Validation and Evaluation of Cloud and Precipitation Forecast Performance by Different Moist Physical Processes Schemes in GRPAES_GFS Model

  • MA Zhanshan ,
  • LIU Qijun ,
  • QIN Yanyan
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  • National Meteorological Center, Beijing 100081, China;2. Numerical Weather Prediction Center of China Meteorological Administration, Beijing 100081, China;3. National Marine Environment Forecast Center, Beijing 100081, China

Received date: 2014-12-31

  Online published: 2016-08-28

Abstract

Cumulus convection schemes and cloud microphysical schemes are the most important moist physical processes in numerical weather model, which have significant effects on cloud and precipitation forecast performance. Cloud and precipitation forecast performances by different combinations between the above precipitation processes parameterization schemes in GRAPES_GFS model have been diagnosed and validated using CMAP precipitation observation data and MODIS, MLS and CloudSat satellite data. The results show that: (1) The distinction of cloud microphysical schemes is a predominant reason for precipitation forecast difference at mid-high latitude areas. There is a sequence of SI > NCEP3 > NCEP5 according to the precipitation intensity of order. While the distinction of cumulus convection schemes is the main reason for those at low latitude areas. (2) There is more grid-scale precipitation of SI and NCEP3 schemes than that of NCEP5 scheme at middle latitude areas. Compared with SAS and KF scheme, BM scheme is prone to causing more grid-scale precipitation with its combinations partner cloud scheme. (3) There is obviously more convection precipitation of BM scheme than those of SAS and KF schemes. The convection precipitation magnitude of SAS scheme and KF scheme is very approximate at mid-high latitude areas, nevertheless, that of SAS scheme is the least among the three convection schemes at low latitude areas. (4) The cloud top temperature of NCEP5 is in good agreement with MODIS satellite data, while those of NCEP3 scheme and SI scheme are warmer than MODIS. There is a sequence of SAS < BM < KF from cold to warm for their cloud top temperature. (5) The integration cloud water content of NCEP5 is closest to MODIS observation and there are significantly less for the two simple ice microphysical schemes, especially for SI scheme. KF scheme has an obvious better performance for integration cloud water prediction than those of SAS and BM scheme. (6) There are all negative biases of cirrus cloud compared with MLS observation for three combinations. The comprehensive mixed phase cloud scheme NCEP5 has an slightly advantage with cirrus cloud forecast than that of simple ice phase scheme. (7) In terms of global average, the combination KF convection scheme and NCEP5 cloud scheme have better performances for cloud and precipitation forecast among all these precipitation processes parameterization schemes.

Cite this article

MA Zhanshan , LIU Qijun , QIN Yanyan . Validation and Evaluation of Cloud and Precipitation Forecast Performance by Different Moist Physical Processes Schemes in GRPAES_GFS Model[J]. Plateau Meteorology, 2016 , 35(4) : 989 -1003 . DOI: 10.7522/j.issn.1000-0534.2015.00063

References

[1]Arakawa A, Schubert W H.1974.Interaction of a cumulus cloud ensemble with the large-scale environment, Part I[J].J Atmos Sci, 31(3):674-701.
[2]Berg L K, Gustafson W I, Kassianov E I, et al.2013.Evaluation of a modified scheme for shallow convection:Implementation of CuP and case studies[J].Mon Wea Rev, 141(1):134-147.
[3]Betts A K, Miller M J.1986.A new convective adjustment scheme.Part I:Observational land theoretical basis[J].Quart J Roy Meteor Soc, 112(473):667-691.
[4]Byun Y H, Hong S Y.2007.Improvements in the subgrid-scale representation of moist convection in a cumulus parameteriation scheme:the single-column test and its impact on seasonal prediction[J].Mon Wea Rev, 135(6):2135-2154.
[5]Gates W L, Boyle J S, Covey C, et al.1999.An overview of the results of the atmospheric model intercomparison project (AMIP I)[J].Bull Amer Meteor Soc, 80(1):29-55.
[6]Hartmann D L, Ockert-Bell M E, Michelsen M L.1992.The effect of cloud type on earth's energy balance:global analysis[J].J Climate, 5(11):1281-1304.
[7]Hirakata M, Okamoto H, Hagihara Y, et al.2014.Comparison of global and seasonal characteristics of cloud phase and horizontal ice plates derived from CALIPSO with MODIS and ECMWF[J].J Atmos Ocea Technol, 31(10):2114-2130.
[8]Illingworth A J, Hogan R J, Connor E J, et al.2007.Cloudnet:Continuous evaluation of cloud profiles in seven operational models using ground-based observations[J].Bull Amer Meteor Soc, 88(6):883-898.
[9]Jacob D, Van den Hurk B J J M, Andra U, et al.2001.A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period[J].Meteor Atmos Phys, 77(1-4):19-43.
[10]Kain J S, Fritsch J M.1990.A one-dimensional entraining/detraining plume model and its application in convective parameterization[J].J Atmos Sci, 47(23):2784-2802.
[11]Klein S, Jakob C.1999.Validation and sensitivities of frontal clouds simulated by the ECMWF model[J].Mon Wea Rev, 127(10):2514-2531.
[12]Li J-L, Jiang J H, Waliser D E, et al.2007.Assessing consistency between EOS MLS and ECMWF analyzed and forecast estimates of cloud ice[J].Geophys Res Lett, 34(L08):L08701.DOI:10.1029/2006GL029022.
[13]Livesey N J.2005.EOS MLS version 2.2 level 2 data quality and description document[R].Jet Propulsion Laboratory, Pasadena, CA, 115.[http://mls.jpl.nasa.gov/data/v2-2_data_quality_document.pdf]
[14]Morcrette J J.1991.Evaluation of model-generated cloudiness:Satellite-observed and model-generated diurnal variability of brightness temperature[J].Mon Wea Rev, 119:1205-1224.
[15]Morrison H, Thompson G, Tatarskii V.2009.Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line:comparison of one- and two-moment schemes[J].Mon Wea Rev, 137(3):991-1007.
[16]Otkin J A, Greenwald T J.2008.Comparison of WRF model-simulated and modis-derived cloud data[J].Mon Wea Rev, 136(6):1957-1970.
[17]Rossow W B, Lacis A A.1990.Global, seasonal cloud variations from satellite radiance measurements.Part II:Cloud properties and radiative effects[J].J Climate, 3(11):1204-1253.
[18]Solomon S, Qin D, Manning M, et al.2007.Climate Change 2007:the physical sciences basis[M].Cambridge:Cambridge University Press.
[19]Stoelinga M T, Hobbs P V, Mass C F, et al.2003.Improvement of microphysical parameterization through observational verification experiment[J].Bull Amer Meteor Soc, 84(12):1807-1826.
[20]Tao W K, Chern J D, Atlas R, et al.2009.A multiscale modeling system developments, applications, and critical issues[J].Amer Meteor Soc, 90(4):515-534.
[21]Tiedtke M.1993.Representation of clouds in large-scale models[J].Mon Wea Rev, 121(11):3040-3061.
[22]Tremblay A, Glazer A, Garand L, et al.2001.Comparison of three cloud schemes in winter storm forecasts[J].Mon Wea Rev, 129(12):2923-2938.
[23]Tremblay A, Vaillancourt P A, Cober S G, et al.2003.Improvements of a mixed-phase cloud scheme using aircraft observations[J].Mon Wea Rev, 131(4):672-686.
[24]Wilkinson J M, Hogan R J, Illingworth A J, et al.2008.Use of a lidar forward model for global comparisons of cloud fraction between the ICESat Lidar and the ECMWF Model[J].Mon Wea Rev, 136(10):3742-3759.
[25]Xie P, Arkin P A.1997.Global precipitation:A 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs[J].Bull Amer Meteor Soc, 78(11):2539-2558.
[26]Yuter S E, Houze JR R A.1995.Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus.Part II:Frequency distributions of vertical velocity, reflectivity, and differential reflectivity[J].Mon Wea Rev, 123(7):1941-1963.
[27]陈德辉, 薛纪善, 杨学胜, 等.2008.GRAPES新一代全球/区域多尺度统一数值预报模式总体设计研究[J].科学通报, 53(20):2396-2407.Chen Dehui, Xue Jishan, Yang Xuesheng, et al.2008.The design of a new Global-Regional Assimilation and Prediction System-GRAPES[J].Chinese Science Bulletin, 53(20):2396-2407.
[28]胡鹏, 赵震, 雷恒池, 等.2009.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[J].高原气象, 28(2):374-384.Hu Peng, Zhao Zhen, Lei Hengchi, et al.2009.Numerical simulation of cloud system structure and precipitation mechanism of stratiform precipitation in spring of Henan Province[J].Plateau Meteor, 28(2):374-384.
[29]廖洞贤, 柳崇健.1995.数值天气预报中的若干新技术[M].北京:气象出版社, 334-338.Liao Dongxian, Liu Chongjian.1995.Many new methods and technologies in numerical weather forecast[M].Beijing:China Meteorological Press, 334-338.
[30]刘奇俊, 胡志晋, 周秀骥.2003.HLAFS显式云降水方案及其对暴雨和云的模拟(I)云降水显式方案[J].应用气象学报, 14(增刊1):60-67.Liu Qijun, Hu Zhijin, Zhou Xiuji.2003.Explicit cloud schemes of HLAFS and simulation of heavy rainfall and clouds, Part I:Explicit cloud schemes[J].J of Appl Meteor Sci, 14(Suppl):60-67.
[31]马占山, 刘奇俊, 秦琰琰, 等.2009.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[J].气象学报, 67(2):260-271.Ma Zhanshan, Liu Qijun, Qin Yanyan, et al.2009.Verification of forecasting efficiency to cloud microphysical characters of mesoscale numerical model for artificial rainfall enhancement by using TRMM satellite data[J].Acta Meteor Sinica, 67(2):260-271.
[32]彭新东, 常燕, 王式功.2010.GRAPES模式对2008年两次强降水过程的数值预报检验[J].高原气象, 29(2):321-330.Peng Xindong, Chang Yan, Wang Shigong.2010.Numerical validation of GRAPES model with two severe precipitation processes in 2008[J].Plateau Meteor, 29(2):321-330.
[33]孙晶, 楼小凤, 史月琴.2011.不同微物理方案对一次梅雨锋暴雨过程模拟的影响[J].气象学报, 69(5):799-809.Sun Jing, Lou Xiaofeng, Shi Yueqin.2011.The effects of diffeent microphysical schemes on the simulation of a meiyu front heavy rainfall[J].Acta Meteor Sinica, 69(5):799-809.
[34]伍红雨, 陈静.2008.不同模式分辨率和物理过程方案对贵州降水影响的对比试验[J].高原气象, 27(6):1295-1306.Wu Hongyu, Chen Jing.2008.Comparison experiments of different resolutions and physical parameterization schemes in Guizhou precipitation[J].Plateau Meteor, 27(6):1295-1306.
[35]徐国强, 陈德辉, 薛纪善, 等.2008.GRAPES物理过程的优化试验及程序结构设计[J].中国科学, 53(20):2428-2434.Xu Guoqiang, Chen Dehui, Xue Jishan, et al.2008.Optimization experiment and program structure design of GRAPES physical processes[J].Science China (Technological Science), 53(20):2428-2434.
[36]许建伟, 高艳红.2014.WRF模式对夏季黑河流域气温和降水的模拟及检验[J].高原气象, 33(4):937-946.Xu Jianwei, Gao Yanhong.2014.Validation of summer surface air temperature and precipitation simulation over Heihe River Basin[J].Plateau Meteor, 33(4):937-946.DOI:10.7522/j.issn.1000-0534.2013.00149.
[37]薛纪善, 陈德辉, 沈学顺, 等.2008.数值预报系统GRAPES的科学设计与应用[M].北京:科学出版社.Xue Jishan, Chen Dehui, Shen Xueshun, et al.2008.The scientific design and application of numerical forecast system GRAPES[M].Beijing:Science Press.
[38]张利红, 何光碧.2014.GRAPES_Meso模式对2011年夏季青藏高原东部及周边区域的预报检验[J].高原气象, 33(1):14-25.Zhang Lihong, He Guangbi.2014.Validation for GRAPESG _Meso model in Eastern Qinghai-Xizang plateau and its surrounding in summer of 2011[J].Plateau Meteor, 33(1):14-25.Doi:10.7522/j.issn.1000-0534.2012.00175.
[39]张人禾, 沈学顺.2008.中国国家级新一代业务数值预报系统GRAPES的发展[J].科学通报, 53(20):2393-2395.Zhang Renhe, Shen Xueshun.2008.The development of new national numerical prediction operational system in China[J].Chinese Science Bulletin, 53(20):2393-2395.
[40]周祖刚, 谈哲敏, 张熠, 等.2011.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[J].南京大学学报, 47(4):481-492.Zhou Zugang, Tan Zhemin, Zhang Xi, et al.2011.The impact of combination of model moist physics process on numerical simulation of a Nanjing heavy rainfall event[J].J Nanjing University (Natural Sciences), 47(4):481-492.
[41]庄世宇, 薛纪善, 朱国富, 等.2005.GRAPES全球三维变分同化系统--基本设计方案与理想实验[J].大气科学, 29(6):872-884.Zhuang Shiyu, Xue Jishan, Zhu Guofu, et al.2005.GRAPES global 3D-Var system-basic scheme design and single observation test[J].Chinese J.Atmos Sci, 29(6):872-884.
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