设为首页
加入收藏
Deviation Distribution Features of CMA-GFS Cloud Prediction
-
摘要: 利用2021年3月—2022年2月ERA5再分析数据云量、云水凝物对中国气象局研发的全球数值预报系统CMA-GFS同期云量产品和由云量、云水凝物产品计算的云发生、云水凝物积分的偏差特征进行诊断评估, 初步探讨了CMA-GFS云预报偏差存在的可能原因。结果显示:CMA-GFS云量、云水凝物的分布较为合理, CMA-GFS能够描绘全球云量、云水凝物的分布特征, 并能够反映季节特征;CMA-GFS预报高云和中云的云量偏差大于低云的云量偏差, 而高云和中云的云量均方根误差较低云偏小, 说明模式高云和中云的预报稳定性优于低云;与ERA5再分析数据相比, CMA-GFS液相水凝物积分以负偏差为主, 冰相水凝物积分以正偏差为主;云量、云水凝物的偏差在不同地区成因不同, 在热带地区的偏差与对流参数化和微物理方案不协调有关, 在南北半球中高纬度地区的偏差与相对湿度偏差相关。Abstract: Clouds play a vital role in weather, climate system and the atmospheric water cycle. The diagnosis and evaluation of numerical model prediction results is important for numerical model research and development. Reasonable diagnosis and evaluation methods can not only provide references for model researchers to optimize model schemes, but also help users understand the performance of model prediction. The cloud characteristics of different regions and seasons should be considered for evaluation because the attributes in different regions are markedly different. The performance and deviation characteristics of the operational CMA-GFS of four seasons are evaluated, based on the reanalysis of ERA5 reanalysis data from March 2021 to February 2022. The frequency bias of cloud occurrence, cloud fraction, integrated cloud hydrometeors from various levels, and the bias and root mean square error of those variables are carefully diagnosed and evaluated via different methods. The deviation characteristics of cloud are emphatically analyzed according to different regions. The possible causes for the significant difference of cloud prediction deviation characteristics at different levels in different regions are preliminarily discussed. The results show that the overall distribution of cloud predicted by CMA-GFS is reasonable, which can describe the meridional peak and valley distribution characteristics of global cloud and reflect the seasonal trend. The cloud amount deviation of high cloud and medium cloud by CMA-GFS is greater than that of low cloud. The root mean square error of cloud amount of high cloud and medium cloud is smaller than that of low cloud. And the model stability for high cloud and medium cloud prediction is also better. The liquid-phased hydrometers integration is mainly negative deviation, and the ice-phased hydrometers integration is mainly positive deviation. The causes of the deviation of cloud predicted by CMA-GFS are different in different regions. In tropical region the deviation is related to the incongruity of convective parameterization and microphysical schemes, while in middle and high latitudes regions the deviations are related to the bias of relative humidity. It also shows that the diagnosis of model cloud features will cover up the actual problems only by a single method, and it needs to be evaluated comprehensively by combining a variety of methods.
-
Key words:
- CMA-GFS;
- cloud occurrence;
- cloud fraction;
- cloud hydrometeor integration
-
图 3 CMA-GFS云量产品与ERA5再分析数据云量经向分布
Fig. 3 Meridional mean cloud fraction from CMA-GFS and ERA5 reanalysis data
图 4 CMA-GFS云发生与云量综合评估
Fig. 4 Evaluation of CMA-GFS cloud occurrence and cloud fraction prediction skill
图 7 季节平均CMA-GFS水凝物积分与ERA5再分析数据水凝物积分经向分布
Fig. 7 Meridional distribution of seasonal mean cloud hydrometeor integration from CMA-GFS and ERA5 reanalysis data
图 8 水凝物积分偏差与均方根误差评估
Fig. 8 Evaluation of the seasonal mean cloud hydrometeors integration bias and root mean square error prediction skill
-
[1] Stephens G L.Cloud feedbacks in the climate system:A critical review.J Climate, 2005, 18(2):237-273. doi: 10.1175/JCLI-3243.1 [2] Morrison H, Gettelman A, Steven J G. A new two-moment bulk stratiform cloud microphysics scheme in the community atmosphere model, version 3(CAM3). Part Ⅱ: Single-column and global results. J Climate, 2008, 21(15): 3660-3679. doi: 10.1175/2008JCLI2116.1 [3] Hahn C J, Warren S G. A Gridded Climatology of Clouds over Land (1971-96) and Ocean (1954-97) from Surface Observations Worldwide. Numeric Data Package NDP-026E ORNL/CDIAC-153, CDIAC, Department of Enery, Oak Ridge, Tennessee, 2007. [4] Stephens G L, Vane D G, Boain R J, et al. The cloudsat mission and the A-train: A new dimension of space-based observations of clouds and precipitation. Bull Amer Meteor Soc, 2002, 83(12): 1771-1790. doi: 10.1175/BAMS-83-12-1771 [5] 尹金方, 王东海, 翟国庆. 区域中尺度模式云微物理参数化方案特征及其在中国的适用性. 地球科学进展, 2014, 29(2): 238-249. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201402005.htmYin J F, Wang D H, Zhai G Q. A study of characteristics of the cloud microphysical parameterization schemes in mesoscale models and its applicability to China. Adv Earth Sci, 2014, 29(2): 238-249. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201402005.htm [6] 邓涛, 张镭, 陈敏, 等. 高云和气溶胶辐射效应对边界层的影响. 大气科学, 2010, 34(5): 979-987. doi: 10.3878/j.issn.1006-9895.2010.05.12Deng T, Zhang L, Chen M, et al. The influence of high cloud and aerosol radiative effect on boudary layer. Chinese J Atmos Sci, 2010, 34(5): 979-987. doi: 10.3878/j.issn.1006-9895.2010.05.12 [7] Wilson R J, Lewis S R, Montabone L, et al. Influence of water ice clouds on Martian tropical atmospheric temperatures. Geophys Res Lett, 2008, 35(7): L07202. doi: 10.1029/2007GL032405/pdf [8] Gates W L, Boyle J S, Covey C, et al. An overview of the results of the atmospheric model intercomparison project (AMIPI). Bull Amer Meteor Soc, 1999, 80(1): 29-55. doi: 10.1175/1520-0477(1999)080<0029:AOOTRO>2.0.CO;2 [9] Jacob D, Van den Hurk B J J M, Andræ U, et al. A comprehensive model intercomparison study investigating the water budget during the BALTEX-PIDCAP period. Meteor Atmos Phys, 2001, 77: 19-43. doi: 10.1007/s007030170015 [10] Klein S, Jakob C. Validation and sensitivities of frontal clouds simulated by the ECMWF model. Mon Wea Rev, 1999, 127(10): 2514-2531. doi: 10.1175/1520-0493(1999)127<2514:VASOFC>2.0.CO;2 [11] Solomon S, Qin D, Manninget M, et al. Climiate Change 2007: The Physical Sciences Basis. Cambridge: Cambriged University Press, 2007. [12] Cotton W R. Numerical simulation of precipitation development in supercooled cumuli-Part I. Mon Wea Rev, 1972, 100(11): 757-763. doi: 10.1175/1520-0493(1972)100<0757:NSOPDI>2.3.CO;2 [13] Orville H D, Kopp F J. Numerical simulation of the life history of a hailstorm. J Atmos Sci, 1977, 34(10): 1596-1618. doi: 10.1175/1520-0469(1977)034<1596:NSOTLH>2.0.CO;2 [14] Takahashi T. Hail in an axisymmetric cloud model. J Atmos Sci, 1976, 33(8): 1576-1601. [15] Paluch I R. Size sorting of hail in a three-dimensional updraft and implications for hail suppression. J Appl Meteor, 1978, 17(6): 763-777. doi: 10.1175/1520-0450(1978)017<0763:SSOHIA>2.0.CO;2 [16] Raymond D J, Blyth A M. Precipitation development in a New Mexico thunderstorm. Quart J Roy Meteor Soc, 1989, 115(490): 1397-1423. doi: 10.1002/qj.49711549011 [17] Sokol Z, Zacharov P, Skripnikova K. Simulation of the storm on 15 August, 2010, using a high resolution COSMO NWP model. Atmos Res, 2014, 137: 100-111. doi: 10.1016/j.atmosres.2013.09.015 [18] Hong S Y, Dudhia J, Chen S H. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Wea Rev, 2004, 132(1): 103-120. doi: 10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2 [19] Morrison H, Curry J A, Khvorostyanov V I. A new double-moment microphysics parameterization for application in cloud and climate models. Part Ⅰ: Description. J Atmos Sci, 2005, 62(6): 1665-1677. doi: 10.1175/JAS3446.1 [20] Thompson G, Field P R, Rasmussen R M, et al. Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part Ⅱ: Implementation of a new snow parameterization. Mon Wea Rev, 2008, 136(12): 5095-5115. doi: 10.1175/2008MWR2387.1 [21] Lim K S S, Hong S Y. Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon Wea Rev, 2010, 138(5): 1587-1612. doi: 10.1175/2009MWR2968.1 [22] 胡志晋, 严采蘩. 层状云微物理过程的数值模拟(一)——微物理模式. 气象科学研究院院刊, 1986, 1(1): 37-52. https://www.cnki.com.cn/Article/CJFDTOTAL-YYQX198601005.htmHu Z J, Yan C F. Numerical simulation of microphysical processes in stratiform clouds (I)-Microphysical model. J Academy Meter Sci, 1986, 1(1): 37-52. https://www.cnki.com.cn/Article/CJFDTOTAL-YYQX198601005.htm [23] 胡志晋, 娄小凤, 包绍武, 等. 一个简单的混合相云降水显示方案. 应用气象学报, 1998, 9(3): 257-264. http://qikan.camscma.cn/article/id/19980338Hu Z J, Lou X F, Bao S W, et al. A simplifed explicit scheme of phase mixed cloud and precipitation. J Appl Meteor Sci, 1998, 9(3): 257-264. http://qikan.camscma.cn/article/id/19980338 [24] 徐焕斌, 段英. 云粒子谱演化研究中的一些问题. 气象学报, 1999, 57(4): 451-460. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB199904005.htmXu H B, Duan Y. Some questions in studying the evolution of size distribution spectaum of hydrometeor paricels. Acta Meteor Sinica, 1999, 57(4): 451-460. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB199904005.htm [25] 孙晶, 楼小凤, 胡志晋, 等. CAMS复杂云微物理方案与GRA-PES模式耦合的数值试验. 应用气象学报, 2008, 19(3): 315-325. doi: 10.3969/j.issn.1001-7313.2008.03.007Sun J, Lou X F, Hu Z J, et al. Numerical experiment of the coupling of CAMS complex microphysical scheme and GRAPES model. J Appl Meteor Sci, 2008, 19(3): 315-325. doi: 10.3969/j.issn.1001-7313.2008.03.007 [26] 胡志晋, 何观芳. 积雨云微物理过程的数值模拟(一)——微物理模式. 气象学报, 1987, 45(4): 467-484. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB198704011.htmHu Z J, He G F. Numerical simulation of micro processes in cumulonimbus clouds (I) microphysical model. Acta Meteor Sinica, 1987, 45(4): 467-484. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB198704011.htm [27] 刘奇俊, 胡志晋, 周秀骥. HLAFS显式云降水方案及其对暴雨和云的模拟(I)云降水显示方案. 应用气象学报, 2003, 14(增刊Ⅰ): 60-66. https://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2003S1007.htmLiu Q J, Hu Z J, Zhou X J. Explicit cloud schemes of hlafs and simulation of heavy rainfall and clouds, Part I: Explicit cloud schemes. J Appl Meteor Sci, 2003, 14(Suppl Ⅰ): 60-66. https://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2003S1007.htm [28] 陈雪娇, 刘奇俊, 马占山. GRAPES全球模式云方案的诊断研究. 气象学报, 2021, 79(1): 65-78. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202101005.htmChen X J, Liu Q J, Ma Z S. A diagnostic study of cloud scheme for the GRAPES global forecast model. Acta Meteor Sinica, 2021, 79(1): 65-78. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202101005.htm [29] 潘留杰, 张宏芳, 王建鹏. 数值天气预报检验方法研究进展. 地球科学进展, 2014, 29(3): 327-335. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201403005.htmPan L J, Zhang H F, Wang J P. Progress on verification methods of numerical weather prediction. Adv Earth Sci, 2014, 29(3): 327-335. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201403005.htm [30] Koh T Y, Bhatt B C, Cheung K K W, et al. Using the spectral scaling exponent for validation of quantitative precipitation forecasts. Meteor Atmos Phys, 2012, 115: 35-45. doi: 10.1007/s00703-011-0166-4 [31] Tiedtke M. Representation of clouds in large-scale models. Mon Wea Rev, 1993, 121(11): 3040-3061. doi: 10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2 [32] Li J L, Jiang J H, Waliser D E, et al. Assessing consistency between COS MLS and ECMWF analyzed and forecast estimates of cloud ice. Geophys Res Lett, 2007, 34(L08): L08701. http://www.onacademic.com/detail/journal_1000035772159510_f168.html [33] 郭学良, 付丹红, 郭欣, 等. 我国云降水物理飞机观测研究进展. 应用气象学报, 2021, 32(6): 641-652. doi: 10.11898/1001-7313.20210601Guo X L, Fu D H, Guo X, et al. Advances in aircraft measurements of clouds and precipitation in China. J Appl Meteor Sci, 2021, 32(6): 641-652. doi: 10.11898/1001-7313.20210601 [34] Illingworth A J, Hogan R J, Connor E J, et al. Cloudnet: Continuous evaluation of cloud profiles in seven operational models using ground-based observations. Bull Amer Meteor Soc, 2007, 88(6): 883-898. doi: 10.1175/BAMS-88-6-883 [35] Tao W K, Chern J D, Atlas R, et al. A multiscale modeling system developments, applications, and critical issues. Amer Meteor Soc, 2009, 90(4): 515-534. doi: 10.1175/2008BAMS2542.1 [36] 韩丰, 杨璐, 周楚炫, 等. 基于探空数据集成学习的短时强降水预报试验. 应用气象学报, 2021, 32(2): 188-199. doi: 10.11898/1001-7313.20210205Han F, Yang L, Zhou C X, et al. An experimental study of the short-time heavy rainfall event forecast based on ensemble learning and sounding data. J Appl Meteor Sci, 2021, 32(2): 188-199. doi: 10.11898/1001-7313.20210205 [37] 谭超, 刘奇俊, 马占山. GRAPES全球模式次网格对流过程对云预报的影响研究. 气象学报, 2013, 71(5): 867-878. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201305006.htmTan C, Liu Q J, Ma Z S. Influences of sub-grid convective processes on cloud forecast in the GRAPES global model. Acta Meteor Sinica, 2013, 71(5): 867-878. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201305006.htm [38] 马占山, 刘奇俊, 秦琰琰. GRAPES_GFS不同湿物理过程对云降水预报性能的诊断与评估. 高原气象, 2016, 35(4): 989-1003. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201604014.htmMa Z S, Liu Q J, Qin Y Y. Validation and evaluation of cloud and precipitation forecast performance by different moist physical processes schemes in GRAPES_GFS Model. Plateau Meteor, 2016, 35(4): 989-1003. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201604014.htm [39] Arbizu-Barrena C, Pozo-Vazquez D, Ruiz-Arias J A, et al. Macroscopic cloud properties in the WRF NWP model: An assessment using sky camera and ceilometer data. J Geophys Res Atmos, 2015, 120(19): 10297-10312. http://smartsearch.nstl.gov.cn/paper_detail.html?id=521dd00a8a280f9f1da41f1387a0c215 [40] World Meteorological Organization/World Weather Research Programme (WMO/WWRP). Recommended Methods for Evaluating Cloud and Related Parameters World Weather Research Programme (WWRP)/Working Group on Numerical Experimentation (WGNE) Joint Working Group on Forecast Verification Research (JWGFVR), Document WWRP 2012-1, 2012. [41] Yuter S E, Houze J R R A. Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part Ⅱ: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity. Mon Wea Rev, 1995, 123(7): 1941-1963. doi: 10.1175/1520-0493(1995)123<1941:TDKAME>2.0.CO;2 [42] Morcrette J J. Evaluation of model-generated cloudiness: Satellite-observed and model-generated diurnal variability of brightness temperature. Mon Wea Rev, 1991, 119(5): 1205-1224. doi: 10.1175/1520-0493(1991)119<1205:EOMGCS>2.0.CO;2 [43] Atger F. Verification of intense precipitation forecasts from single models and ensemble prediction systems. Nonlin Processes Geophys, 2001, 8(6): 401-417. doi: 10.5194/npg-8-401-2001 [44] Casati B, Ross G, Stephenson D B. A new intensity-scale approach for the verification of spatial precipitation forecasts. Meteor Appl, 2004, 11(2): 141-154. doi: 10.1017/S1350482704001239 [45] Keil C, Craig G C. A displacement-based error measure applied in a regional ensemble forecasting system. Mon Wea Rev, 2007, 135(9): 3248-3259. doi: 10.1175/MWR3457.1 [46] Keil C, Craig G C. A displacement and amplitude score employing an optical flow technique. Wea Forecasting, 2009, 24(5): 1297-1308. doi: 10.1175/2009WAF2222247.1 [47] Ferro C A T, Richardson D S, Weigel A P. On the effect of ensemble size on the discrete and continuous ranked probability scores. Meteor Appl, 2008, 15(1): 19-24. doi: 10.1002/met.45 [48] 何光碧, 张利红, 屠妮妮. 区域中尺度模式对西南地区一次强降水过程的预报分析. 高原山地气象研究, 2014, 34(2): 1-7. doi: 10.3969/j.issn.1674-2184.2014.02.001He G B, Zhang L B, Tu N N. Analyses on a heavy rainfall process prediction of regional numerical models. Plateau Mountain Meteor Res, 2014, 34(2): 1-7. doi: 10.3969/j.issn.1674-2184.2014.02.001 [49] 许建伟, 高艳红. WRF模式对夏季黑河流域气温和降水的模拟及检验. 高原气象, 2014, 33(4): 937-946. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201404007.htmXu J W, Gao Y H. Validation of summer surface air temperature and precipitation simulation over Heihe River Basin. Plateau Meteor, 2014, 33(4): 937-946. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201404007.htm [50] 彭新东, 常燕, 王式功. GRAPES模式对2008年两次强降水过程的数值预报检验. 高原气象, 2010, 29(2): 321-330. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201002008.htmPeng X D, Chang Y, Wang S G. Numerical validation of GRAPES model with two severe precipitation processes in 2008. Plateau Meteor, 2010, 29(2): 321-330. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201002008.htm [51] 张小雯, 唐文苑, 郑永光, 等. GRAPES_3 km数值模式对流风暴预报能力的多方法综合评估. 气象, 2020, 46(3): 367-380. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202003008.htmZhang X W, Tang W Y, Zheng Y G, et al. Comprehensive evaluations of GRAPES_3 km numerical model in forecasting convective storms using various verification methods. Meteor Mon, 2020, 46(3): 367-380. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202003008.htm [52] 徐双柱, 张兵, 谌伟. GRAPES模式对长江流域天气预报的检验分析. 气象, 2007, 33(11): 65-71. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200711011.htmXu S Z, Zhang B, Shen W. Forecasting verification of GRAPES model in the reaches of Changjiang River. Meteor Mon, 2007, 33(11): 65-71. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200711011.htm [53] 叶成志, 欧阳里程, 李象玉. GRAPES中尺度模式对2005年长江流域重大灾害性降水天气过程预报性能的检验分析. 热带气象学报, 2006, 22(4): 393-399. doi: 10.3969/j.issn.1004-4965.2006.04.012Ye C Z, Ouyang L C, Li X Y. Validation of 2005 heavy rain events over the Yangtze River Basin forecast by GRAPES. J Trop Meteor, 2006, 22(4): 393-399. doi: 10.3969/j.issn.1004-4965.2006.04.012 [54] 常婉婷, 高文华, 端义宏, 等. 云微物理过程对台风数值模拟的影响. 应用气象学报, 2019, 30(4): 443-455. doi: 10.11898/1001-7313.20190405Chang W T, Gao W H, Duan Y H, et al. The impact of cloud microphysical processes on typhoon numerical simulation. J Appl Meteor Sci, 2019, 30(4): 443-455. doi: 10.11898/1001-7313.20190405 [55] 周祖刚, 谈哲敏, 张熠, 等. 模式湿物理过程的组合对一次南京大暴雨降水模拟的影响分析. 南京大学学报(自然科学版), 2011, 47(4): 481-492. https://www.cnki.com.cn/Article/CJFDTOTAL-NJDZ201104019.htmZhou Z G, Tan Z M, Zhang Y, et al. The impact of combination of model moist physics process on numerical simulation of a Nanjing heavy rainfall event. J Nanjing Univ(Nat Sci Ed), 2011, 47(4): 481-492. https://www.cnki.com.cn/Article/CJFDTOTAL-NJDZ201104019.htm [56] 胡鹏, 赵震, 雷恒池, 等. 河南省春季一次层状云降水云系结构和降水机制的数值模拟. 高原气象, 2009, 28(2): 374-384. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200902015.htmHu P, Zhao Z, Lei H C, et al. Numerical simulation of cloud system structure and precipitation mechanism of stratiform precipitation in spring of Henan Province. Plateau Meteor, 2009, 28(2): 374-384. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX200902015.htm [57] 马占山, 刘奇俊, 秦琰琰, 等. 利用TRMM卫星资料对人工增雨云系模式云微观场预报能力的检验. 气象学报, 2009, 67(2): 260-271. doi: 10.3321/j.issn:0577-6619.2009.02.009Ma Z S, Liu Q J, Qin Y Y, et al. Verification of forecasting efficiency to cloud microphysical characters of mesoscale numerical model for artificial rainfall enhancement by using TRMM satellite data. Acta Meteor Sinica, 2009, 67(2): 260-271. doi: 10.3321/j.issn:0577-6619.2009.02.009 [58] 孙晶, 楼小凤, 史月琴. 不同微物理方案对一次梅雨锋暴雨过程模拟的影响. 气象学报, 2011, 69(5): 799-809. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201105005.htmSun J, Lou X F, Shi Y Q. The effects of different microphysical schemes on the simulation of a Meiyu front heavy rainfall. Acta Meteor Sinica, 2011, 69(5): 799-809. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB201105005.htm [59] Liu K, Chen Q Y, Sun J. Modification of cumulus convection and planetary boundary layer schemes in the GRAPES global model. J Meteor Res, 2015, 29(5): 806-822. doi: 10.1007/s13351-015-5043-5 [60] 陈炯, 马占山, 苏勇. 适用于GRAPES模式C-P边界层方案的设计和实现. 应用气象学报, 2017, 28(1): 52-61. doi: 10.11898/1001-7313.20170105Chen J, Ma Z S, Su Y. Boundary layer coupling to Charney-Phillips vertical grid in GRAPES Model. J Appl Meteor Sci, 2017, 28(1): 52-61. doi: 10.11898/1001-7313.20170105 [61] 沈学顺, 苏勇, 胡江林, 等. GRAPES_GFS全球中期预报系统的研发和业务化. 应用气象学报, 2017, 28(1): 1-10. doi: 10.11898/1001-7313.20170101Shen X S, Su Y, Hu J L, et al. Development and operation transformation of GRAPES global middle-range forecast system. J Appl Meteor Sci, 2017, 28(1): 1-10. doi: 10.11898/1001-7313.20170101 [62] Chen J, Ma Z S, Li Z, et al. Vertical diffusion and cloud scheme coupling to the Charney-Phillips vertical grid in GRAPES global forecast system. Quart J Roy Meteor Soc, 2020, 146(730): 2191-2204. doi: 10.1002/qj.3787 [63] 张萌, 于海鹏, 黄建平, 等. GRAPES_GFS 2.0模式非系统误差评估. 应用气象学报, 2019, 30(3): 332-344. doi: 10.11898/1001-7313.20190307Zhang M, Yu H P, Huang J P, et al. Assessment on unsystematic errors of GRAPES_GFS 2.0. J Appl Meteor Sci, 2019, 30(3): 332-344. doi: 10.11898/1001-7313.20190307 [64] Hersbach H, Bell B, Berrisford P, et al. The ERA5 global reanalysis. Quart J Roy Meteor Soc, 2020, 145(722): 1882-1896. http://www.researchgate.net/publication/341448930_The_ERA5_global_reanalysis [65] Ma Z, Liu Q, Zhao C, et al. Application and evaluation of an explicit prognostic cloud-cover scheme in GRAPES global forecast system. J Adv Mod Earth Sys, 2018, 10(3): 652-667. doi: 10.1002/2017MS001234 [66] Ma Z S, Zhao C, Gong C, et al. Spin-up characteristics with three types of initial fields and the restart effects on forecast accuracy in the GRAPES global forecast system. Geosci Model Dev, 2020, 14(1): 205-221. http://www.xueshufan.com/publication/3048719362 [67] 刘帅, 王建捷, 陈起英, 等. GRAPES_GFS模式全球降水预报的主要偏差特征. 气象学报, 2021, 79(2): 255-281. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202102007.htmLiu S, Wang J J, Chen Q Y, et al. The main characteristics of forecast deviation in global precipitation by GRAPES_GFS. Acta Meteor Sinica, 2021, 79(2): 255-281. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202102007.htm -
下载:
图(9)
计量
- 摘要浏览量: 1327
- HTML全文浏览量: 272
- PDF下载量: 176
- 被引次数: 0
