摘要
依据区域气候模式RIEMS2.0输出的3 km高分辨率数据和站点降水记录分析了中国西北黑河流域降水的动力降尺度和统计—动力降尺度问题,检验了多种因子组合下多元线性回归(MLR)和贝叶斯模式平均(BMA)降尺度模型,评估了降尺度降水的均方根误差、相关系数、方差百分率及“负降水”偏差率等方面的统计特征。结果表明,动力降尺度降水相关系数最高,误差也最大,降水方差达到观测值的1.5~2倍;除相关系数外,统计—动力降尺度模型的几个统计特征均最优,纯统计模型次之。检验表明,仅用700 hPa位势高度场、经向风和比湿等构建的统计降尺度模型估计的站点降水相关系数较低,均方根误差也较大。当在统计降尺度模型中引入模式降水因子后站点降水的估计得到明显改善,其中MLR类模型的降水相关系数和方差百分率均明显高于BMA类模型,均方根误差二者相当,但前者“负降水”出现频次明显大于后者,“负降水”偏差主要出现在降水稀少的冬半年及黑河中、下游干旱或极端干旱区,上游出现频率较低,其中MLR类模型“负降水”出现频次明显高于BMA类模型,后者仅出现在黑河中、下游地区。包含模式降水因子的统计—动力降尺度模型能减少“负降水”出现的频次。此外,降尺度模型估计降水的统计特征随季节变化,其中7种降尺度模型估计的站点降水误差与站点气候降水量成比例,但相对误差与之相反。这些评估结果表明,即使用高分辨率动力降尺度估计干旱区站点降水也存在明显偏差,需要结合统计降尺度模型进一步降低站点降水估计的不确定性。
This paper focuses on the dynamic and statistical–dynamical downscaling techniques for estimating the precipitation at the stations in the Heihe River basin of Northwest China based on local observations at 14 sites and the outputs from RIEMS2.0(Regional Climate Model)with a grid resolution of 3×3 km.The precipitation estimated further using MLR(multiple linear regression)and BMA(Bayesian Model Average)with different factor combinations is tested on the assessment indices as RMSE(Root Mean Square Error),correlation coefficient,variance percent,and“negative precipitation bias”with observation.Results show that the precipitation produced by the dynamic model has the largest RMSE,the most significant coherence,and a considerably larger variance than the observation by a factor of 1.5–2.Except for correlation coefficients,the statistical–dynamical downscaling models are optimal,and the statistical models are between statistical–dynamical models and dynamic model.The test shows that the correlation coefficient of the statistical downscaling models constructed with 700-hPa geopotential height field,meridional wind,and specific humidity is lower,and the RMSE is larger.The statistic indices were improved when the precipitation factor was introduced into the statistically downscaling models.The correlation coefficient and variance percentage of MLR models are considerably higher than BMA models,the RMSE of the two types of models is close in value,but the bias of negative precipitation of the former is significantly higher than that of the latter.The negative precipitation produced by the statistically downscaling models appears mainly in the cold season or dry and arid lands,such as the lower reaches of the river,of which the“negative precipitation”frequency decreases if the model precipitation is added as a factor in the downscaling models.Moreover,the statistical assessment of the monthly precipitation estimated from the downscaling models reveals that the four indices would evolve with season,in which
作者
苏海锋
戴新刚
熊喆
延晓冬
Haifeng SU;Xin’gang DAI;Zhe XIONG;Xiaodong YAN(Key Laboratory of Regional Climate–Environment for Temperate East Asia,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029;University of Chinese Academy of Sciences,Beijing 100049;Future Earth Research Institute,Beijing Normal University,Beijing 100875)
出处
《大气科学》
CSCD
北大核心
2023年第3期642-654,共13页
Chinese Journal of Atmospheric Sciences
基金
国家自然科学基金项目41675087、42061144015
国家重点研发计划项目2016YFA0600404。
关键词
黑河流域
极端干旱区
站点降水估计
区域气候模式
统计—动力降尺度
Heihe River basin
Extremely dry land
In-site precipitation estimate
Regional climate model
Statistical–dynamical downscaling
