COMPARISON OF BIAS CORRECTION TECHNIQUES BASED ON CWRF MODEL FOR DAILY PRECIPITATION IN SUMMER
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摘要: 误差订正是提高模式模拟和预报性能的有效方法。基于CWRF(regional Climate-Weather Research and Forecasting model)25套不同物理参数化方案的日降水量模拟资料, 对比仅进行降水日数订正(OCD)、仅进行降水量订正(OCM)和先订正降水日数再订正降水量(COR)三种订正方法, 先订正再等权重集成和先等权重集成再订正两种订正思路, 重点对1997—2015年华中和华南地区夏季日降水进行订正效果的对比。结果表明:(1)降水日的订正是必要的, 综合而言COR方法对CWRF模式日降水的订正效果更佳, 尤其是小量级降水, 但降水强度的表现不如OCM; (2)先集成后订正的效果更好; (3) CWRF模式不同参数化方案对日降水的模拟能力有显著差别, 经过订正后模拟能力均有所提升, 但对于不同的模拟方案, 其订正效果也不同。表明, 误差订正确实能有效提高模式模拟及预报性能, 但其效果存在不确定性。提高模式的预报性能, 关键还是提高模式对真实大气动力学的表述能力。Abstract: Error correction is an effective method to improve the performance of simulation and prediction in a model. Based on summer daily precipitation simulated by CWRF (regional Climate-Weather Research and Forecasting model), focusing on central and southern China, the effectiveness of three correction methods including only corrected precipitation day (OCD), only corrected magnitude of precipitation (OCM) and corrected both (COR) as well as two schemes that correction before or after equal-weighted integration were compared in the paper. The result are as follows. (1) The correction of precipitation day is necessary. Compared with OCM, COR was better on the effect of CWRF daily precipitation correction, especially of smaller magnitude. (2) Equal-weighted integration before correction performed better. (3) Different parametric schemes of CWRF model have significant differences in the simulation ability of daily precipitation, and the simulation ability has been improved after the revision. The revision effect is different for different simulated schemes. The key to improve the prediction performance of CWRF is to improve the ability of the model to express real atmospheric dynamics.
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Key words:
- CWRF /
- bias correction /
- quantile mapping
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图 4 同图 2, 但为降水强度(单位:mm/day)
图 6 同图 2, 但为暴雨日数(单位:天)
图 7 同图 5, 但为暴雨日数(单位:天)
表 1 本文选择的25套CWRF模式方案的名称及其物理过程的组合[26]
方案名称
CASE_NAME积云方案
CU微物理方案
MP辐射方案
RA边界层方案
BL下垫面方案
SF云量方案
CLControl ECP+ KFeta KFeta BMJ BMJ Grell Grell Tiedtke Tiedtke GSFC+ NSAS NSAS Donner Donner Emanuel Emanuel GSFCLXZ+ Lin Lin WSM6 WSM6 CAM3+ Etamp Etamp Thompson Thompson Thompson-aero Thompson-aero CSSP+ Morrison Morrison XRL Morrison/aerosol Morrison/aerosol CCCMA CCCMA CAWCR CAWCR CAM ECP+ CAM FuLiou FuLiou RRTMG RRTMG YSU GSFC+ YSU MYNN MYNN Boulac Boulac ACM GSFCLXZ+ ACM UW UW NOAH CAM3+ NOAH Prognostic CC CSSP+ Prognostic CC -
[1] 麻素红, 陈德辉.国家气象中心区域台风模式预报性能分析[J].热带气象学报, 2018, 34(4): 451-459. [2] 徐经纬, 徐敏, 蒋熹, 等.区域气候模式REMO对中国气温和降水模拟能力的评估[J].气候变化研究进展, 2016, 12(4): 286-293. [3] 夏阳, 龙园, 任倩, 等.云贵高原夏季不同等级极端日降水事件的气候特征[J].热带气象学报, 2018, 34(2): 239-249. [4] 任倩, 祁莉, 詹丰兴, 等.江南雨季降水与前期西太平洋暖池热含量异常的关系及其可能机制[J].大气科学学报, 2018, 41(6): 762-774. [5] LIANG X Z, LI L, KUNKEL K E, et al. Regional Climate Model simulation of U.S. precipitation during 1982-2002. Part Ⅰ: Annual cycle[J]. J Climate, 2004, 17(17) : 3 510-3 529. [6] LIANG X Z, PAN J, ZHU J, et al. Regional climate model downscaling of the U.S. summer climate and future change[J]. J Geophys Res Atmos, 2006, 111(D10): 1 879-1 894. [7] LIANG X Z, KUNKEL K E, SAMEL A N. Development of a Regional Climate Model for U.S. Midwest applications. Part Ⅰ: Sensitivity to Buffer zone treatment[J]. J Climate, 2001, 14(23): 4 363-4 378. [8] LIANG X Z, KUNKEL K E, WILHELMSON R, et al. The WRF simulation of the 1993 central U.S. heavy rain: Sensitivity to cloud microphysics representation[C] //Proceedings of the 82nd AMS Annual Meeting: 16th Conference on Hydrology. Orlando, FL, January, 2002, 13 /17: 123-126. [9] 刘冠州, 梁信忠.新一代区域气候模式(CWRF)国内应用进展[J].地球科学进展, 2017, 32(7): 781-787. [10] QIAO F, LIANG X Z. Effects of cumulus parameterizations on predictions of summer flood in the Central United States[J]. Climate Dyn, 2014, 45(3/4): 727-744. [11] QIAO F, LIANG X Z. Effects of cumulus parameterization closures on simulations of summer precipitation over the United States coastal oceans[J]. Journal of Advances in Modeling Earth Systems, 2016, 8(2): 1-23. [12] QIAO F, LIANG X Z. Effects of cumulus parameterization closures on simulations of summer precipitation over the continental United States[J]. Climate Dyn, 2017, 49: 225-247. https://doi.org/10.1007/s00382-016-3338-6 [13] YUAN X, LIANG X Z. Improving cold season precipitation prediction by the nested CWRF-CFS system[J]. Geophys Res Lett, 2011, 38(2): 79-89. https://doi.org/10.1029/2010GL046104 [14] CHEN L, LIANG X Z, DEWITT D, et al. Simulation of seasonal US precipitation and temperature by the nested CWRF-ECHAM system[J]. Climate Dyn, 2016, 46(3 /4): 879-896. [15] 刘术艳. CWRF在中国东部季风区的应用[D].南京: 南京信息工程大学, 2006. [16] LIANG X Z, SUN C, ZHENG X, et al. CWRF performance at downscaling China climate characteristics[J]. Climate Dyn, 2019, 52: 2 159-2 184. https://doi.org/10.1007/s00382-018-4257-5 [17] 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[J].大气科学, 2017, 41(6): 1 156-1 166. [18] 周林, 潘婕, 张镭, 等.气候模拟日降水量的统计误差订正分析——以上海为例[J].热带气象学报, 2014, 30(1): 137-144. [19] 杨浩, 江志红, 李肇新, 等.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. [20] YANG W, JOHAN A, PHIL G L, et al. Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impact studies[J]. Hydrology Research, 2010, 41(3-4): 211-229. [21] BURGER G, MURDOCK T Q, WERNER A T, et al. Downscaling extremes-an intercomparison of multiple statistical methods for present climate[J]. J Climate, 2012, doi:10.1175/JCLI-D-11-00408.1. [22] GUDMUNDSSON L, BREMNES J B, HAUGEN J E, et al. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations-a comparison of methods[J]. Hydrology and Earth System Sciences, 2012, 16: 3 383-3 390, doi:10.5194/hess-16-3383-2012. [23] LUKAS G. Statistical Transformations for Post-Processing Climate Model Output[DB/OL]. https: //CRAN.R-project.org/package=qmap. [24] LAFON T, DADSON S. BUYS G, et al. Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods[J]. Int J Climatol, 2013, 33(6): 1 367-1 381. [25] 童尧.不同误差订正方法在中国区域气候模拟中的比较和应用[D].北京: 中国气象科学研究院, 2017. [26] LIANG X Z, XU M, YUAN X, et al. Michalakes J Regional Climate-weather research and forecasting model(CWRF)[J]. Bull Amer Meteor Soc, 2012, 93:1 363-1 387. https://doi.org/10.1175/BAMS-D-11-00180.1 [27] TAYLOR K E. Summarizing multiple aspects of model performance in a single diagram[J]. J Geophys Res, 2001, 106(D7): 7 183-7 192. [28] 梁玉莲, 延晓冬. RCPs情景下中国21世纪气候变化预估及不确定性分析[J].热带气象学报, 2016, 32(2): 183-192. [29] 张宏芳, 潘留杰, 卢珊, 等. ECMWF集合预报系统对秦岭周边地区降水确定性预报的性能分析[J].气候与环境研究, 2017, 22(5): 551-562. [30] 梁巧倩, 蒙伟光, 孙喜艳, 等.广东前汛期锋面强降水和后汛期季风强降水特征对比分析[J].热带气象学报, 2019, 35(1):51-62. [31] 刘楚薇, 饶建, 吴志文, 等. ENSO与中国夏季降水的联系:冬季QBO的调制作用[J].热带气象学报, 2019, 35(2): 210-223. [32] 王欢, 李栋梁.气候变暖背景下全球海温对中国东部夏季降水年代际转折的影响[J].热带气象学报, 2019, 35(3):398-408 [33] 李俊, 杜钧, 陈超君. "频率匹配法"在集合降水预报中的应用研究[J].气象, 2015, 41(6): 674-684. -