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Bias Correction of Summer Extreme Precipitation Simulated by CWRF Model over China
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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。Abstract: Accurate forecast of extreme precipitation plays an important role in guiding the national economy and people's livelihood. The newly developed Climate-Weather Research and Forecasting model (CWRF) is applied to the operational forecasting experiment of China National Climate Center. It provides valuable scientific basis for improving the operational prediction for extreme precipitation.CWRF integrates a comprehensive ensemble of alternate parameterization schemes for each of key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation. This facilitates the use of an optimized physics ensemble approach to improve weather and climate prediction.Daily precipitation data simulated by CWRF from June to August during 1980-2015 and the observation by China Meteorological Administration are used to evaluate the performance of various parameterization schemes, and cumulative distribution function transform (CDFt) correction method is introduced. Based on the CDFt, the probability bias correction model XCDFt is proposed for extreme rainfall by introducing generalized Pareto distribution (GPD) and results are assessed. It shows that Morrison-aerosol parameterization scheme of CWRF model can simulate the spatial distribution of extreme precipitation better as well as correct daily precipitation by CDFt.Simulated results of Morrison-aerosol for daily precipitation threshold and super-threshold samples in summer in North China, Central China and East China are similar to those observed in this scheme. In Changsha, Jinan, Nanjing, and Nanning, GPD characterizes the distribution of each extreme precipitation well. It shows that XCDFt can preserve the CDF form of the observed calibrated precipitation and acquire the small change from the calibration to validations. XCDFt can improve the consistency between model simulation and observation in regional extreme precipitation recurrence levels. In North China, Central China and South China, after model simulation correction by XCDFt, the 20-year recurrence interval of extreme precipitation are closer to the observation, which shows that the revised data are more reliable.Error correction can only be used as a supplementary means to improve extreme precipitation prediction, though. The precision description of model physical process and the improvement of model resolution are the key to improve extreme precipitation prediction level.
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Key words:
- CWRF;
- extreme rainfall;
- bias correction
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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] 
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