefficients ranging from-0.1 to 0.87,obviously higher than the models＇ raw hindcasted rainfall results.Thus,the present approach exhibits a great advantage and may be appropriate for use in operational predictions.展开更多
选用2013—2020年6—8月河北省中南部(冀中南)地区1115个自动站逐小时降水数据与地形高度资料,统计该地区夏季小时强降水(hourly heavy rainfall,HHR)和暴雨日的发生频次、持续时间、降水强度等方面的分布特征。结果表明:冀中南地区夏...选用2013—2020年6—8月河北省中南部(冀中南)地区1115个自动站逐小时降水数据与地形高度资料,统计该地区夏季小时强降水(hourly heavy rainfall,HHR)和暴雨日的发生频次、持续时间、降水强度等方面的分布特征。结果表明:冀中南地区夏季发生频次为2.2~3.0次·a-1的HHR对降水贡献率大于35%,高频区有6个,在沧州东部沿海呈片状分布,在西部山区呈点状分布。小于60 mm·h^(-1)的HHR发生站次日变化特征为单峰、单谷,60 mm·h^(-1)以上发生站次随降水强度增大而锐减,日变化特征不明显。降水性质方面,冀中南地区的西部山区HHR高频区多积状云对流性降水,常发生在12:00—18:00;沧州东部沿海多受台风和切变线影响,HHR为降水强度较大的层状云和积状云混合性降水。展开更多
基金supported by the Special Program in the Public Interest of the China Meteorological Administration (Grant No. GYHY201006022)the Strategic Special Projects of the Chinese Academy of Sciences (Grant No. XDA05090000)
文摘A statistical downscaling approach was developed to improve seasonal-to-interannual prediction of summer rainfall over North China by considering the effect of decadal variability based on observational datasets and dynamical model outputs.Both predictands and predictors were first decomposed into interannual and decadal components.Two predictive equations were then built separately for the two distinct timescales by using multivariate linear regressions based on independent sample validation.For the interannual timescale,850-hPa meridional wind and 500-hPa geopotential heights from multiple dynamical models＇ hindcasts and SSTs from observational datasets were used to construct predictors.For the decadal timescale,two well-known basin-scale SST decadal oscillation （the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation） indices were used as predictors.Then,the downscaled predictands were combined to represent the predicted/hindcasted total rainfall.The prediction was compared with the models＇ raw hindcasts and those from a similar approach but without timescale decomposition.In comparison to hindcasts from individual models or their multi-model ensemble mean,the skill of the present scheme was found to be significantly higher,with anomaly correlation coefficients increasing from nearly neutral to over 0.4 and with RMSE decreasing by up to 0.6 mm d-1.The improvements were also seen in the station-based temporal correlation of the predictions with observed rainfall,with the coefficients ranging from-0.1 to 0.87,obviously higher than the models＇ raw hindcasted rainfall results.Thus,the present approach exhibits a great advantage and may be appropriate for use in operational predictions.
文摘选用2013—2020年6—8月河北省中南部(冀中南)地区1115个自动站逐小时降水数据与地形高度资料,统计该地区夏季小时强降水(hourly heavy rainfall,HHR)和暴雨日的发生频次、持续时间、降水强度等方面的分布特征。结果表明:冀中南地区夏季发生频次为2.2~3.0次·a-1的HHR对降水贡献率大于35%,高频区有6个,在沧州东部沿海呈片状分布,在西部山区呈点状分布。小于60 mm·h^(-1)的HHR发生站次日变化特征为单峰、单谷,60 mm·h^(-1)以上发生站次随降水强度增大而锐减,日变化特征不明显。降水性质方面,冀中南地区的西部山区HHR高频区多积状云对流性降水,常发生在12:00—18:00;沧州东部沿海多受台风和切变线影响,HHR为降水强度较大的层状云和积状云混合性降水。
