杨浩,江志红,李肇新,王晓芳,崔春光. 2017. 分位数调整法在北京动力降尺度模拟订正中的适用性评估[J]. 气象学报, 75(3):460-470, doi:10.11676/qxxb2017.028
分位数调整法在北京动力降尺度模拟订正中的适用性评估
Applicability of a quantile-quantile (Q-Q) bias-correction method for climate dynamical downscaling at Beijing station
投稿时间:2016-09-29  修订日期:2017-01-09
DOI:10.11676/qxxb2017.028
中文关键词:  分位数调整  偏差订正  动力降尺度  气温  降水
英文关键词:Quantile-quantile (Q-Q) adjustment  Bias correction  Dynamical downscaling  Temperature  Precipitation
基金项目:国家自然科学基金重点项目(41230528、91637211、41620104009、41541037)、湖北省气象局科技发展基金(2017C05)、中国气象局武汉暴雨研究所科研业务费青年项目。
作者单位E-mail
杨浩 中国气象局武汉暴雨研究所, 暴雨监测预警湖北省重点实验室, 武汉, 430205  
江志红 南京信息工程大学气象灾害预报预警与评估协同创新中心和气象灾害教育部重点实验室, 南京, 210044 zhjiang@nuist.edu.cn 
李肇新 法国科研中心动力气象实验室, 巴黎, 法国  
王晓芳 中国气象局武汉暴雨研究所, 暴雨监测预警湖北省重点实验室, 武汉, 430205  
崔春光 中国气象局武汉暴雨研究所, 暴雨监测预警湖北省重点实验室, 武汉, 430205  
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中文摘要:
      基于分位数调整法对变网格模式LMDZ4在中国区域进行动力降尺度模拟的北京日平均气温和降水结果进行了统计误差订正。订正后的日平均气温在年循环、平均值和频率等方面均十分接近观测值,全年平均气温偏差由-1.2℃降至-0.4℃。降水的订正过程较气温更加复杂,首先对降水日数进行订正,以消除模式产生的虚假微量降水,订正后降水日数误差由61.5%降至3.7%。此外,分位数调整法可有效订正中小型与极端降水的频率和强度,订正后全年降水误差由0.28 mm/d降至0.07 mm/d。订正后最大降水月为7月,与观测一致,消除了冬季的虚假极端降水。分位数调整法无论是对气温还是降水,其订正效果都存在明显的季节性差异。日平均气温的订正在冬、夏季要优于春、秋季,对极端高、低气温的订正更加显著。该统计误差订正方法不仅有效消除了气候平均值的漂移,同时对极值也有一定改善,是一种相对完善的订正方案。分位数调整法也存在一定的不确定性,订正效果受观测资料和模式模拟能力影响较大。
英文摘要:
      A statistical bias correction based on quantile-quantile (Q-Q) adjustment is applied to daily temperature and precipitation at Beijing simulated by the variational resolution model LMDZ4. After bias correction, the annual cycle, the average and frequency of temperature are all closer to observation, while the deviation of annual mean temperature decreases from -1.2℃ to -0.4℃. The bias correction can remove most of the spurious drizzles generated by the LMDZ4 model. The bias of rainy days decreases to 3.7% from 61.5%. The Q-Q adjustment shows a good performance of correction on precipitation intensity and frequency, and the deviation of annual mean precipitation decreases to 0.07 mm/d from 0.28 mm/d. After correction, precipitation peaks in July, which is consistent with observation, and the false extreme precipitation in the winter is removed. The Q-Q adjustment is separately operated for different seasons for both temperature and precipitation. The corrective effect for daily temperature is superior in the winter and summer, compared to that in the spring and autumn. Significant improvements are obtained for extremely high and low air temperatures. This statistical bias-correction method not only effectively eliminates drifts on the simulated climatological mean, but also increases the capability of reproducing extreme climate values. It is a relatively satisfactory correction scheme. Meanwhile, there still exist some uncertainties in Q-Q adjustment, and the corrective effect is influenced by observational data and model performance.
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