Correction of Precipitation Forecast Predicted by DERF2.0 During the Pre-flood Season in South China
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摘要: 针对我国华南前汛期(4—6月)降水,基于国家气候中心第2代月动力延伸模式(DERF2.0)结果,利用非参数百分位映射方法将模式预测结果转化为概率预报,并进行概率订正。分别选用交叉建模与独立样本建模两种订正方法,并利用偏差、偏差百分率、时间相关系数、均方根误差等统计方法检验订正效果。结果表明:订正方法对预报技巧的改善与起报时间无显著相关,且具有误差稳定性,其订正效果受预报误差影响较小;与订正前模式预测降水落区的范围和平均强度相比,订正后结果与观测更接近;按百分位区间统计的不同强度降水订正预报均有明显改进;预测时段的订正效果与回报时段的订正效果基本一致。Abstract: There are two main types of precipitation during the pre-flood season in South China, frontal precipitation in early period and monsoon precipitation in late period. It is related to not only the tropical system, but also the cold air in the middle and high latitudes. The extended range forecasting skills of the precipitation in the pre-flood season which depend on the atmosphere-ocean interaction and the internal changes of the atmosphere are still very low. There are biases in models compared to the observations, which makes it hard to directly use model in operational forecast. Therefore, in order to better apply model forecast data to extended period forecasts, the precipitation biases are corrected during the pre-flood season from 1983 to 2019 produced by the Dynamic Extended Range Forecast Operational System version 2.0 (DERF2.0) based on a non-parameter Quantile-Mapping (QM) correction method. Daily precipitation observation data from 261 stations in South China from 1983 to 2019 are selected for evaluation. On the basis of probability forecast of the original model outputs, the model biases are then corrected using monotone cubic spline interpolation combined with the observation. The models are established by cross samples and independent samples to validate the correction method's performance by absolute difference/percentage difference, root mean square error, temporal correlation coefficient and pattern correlation coefficient. It is found that the QM correction method can improve the model forecasting skills by effectively eliminating the systematic deviation of the model. It shows that the improvement of the method remains stable with different lead times and magnitudes of model biases. Further analysis shows that the main locations and average intensities of precipitation show better consistency with observation after correction. The QM correction method can generally capture the trend difference between the model and the observation, and effectively improve the inter-annual variability of model, but it has a poor ability for extreme events. On the other hand, the revised effect of the statistical scheme according to different percentile intervals is also significant. In addition, it shows that the correction performances of prediction are more consistent with the hindcast result.
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Key words:
- Quantile-Mapping;
- probability;
- bias correction;
- stability;
- precipitation
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表 1 2001—2014年4—6月华南地区平均降水率订正前后与观测对比
Table 1 Comparison of mean precipitation rate over South China in Apr-Jun during 2001-2014
统计量 订正前 订正后 观测值 平均值/(mm·d-1) 8.99 4.19 7.67 偏差绝对值/(mm·d-1) 1.32 3.48 表 2 2015—2019年4—6月华南地区平均降水率订正前后模式预测与观测对比
Table 2 Comparison of mean precipitation rate over South China in Apr-Jun during 2015-2019
统计量 LD10 LD20 观测值 订正前 订正后 订正前 订正后 平均值/(mm·d-1) 4.176 6.39 3.92 6.95 7.21 偏差绝对值/(mm·d-1) 3.04 0.82 3.29 0.26 空间相关系数 0.31 0.41 0.29 0.36 -
[1] 伍红雨, 邹燕, 刘尉.广东区域性暴雨过程的定量化评估及气候特征.应用气象学报, 2019, 30(2): 233-244. doi: 10.11898/1001-7313.20190210Wu H Y, Zou Y, Liu W.Quantitative assessment of regional heavy rainfall process in Guangdong and its climatological characteristics.J Appl Meteor Sci, 2019, 30(2): 233-244. doi: 10.11898/1001-7313.20190210 [2] 王朋岭, 王启祎, 王东阡, 等.2012年4月华南地区降水异常事件及成因诊断分析.地理科学, 2015, 35(3): 352-357.Wang P L, Wang Q Y, Wang D Q, et al.Abnormal precipitation event and its possible mechanism over South China in April 2012.Scientia Geographica Sinica, 2015, 35(3): 352-357. [3] 郑彬, 林爱兰.广东省干旱趋势变化和空间分布特征.地理科学, 2011, 31(6): 715-720. https://www.cnki.com.cn/Article/CJFDTOTAL-DLKX201106013.htmZheng B, Lin A L.Trend and spatial features of drought in Guangdong.Scientia Geographica Sinica, 2011, 31(6): 715-720. https://www.cnki.com.cn/Article/CJFDTOTAL-DLKX201106013.htm [4] 祝从文, 刘伯奇, 左志燕, 等.东亚夏季风次季节变化研究进展.应用气象学报, 2019, 30(4): 401-415. doi: 10.11898/1001-7313.20190402Zhu C W, Liu B Q, Zuo Z Y, et al.Recent advances on sub-seasonal variability of east Asian summer monsoon.J Appl Meteor Sci.2019, 30(4): 401-415. doi: 10.11898/1001-7313.20190402 [5] 穆穆, 陈博宇, 周菲凡, 等.气象预报的方法与不确定性.气象, 2011, 37(1): 1-13.Mu M, Chen B Y, Zhou F F, et al. Methods and uncertainties of meteorological forecast.Meteorological Monthly, 2011, 37(1): 1-13. [6] 陈朝平, 冯汉中, 陈静.基于贝叶斯方法的四川暴雨集合概率预报产品释用.气象, 2010, 36(5): 32-39. doi: 10.3969/j.issn.1003-6598.2010.05.012Chen C P, Feng H Z, Chen J.Application of Sichuan heavy rainfall ensemble prediction probability products based on Bayesian method.Meteorological Monthly, 2010, 36(5): 32-39. doi: 10.3969/j.issn.1003-6598.2010.05.012 [7] 陈法敬, 矫梅燕, 陈静.一种温度集合预报产品释用方法的初步研究.气象, 2011, 37(1): 14-20.Chen F J, Jiao M Y, Chen J.A new scheme of calibration of ensemble forecast products based on Bayesian processor of output and its study results for temperature prediction.Meteorological Monthly, 2011, 37(1): 14-20. [8] 李莉, 李应林, 田华, 等.T213球集合预报系统性误差订正研究.气象, 2011, 37(1): 31-38.Li L, Li Y L, Tian H, et al.Study of bias-correction in T213 global ensemble forecast.Meteorological Monthly, 2011, 37(1): 31-38. [9] 郝民, 龚建东, 田伟红, 等.L波段探空仪湿度资料偏差订正及同化试验.应用气象学报, 2018, 29(5): 559-570. doi: 10.11898/1001-7313.20180505Hao M, Gong J D, Tian W H, et al.Deviation correction and assimilation experiment on L-band radiosonde humidity data.J Appl Meteor Sci, 2018, 29(5): 559-570. doi: 10.11898/1001-7313.20180505 [10] 卢新玉, 魏鸣, 王秀琴.TRMM月降水量产品在新疆地区的订正.应用气象学报, 2017, 28(3): 379-384. doi: 10.11898/1001-7313.20170311Lu X Y, Wei M, Wang X Q.Correction of TRMM monthly precipitation data from 1998 to 2013 in Xinjiang.J Appl Meteor Sci, 2017, 28(3): 379-384. doi: 10.11898/1001-7313.20170311 [11] 邓国, 龚建东, 邓莲堂, 等.国家级区域集合预报系统研发和性能检验.应用气象学报, 2010, 21(5): 513-523. doi: 10.3969/j.issn.1001-7313.2010.05.001Deng G, Gong J D, Deng L T, et al.Development of mesoscale ensemble prediction system at national meteorological center.J Appl Meteor Sci, 2010, 21(5): 513-523. doi: 10.3969/j.issn.1001-7313.2010.05.001 [12] 孙靖, 程光光, 张小玲.一种改进的数值预报降水偏差订正方法及应用.应用气象学报, 2015, 26(2): 173-184. doi: 10.11898/1001-7313.20150205Sun J, Cheng G G, Zhang X L.An improved bias removed method for precipitation prediction and its application.J Appl Meteor Sci, 2015, 26(2): 173-184. doi: 10.11898/1001-7313.20150205 [13] Zhu Y, Luo Y.Precipitation calibration based on the frequency-matching method.Wea Forecasting, 2015, 30(5): 1109-1124. doi: 10.1175/WAF-D-13-00049.1 [14] 李俊, 杜钧, 陈超君.降水偏差订正的频率(或面积)匹配方法介绍和分析.气象, 2014, 40(5): 580-588.Li J, Du J, Chen C J.Introduction and analysis to frequency or area matching method applied to precipitation forecast bias correction.Meteorological Monthly, 2014, 40(5): 580-588. [15] 周迪, 陈静, 陈朝平, 等.暴雨集合预报-观测概率匹配订正法在四川盆地的应用研究.暴雨灾害, 2015, 34(2): 97-104. https://www.cnki.com.cn/Article/CJFDTOTAL-HBQX201502001.htmZhou D, Chen J, Chen C P, et al.Application research on heavy rainfall calibration based on ensemble forecast vs.observed precipitation probability matching method in the Sichuan basin.Torrential Rain and Disasters, 2015, 34(2): 97-104. https://www.cnki.com.cn/Article/CJFDTOTAL-HBQX201502001.htm [16] 智协飞, 赵忱.基于集合成员订正的强降水多模式集成预报.应用气象学报, 2020, 31(3): 303-314. doi: 10.11898/1001-7313.20200305Zhi X F, Zhao C.Heavy Precipitation forecasts based on multi-model ensemble members.J Appl Meteor Sci, 2020, 31(3): 303-314. doi: 10.11898/1001-7313.20200305 [17] Panofsky H A, Brier G W.Some Applications of Statistics to Meteorology.Philadelphia:The Pennsylvania State University Press, 1968: 224-225. [18] 章大全, 陈丽娟.基于DERF2.0的月平均温度概率订正预报.大气科学, 2016, 40(5): 1022-1032.Zhang D Q, Chen L J.Bias correction in monthly means of temperature predictions of the dynamic extended range forecast model.Chinese Journal of Atmospheric Sciences, 2016, 40(5): 1022-1032. [19] 童尧, 高学杰, 韩振宇, 等.基于RegCM4模式的中国区域日尺度降水模拟误差订正.大气科学, 2017, 41(6): 1156-1166. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201706003.htmTong Y, Gao X J, Han Z Y, et al.Bias correction of daily precipitation simulated by RegCM4 model over China.Chinese Journal of Atmospheric Sciences, 2017, 41(6): 1156-1166. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201706003.htm [20] Raisanen J, Raty O.Projections of daily mean temperature variability in the future:Cross-validation tests with ENSEMBLES regional climate simulations.Climate Dyn, 2013, 41(5-6): 1553-1568. [21] 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: 10.11898/1001-7313.20190209Dong X Y, Yu J H, Liang X Z, et al.Bias correction of summer extreme precipitation simulated by CWRF model over China.J Appl Meteorl Sci, 2019, 30(2): 223-232. doi: 10.11898/1001-7313.20190209 [22] Kang I S, Kim H M.Assessment of MJO predictability for boreal winter with various statistical and dynamical models.J Climate, 2010, 23(9): 2368-2378. [23] Abhilash S, Sahai A K, Borah N, et al.Prediction and monitoring of monsoon intraseasonal oscillations over Indian monsoon region in an ensemble prediction system using CFSv2.Climate Dyn, 2014, 42(9-10): 2801-2815. [24] DeMott C A, Stan C, Randall D A, et al.Intraseasonal variability in coupled GCMs:The roles of ocean feedbacks and model physics.J Climate, 2014, 27(13): 4970-4995. [25] Liu X, Yang S, Li Q, et al. Subseasonal forecast skills and biases of global summer monsoons in the NCEP Climate Forecast System version 2.Climate Dyn, 2014, 42(5-6): 1487-1508. [26] Liu X, Yang S, Li J, et al.Subseasonal predictions of regional summer monsoon rainfall over tropical Asian oceans and land.J Climate, 2015, 28(24): 9583-9605. [27] Liu X, Yang S, Kumar A, et al.Diagnostics of subseasonal prediction biases of the Asian summer monsoon by the NCEP Climate Forecast System.Climate Dyn, 2013, 41(5-6): 1453-1474. [28] Anderson J L, Van den Dool H M.Skill and return of skill in dynamic extended-range forecasts.Mon Wea Rev, 1994, 122: 507-516. [29] 章大全, 郑志海, 陈丽娟, 等.10~30 d延伸期可预报性与预报方法研究进展.应用气象学报, 2019, 30(4): 416-430. doi: 10.11898/1001-7313.20190403Zhang D Q, Zheng Z H, Chen L J, et al.Advances on the predictability and prediction methods of 10-30 d extended range forecast.J Appl Meteor Sci, 2019, 30(4): 416-430. doi: 10.11898/1001-7313.20190403 [30] Li Q, Wang J, Yang S, et al.Sub-seasonal prediction of rainfall over the South China Sea and its surrounding areas during spring-summer transitional season.Int J Climatol, 2020, 40(10): 4326-4346. [31] Piani C, Haerter J O, Coppola E.Statistical bias correction for daily precipitation in regional climate models over Europe.Theor Appl Climatol, 2010, 99(1-2): 187-192.DOI: 10.1007/s00704-009-0134-9.