设为首页
加入收藏
Bias Correction of Summer Extreme Precipitation Simulated by CWRF Model
-
摘要: 基于1980—2015年6—8月CWRF模式（Climate-Weather Research and Forecasting model）14种方案的模拟结果和全国逐日降水观测资料，对比了Q-lin，Q-tri，RQ-lin，RQ-tri，SSP-lin和CDFt 6种误差订正方法对CWRF模式控制化方案（C1）模拟中国东部夏季日极端降水的订正效果，以CWRF模式14种方案日极端降水的模拟效果排名为基础，对比了模拟效果较好的4种方案集合、模拟较差的4种方案集合以及14种方案集合的订正效果，选出相对较好的订正方案进一步评估其成员集合后订正和成员分别订正后再集合的订正效果，结果表明：采用6种误差订正方法均可明显减少日极端降水模拟误差，其中RQ-lin方法订正效果最佳。CWRF模式对中国东部的极端降水指数均表现出较好的模拟能力，不同参数化集合方案得到14种方案成员先订正再集合与观测日极端降水平均值最为接近，研究结果对于改进模拟结果、提高其预测能力有重要应用价值。Abstract: The 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) integrates a comprehensive ensemble of alterable parameterization schemes for each of the 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. Evaluating the simulation performance and correcting the error can effectively improve the operational prediction level of extreme precipitation in CWRF model.Daily rainfall data simulated by CWRF model and observed at 2416 meteorological stations in China from June to August during 1980-2015 are used to compare correcting effects of Q-lin, Q-tri, RQ-lin, RQ-tri, SSP-lin and CDFt on extreme precipitation of control scheme simulated by CWRF in eastern China. Based on the simulation performance ranking of 14 parameterization schemes in CWRF model, effects of the top 4, the latter 4 and the ensemble of 14 parameterization schemes are compared. Correcting effects of two approaches are compared: Revising after the collection of members and revising before the collection of members. Main results show that the error of the extreme precipitation simulation of C1 scheme can be obviously reduced by using six error correction methods, among which the RQ-lin correction method is the best. Although there are great differences between parameterization schemes in the simulation of extreme precipitation index, CWRF model shows good ability for extreme precipitation index in eastern China. The first four parametric schemes with good extreme precipitation simulation ability are C13, C14, C12 and C1, while the C6, C4, C3 and C10 schemes perform worse, respectively. Different parameterization schemes are revised to ensure that it is the closest to the average value of observed extreme precipitation after each of 14 members of the parameterization scheme being revised. Results have important application value for improving outputs of model and improving its prediction ability.Error correction can only be used as a supplementary means to improve extreme precipitation prediction. The precision of model physical process and the improvement of model resolution are the key to improve extreme precipitation prediction.
-
Key words:
- CWRF;
- extreme rainfall;
- simulation evaluation;
- error correction
-
图 1 验证时期CWRF模式C1方案日极端降水平均值及经6种方法的订正值与观测值
Fig. 1 Daily extreme precipitation from observation and simulation by CWRF control scheme(C1) with its revision under 6 methods in validation period
图 2 CWRF模式14种方案下5种极端降水指数的泰勒评分
Fig. 2 Taylor score for 5 simulated extreme indices of 14 parameterization schemes of CWRF Model
图 3 CWRF模式14种方案下5种极端指数的M2指数
Fig. 3 M2 for 5 simulated extreme indices of 14 parameterization schemes of CWRF Model
图 4 RQ-lin方法订正后不同方案集合的日极端降水平均值和观测值(a)观测，(b)C1，(c)C1，C12，C13和C14的集合，(d)C3，C4，C6和C10的集合，(e)C1~C14的集合
Fig. 4 Daily extreme precipitation mean for different parameterization schemes under RQ-lin revision and observation (a)observation, (b)C1, (c)sets of C1, C12, C13 and C14, (d)sets of C3, C4, C6 and C10, (e)sets of C1-C14
图 5 14种方案成员分别订正后再集合的日极端降水平均值空间分布
Fig. 5 Daily extreme precipitation mean of 14 parameterization schemes revised and regrouped
表 1 CWRF模式参数化方案组合
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 GSFCGCE C3 BMJ GSFCGCE C4 Grell GSFCGCE C5 NSAS GSFCGCE C6 Donner GSFCGCE C7 Emanuel GSFCGCE C8 ECP & UW Lin C9 ECP & UW WSM6 C10 ECP & UW Etamp-new C11 ECP & UW Thompson C12 ECP & UW Thompson-aero C13 ECP & UW Morrison C14 ECP & UW Morrison-aerosol 
下载: 导出CSV
表 2 极端降水指数
Table 2 Extreme rainfall indices
指数名称 缩写 定义 单位 降水强度 SDII 总降水量/有雨日数 mm·d-1 暴雨日数 R50 日降水量不低于50 mm的日数 d 第95百分位降水量 P95 日降水量在第95百分位的值 mm 强降水量 R95P 日降水量大于第95百分位值的总降水量 mm 极端降水贡献率 R95T 超过第95百分位降水量之和占总降水量的百分率 %
下载: 导出CSV
表 3 验证时期CWRF模式C1方案模拟的日极端降水平均值经6种方法订正后与观测值的区域相关系数以及均方根误差
Table 3 Regional correlation coefficient and root mean square error of daily extreme precipitation mean from observation to revision of CWRF control scheme(C1) under 6 methods in validation period
订正方法 相关系数 均方根误差 Q-lin 0.81 10.29 Q-tri 0.78 11.64 RQ-lin 0.82 10.03 RQ-tri 0.80 10.75 SSP-lin 0.67 18.76 CDFt 0.73 15.77
下载: 导出CSV
表 4 14种方案模拟能力排名
Table 4 Ranking of simulation capabilities of 14 parameterization schemes
方案 泰勒评分 时间变率 综合 C1 3 8 4 C2 8 4 5 C3 10 13 12 C4 14 11 13 C5 11 2 8 C6 13 14 14 C7 12 7 10 C8 5 8 8 C9 4 8 5 C10 9 11 11 C11 6 6 5 C12 6 4 3 C13 1 1 1 C14 2 2 2
下载: 导出CSV
-
[1] 王静, 余锦华, 何俊琦.江淮地区极端降水特征及其变化趋势的研究.气候与环境研究, 2015, 20(1):80-88. http://d.wanfangdata.com.cn/Periodical/qhyhjyj201501009 [2] 霍治国, 范雨娴, 杨建莹, 等.中国农业洪涝灾害研究进展.应用气象学报, 2017, 28(6):641-653. doi: 10.11898/1001-7313.20170601 [3] 甘衍军, 徐晶, 赵平, 等.暴雨致洪预报系统及其评估.应用气象学报, 2017, 28(4):385-398. doi: 10.11898/1001-7313.20170401 [4] 翟盘茂, 王萃萃, 李威.极端降水事件变化的观测研究.气候变化研究进展, 2007, 3(3):144-148. http://www.cnki.com.cn/Article/CJFDTotal-QHBH200703005.htm [5] 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: 10.11898/1001-7313.20190210 [6] Liang X Z, Sun C, Zheng X, et al.CWRF performance at downscaling China climate characteristics.Climate Dyn, 2018, 12:1-26. doi: 10.1007/s00382-018-4257-5 [7] 刘冠州, 梁信忠.新一代区域气候模式(CWRF)国内应用进展.地球科学进展, 2017, 32(7):781-787. http://www.cqvip.com/QK/94287X/201707/673221462.html [8] Liang X Z, Xu M, Yuan X, et al.Regional climate-weather research and forecasting model (CWRF).Bull Amer Meteor Soc, 2012, 93(9):1363-1387. [9] 董晓云, 余锦华, 梁信忠, 等.CWRF模式在中囯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 [10] Khain A, Lang S, Lynn B, et al.Microphysics, radiation and surface processes in the Goddard Cumulus Ensemble (GCE) model.Meteor Atmos Phy, 2003, 82(1):97-137. http://www.springerlink.com/index/44myc52xcmc3ajxv.pdf [11] Qiao F, Liang X Z.Effects of cumulus parameterization closures on simulations of summer precipitation over the United States coastal oceans.J Adv Model Earth Sys, 2016, 8(1/2):1-23. doi: 10.1002/jame.20206 [12] Qiao F, Liang X Z.Effects of cumulus parameterization closures on simulations of summer precipitation over the continental United States.Climate Dyn, 2017, 49(1/2):225-247. doi: 10.1007/s00382-016-3323-0 [13] Bretherton C S, Park S S.A new moist turbulence parameterization in the Community Atmosphere Model.J Climate, 2009, 22(12):3422-3448. doi: 10.1175/2008JCLI2556.1 [14] Kain J S.The Kain-Fritsch convective parameterization:An update.J Appl Meteor, 2004, 43:170-181. doi: 10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2 [15] Janji c' I J.The step-mountain eta coordinate model:Further developments of the convection, viscous sublayer, and turbulence closure schemes.Mon Wea Rev, 1994, 122(5):927-945. doi: 10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2 [16] Grell G A, Dévényi D.A generalized approach to parameterizing convection combining ensemble and data assimilation techniques.Geophys Res Lett, 2002, 29(6):587-590. http://www.researchgate.net/publication/239743738_A_generalized_approach_to_parameterizing_convection_combining_ensemble_and_data_assimilation [17] Han J, Pan H L.Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System.Wea Forecasting, 2011, 26(4):520-533. doi: 10.1175/WAF-D-10-05038.1 [18] Donner L J.A cumulus parameterization including mass fluxes, convective vertical velocities, and mesoscale effects:Thermodynamic and hydrological aspects in a general circulation model.J Climate, 2001, 14(16):3444-3463. doi: 10.1175/1520-0442(2001)014<3444:ACPIMF>2.0.CO;2 [19] Emanuel K A, Ivkovirothman M.Development and evaluation of a convection scheme for use in climate models.J Atmos Sci, 1999, 56(11):1766-1782. doi: 10.1175/1520-0469(1999)056<1766:DAEOAC>2.0.CO;2 [20] Lin Y L, Farley R D, Orville H D.Bulk parameterization of the snow field in a cloud model.J Appl Meteor, 1983, 22(6):1065-1092. doi: 10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2 [21] Hong S Y, Lim J O J.The WRF single-moment 6-class microphysics scheme (WSM6).J Korean Medi Sci, 2006, 42:129-151. http://www.researchgate.net/publication/284701586_The_WRF_single-moment_6-class_microphysics_scheme_WSM6 [22] Thompson G, Rasmussen R M, Manning K, et al.Explicit forecasts of winter precipitation using an improved bulk microphysics scheme.Part Ⅰ:Description and sensitivity analysis.Mon Wea Rev, 2004, 136(12):5095-5115. http://www.researchgate.net/publication/249621721_Explicit_Forecasts_of_Winter_Precipitation_Using_an_Improved_Bulk_Microphysics_Scheme._Part_I_Description_and_Sensitivity_Analysis [23] Thompson G, Eidhammer T.A study of aerosol impacts on clouds and precipitation development in a large winter cyclone.J Atmos Sci, 2014, 71(10):3636-3658. doi: 10.1175/JAS-D-13-0305.1 [24] Morrison H, Thompson G, Tatarskii V.Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line:Comparison of one- and two-moment schemes.Mon Wea Rev, 2009, 137(3):991-1007. doi: 10.1175/2008MWR2556.1 [25] Taylor K E.Summarizing multiple aspects of model performance in a single diagram.J Geophys Res, 2001, 106(D7):7183-7192. doi: 10.1029/2000JA002008 [26] Chen W, Jiang Z, Li L.Probabilistic projections of climate change over China under the SRES A1B Scenario using 28 AOGCMs.J Climate, 2011, 24(17):4741-4756. doi: 10.1175/2011JCLI4102.1 [27] 杭月荷.CMIP5多模式对中国极端降水的模拟评估及未来情景预估.南京: 南京信息工程大学, 2013. [28] 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. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201706003.htm [29] 曾晓青, 薛峰, 姚莉, 等.针对模式风场的格点预报订正方案对比.应用气象学报, 2019, 30(1):51-62. doi: 10.11898/1001-7313.20190105 [30] 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):49-60. doi: 10.11898/1001-7313.20180505 [31] 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: 10.11898/1001-7313.20170311 [32] 朱连华.中国地区极端降水的统计建模及其未来概率预估.南京: 南京信息工程大学, 2017. [33] 郭玉娣, 刘彬贤, 梁冬坡.变分方法在渤海海域ASCAT风场订正中的应用.应用气象学报, 2019, 30(3):122-130. doi: 10.11898/1001-7313.20190310 [34] 刘甲毅, 邓丽姣, 傅国斌, 等.两种统计降尺度方法在秦岭山地的适用性.应用气象学报, 2018, 29(6):99-109. doi: 10.11898/1001-7313.20180609 [35] Michelangeli P A, Vrac M, Loukos H.Probabilistic downscaling approaches:Application to wind cumulative distribution functions.Geophys Res Lett, 2009, 36(11):163-182. http://www.researchgate.net/publication/238623680_Probabilistic_downscaling_approaches_Application_to_wind_cumulative_distribution_functions -
计量
- 摘要浏览量: 3224
- HTML全文浏览量: 1402
- PDF下载量: 91
- 被引次数: 0
