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CWRF模式在中国夏季极端降水模拟的误差订正

董晓云 余锦华 梁信忠 马圆

董晓云, 余锦华, 梁信忠, 等. CWRF模式在中国夏季极端降水模拟的误差订正. 应用气象学报, 2019, 30(2): 223-232. DOI: 10.11898/1001-7313.20190209..
引用本文: 董晓云, 余锦华, 梁信忠, 等. CWRF模式在中国夏季极端降水模拟的误差订正. 应用气象学报, 2019, 30(2): 223-232. DOI: 10.11898/1001-7313.20190209.
Dong Xiaoyun, Yu Jinhua, Liang Xinzhong, et al. Bias correction of summer extreme precipitation simulated by CWRF model over China. J Appl Meteor Sci, 2019, 30(2): 223-232. DOI:  10.11898/1001-7313.20190209.
Citation: Dong Xiaoyun, Yu Jinhua, Liang Xinzhong, et al. Bias correction of summer extreme precipitation simulated by CWRF model over China. J Appl Meteor Sci, 2019, 30(2): 223-232. DOI:  10.11898/1001-7313.20190209.

CWRF模式在中国夏季极端降水模拟的误差订正

DOI: 10.11898/1001-7313.20190209
资助项目: 

南京大气科学联合研究中心北极阁开放研究基金 NJCAR-2016ZD03

国家气候中心中国精细化区域气候预测系统研发项目 NCC2016013

详细信息
    通信作者:

    董晓云, 邮箱:1203105011@qq.com

Bias Correction of Summer Extreme Precipitation Simulated by CWRF Model over China

  • 摘要: 利用1980—2015年6—8月我国逐日降水观测数据评估CWRF模式(Climate-Weather Research and Forecasting model)多种参数化方案对我国夏季日降水的模拟能力,并考察累积概率变换偏差订正法(CDFt)的订正效果。通过将广义帕累托分布(GPD)引入到偏差订正模型中,提出针对极端降水的累积概率变换偏差订正法(XCDFt),检验和评估其对极端降水订正的适用性。结果显示:CWRF模式微物理过程选用Morrison-aerosol参数化方案组合对我国降水场的模拟较好,CDFt订正效果良好;XCDFt偏差订正模型能够较好地提取模式建模与验证时期变化信号,订正后相比订正前与观测极端降水的概率分布更为接近;经过XCDFt订正后华南、华中和华北地区20年一遇的极端降水重现水平较模拟值更接近观测值,可为CWRF模式提高极端降水的业务预测水平提供参考。
  • 图  1  不同参数化方案下中国区域CWRF模拟和CDFt订正日降水量对比

    (a)布莱尔评分,(b)显著性评分,(c)均方根误差

    Fig. 1  Comparison of daily precipitation simulated by CWRF and corrected by CDFt over China under different schemes

    (a)Brier score, (b)significance score, (c)root mean square error

    图  2  不同参数化方案CWRF模拟、CDFt订正以及综合的日降水评估指标累计排名

    Fig. 2  The cumulative ranking of evaluation indicators for different parameterized schemes of simulation, correction and comprehensiveness for daily precipitation

    图  3  验证时期中国夏季日降水量第95百分位数阈值及超过该阈值的极端降水日数

    (a)CWRF模拟阈值,(b)观测阈值,(c)CWRF模拟日数,(d)观测日数

    Fig. 3  The threshold of the 95th percentile and the number of extreme precipitation days over threshold in summer during the validation period over China

    (a)simulated threshold by CWRF(b)observed threshold, (c)the number of days simulated by CWRF, (d)the number of days observed

    图  4  南京站验证时期观测极端降水GPD拟合

    Fig. 4  GPD fitting effect of the observed extreme rainfall during the validation period in Nanjing

    图  5  建模和验证时期4个代表地区XCDFt偏差订正中CWRF模拟和观测夏季极端降水累积概率分布

    Fig. 5  The cumulative probability distribution of extreme precipitation simulated by CWRF compared to observation in summer in the bias correction model XCDFt during the calibration and validation periods of four representation areas

    图  6  验证时期4个代表地区XCDFt偏差订正中CWRF模拟和观测夏季极端降水Q-Q图

    Fig. 6  Quantile-quantile plot of extreme precipitation simulated by CWRF compared to observation in summer during the validation period of four representation areas

    图  7  验证时期华南、华中、华北地区极端降水20年一遇重现水平空间分布

    Fig. 7  Spatial distribution of 20-year return level of extreme precipitation simulated, XCDFt corrected and observed over South China, Central China and North China during the validation period

    表  1  CWRF模式参数化方案

    Table  1  Parameterization schemes of CWRF

    方案 积云对流参数化 微物理过程参数化
    C1 ECP & UW GSFCGCE
    C2 KFeta[25] GSFCGCE
    C3 BMJ[26] GSFCGCE
    C4 Grell[27] GSFCGCE
    C5 NSAS[28] GSFCGCE
    C6 Donner[29] GSFCGCE
    C7 Emanuel[30] GSFCGCE
    C8 ECP & UW Lin[31]
    C9 ECP & UW WSM6[32]
    C10 ECP & UW Etamp new[6]
    C11 ECP & UW Thompson[33]
    C12 ECP & UW Thompson-aero[34]
    C13 ECP & UW Morrison[35]
    C14 ECP & UW Morrison-aerosol[6]
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-08-20
  • 修回日期:  2018-11-05
  • 刊出日期:  2019-03-31

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