Analyses of Simulation Result in Loess Plateau by WRF Model with Two Reanalysis Data

  • MA Chenchen ,
  • YU Ye ,
  • HE Jianjun ,
  • CHEN Xing ,
  • XIE Jin
Expand
  • Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Science, Lanzhou 73000, China;2. University of Chinese Academy of Science, Beijing 100049, China;3. Pingliang Station of Lightning and Hail Research, Chinese Academy of Sciences, Pingliang 744015, China

Received date: 2013-09-30

  Online published: 2014-06-28

Abstract

WRF is one of the most widely used numerical weather prediction models, for which reanalysis data provide initial and lateral boundary conditions. Different reanalysis products may affect WRF simulation results differently. Taking Loess Plateau as research area, two WRF experiments (NCEP/WRF and ERA/WRF), using surface observation data and sounding data, taking NCEP and ERA-Interim reanalysis as initial and boundary conditions, respectively, were conducted to evaluate the influence of different reanalysis products on WRF simulations. The results indicated that both experiments can accurately reproduce the diurnal variation of 2 m temperature, 2 m relative humidity and surface temperature, with ERA/WRF giving better results than NCEP/WRF. Due to the complex terrain in the studied area, model performance for 10 m wind speed was poor. Both experiments cauld approximately simulate the diurnal variation of radiation components and ground heat fluxes. The deviation from observations mainly appeared at noon. Both experiments captured the variation of potential temperature and specific humidity in the atmospheric boundary layer well with correlation coefficient higher than 0.8. However, the performance for wind speed was not as good, with correlation coefficients of 0.63 and 0.60, respectively, for NCEP/WRF and ERA/WRF experiments. NCEP/WRF performed better than ERA/WRF in the atmospheric boundary layer.

Cite this article

MA Chenchen , YU Ye , HE Jianjun , CHEN Xing , XIE Jin . Analyses of Simulation Result in Loess Plateau by WRF Model with Two Reanalysis Data[J]. Plateau Meteorology, 2014 , 33(3) : 698 -711 . DOI: 10.7522/j.issn.1000-0534.2014.00038

References

[1]Zhao T B, Fu C B. Comparison of products from ERA40 NCEP2 and CRU with station data for summer precipitation over China[J]. Adv Atmos Sci, 2006, 23(4): 593-604.
[2]李瑞青, 吕世华, 韩博, 等. 青藏高原东部三种再分析资料与地面气温观测资料的对比分析[J]. 高原气象, 2012, 31(6): 1488-1502.
[3]赵天保, 符淙斌. 应用探空观测资料评估几类再分析资料在中国区域的适用性[J]. 大气科学, 2009, 33(3): 634-648.
[4]赵天保, 符淙斌. 几种再分析地表气温资料在中国区域的适用性评估[J]. 高原气象, 2009, 28(3): 594-505.
[5]王毅荣. 黄土高原气候系统的基本特征[J]. 甘肃农业, 2004, 7: 12-13.
[6]Wen J, Wang L, Wei Z G. An overview of the Loess Plateau mesa region land surface process field experiment series(LOPEXs)[J]. Hydrol Earth Syst Sci, 2009, 13: 945-951.
[7]李耀辉, 韩涛. 基于EOS/MODIS 资料的中国黄土高原西部土地覆盖分类[J]. 高原气象, 2008, 27(3): 538-543.
[8]Zhang J T, Ru W M, Li B. Relationships between vegetation and climate on the Loess Plateau in China[J]. Folia Geobotanica, 2006, 41: 151-163.
[9]王毅荣. 黄土高原秋季气候对全球增暖的暖干化区域响应[J]. 高原气象, 2008, 27(1): 104-112.
[10]韦志刚, 文军, 吕世华, 等.黄土高原陆—气相互作用预试验及其晴天地表能量特征分析[J]. 高原气象, 2005, 24(4): 545-555.
[11]王兴, 张强, 王胜. 中国黄土高原半湿润地区陆面温、 湿特性及辐射收支特征研究[J]. 高原气象, 2013, 32(5): 1272-1279, doi: 10.7522/j.issn.1000-0534.2013.00058.
[12]Fan S J, Fan Q, Yu W, et al. Atmospheric boundary layer characteristics over the Pearl River Delta, China during summer 2006: measurement and model results[J]. Atmospheric Chemistry and Physics Discussions, 2011, 11(2): 4807-4842.
[13]董俊玲, 韩志伟, 张仁健, 等. WRF 模式对中国城市和半干旱地区气象要素的模拟检验和对比分析[J]. 气象科学, 2011, 31(4): 484-492.
[14]张宇, 郭振海, 张文煜, 等. 中尺度模式不同分辨率下大气多尺度特征模拟能力分析[J]. 大气科学, 2010, 34(3): 653-660.
[15]Ruiz J J, Saulo C, Nogués-Paegle J. WRF model sensitivity to choice of parameterization over south america: Validation against surface variables[J]. Mon Wea Rev, 2010, 138(8): 3342-3355.
[16]王腾蛟, 张镭, 胡向军, 等. WRF模式对黄土高原丘陵地形条件下夏季边界层结构的数值模拟[J]. 高原气象, 2013, 32(5): 1261-1271, doi: 10.7522/j.issn.1000-0534.2012.00121.
[17]Wilby R L, Dawson C W, Barrow E M. SDSM—A decision support tool for the assessment of regional climate change impacts[J]. Environmental Modelling & Software, 2002, 17(2): 145-157.
[18]Hong S Y, Noh Y, Dudhia J. A new vertical diffusion package with an explicit treatment of entrainment processes[J]. Mon Wea Rev, 2006, 134(9): 2318-2341.
[19]Dyer A J, Hicks B B. Flux-gradient relationships in the constant flux layer[J]. Quart J Roy Meteor Soc, 1970, 96(410): 715-721.
[20]Beljears A. The parametrization of surface fluxes in large-scale models under free convection[J]. Quart J Roy Meteor Soc, 1995, 121(522): 255-270.
[21]Chen F, Dudhia J. Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity[J]. Mon Wea Rev, 2001, 129(4): 569-585.
[22]Kain J S. The Kain-Fritsch convective parameterization: An update[J]. J Appl Meteor, 2004, 43(1): 170-181.
[23]Mlawer E J, Taubman S J, Brown P D, et al. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave[J]. J Geophys Res: Atmospheres (1984-2012), 1997, 102(D14): 16663-16682.
[24]Dudhia J. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model[J]. Atmos Sci, 1989, 46: 3077-3107.
[25]Lin Y L, Farley R D, Orville H D. Bulk parameterization of the snow field in a cloud model[J]. J Climate Appl Meteor, 1983, 22(6): 1065-1092.
[26]Hong S Y, Dudhia J, Chen S H. A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation[J]. Mon Wea Rev, 2004, 132(1): 103-120.
[27]刘蓉, 文军, 张堂堂, 等. 利用 MERIS和AATSR 资料估算黄土高原塬区蒸散发量研究[J]. 高原气象, 2008, 27(5): 949-955.
[28]韦志刚, 文军, 吕世华, 等. 黄土高原陆-气相互作用预试验及其晴天地表能量特征分析[J]. 高原气象, 2005, 24(4): 545-555.
[29]Hogrefe C, Rao S T, Kasibhatla P, et al. Evaluating the performance of regional-scale photochemical modeling systems: Part II-Ozone predictions[J]. Atmos Environ, 2001, 35(24): 4175-4188.
[30]Zhong S, Fast J. An evaluation of the MM5, RAMS, and Meso-Eta models at subkilometer resolution using VTMX field campaign data in the Salt Lake Valley[J]. Mon Wea Rev, 2003, 131(7): 1301-1322.
[31]Jiménez P A, Dudhia J. Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model[J]. J Appl Meteor Climatol, 2012, 51(2): 300-316.
[32]Niemel? S, R?is?nen P, Savij?rvi H. Comparison of surface radiative flux parameterizations, Part I: Longwave radiation[J]. Atmos Res, 2001, 58(1): 1-18.
[33]Guichard F, Parsons D B, Dudhia J, et al. Evaluating mesoscale model predictions of clouds and radiation with SGP ARM data over a seasonal timescale[J]. Mon Wea Rev, 2003, 131(5): 926-944.
[34]Steeneveld G J, Tolk L F, Moene A F, et al. Confronting the WRF and RAMS mesoscale models with innovative observations in the Netherlands: evaluating the boundary layer heat budget[J]. J Geophys Res: Atmospheres (1984-2012), 2011, 116(D23): 1-16.
[35]张强, 张杰, 乔娟, 等. 我国干旱区深厚大气边界层与陆面热力过程的关系研究[J]. 中国科学: 地球科学, 2011, 41(9): 1365-1374, doi: 10.1007/s11430-011-4207-0.
[36]Delle Monache L, Perry K D, Cederwall R T, et al. In situ aerosol profiles over the Southern Great Plains cloud and radiation test bed site: 2. Effects of mixing height on aerosol properties[J]. J Geophys Res: Atmospheres (1984-2012), 2004, 109(D6): 1-9.
[37]Liu H Z, Zhang H S, Bian L G, et al. Characteristics of micrometeorology in the surface layer in the Tibetan Plateau[J]. Adv Atmos Sci, 2002, 19(1): 73-88.
[38]Hu X M, Nielsen-Gammon J W, Zhang F. Evaluation of three planetary boundary layer schemes in the WRF model[J]. J Appl Meteor Climatol, 2010, 49(9): 1831-1844.
[39]Sorbjan Z. Improving non-local parameterization of the convective boundary layer[J]. Bound-Layer Meteor, 2009, 130(1): 57-69.
Outlines

/