Abstract
This study investigated the performance of the mesoscale Weather Research and Forecasting (WRF) model in predicting near-surface atmospheric temperature and wind for a complex underlying surface in Northwest China in June and December 2015. The spatial distribution of the monthly average bias errors in the forecasts of 2-m temperature and 10-m wind speed is analyzed first. It is found that the forecast errors for 2-m temperature and 10-m wind speed in June are strongly correlated with the terrain distribution. However, this type of correlation is not apparent in December, perhaps due to the inaccurate specification of the surface albedo and freezing–thawing process of frozen soil in winter in Northwest China in the WRF model. In addition, the WRF model is able to reproduce the diurnal variation in 2-m temperature and 10-m wind speed, although with weakened magnitude. Elevations and land-use types have strong influences on the forecast of near-surface variables with seasonal variations. The overall results imply that accurate specification of the complex underlying surface and seasonal changes in land cover is necessary for improving near-surface forecasts over Northwest China.
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Ancell B. C., C. F. Mass, and G. J. Hakim, 2011: Evaluation of surface analyses and forecasts with a multiscale ensemble Kalman filter in regions of complex terrain. Mon. Wea. Rev., 139, 2008–2024, doi: 10.1175/2010MWR3612.1.
Byun D., F. Ngan, X. S. Li, et al., 2008: Evaluation of Retrospective MM5 and CMAQ Simulation of TexAQS-II Period with CAMS Measurements. Texas Commission on Environmental Quality Final Rep., Grant No. 582-5-64594-FY07-02, 30 pp.
Cao F. Q., L. Dan, and Z. G. Ma, 2015: Simulative study of the impact of the cropland change on the regional climate over China. Acta Meteor. Sinica, 73, 128–141, doi: 10.11676/qxxb2015.001. (in Chinese)
Chen B., A. F. Stein, N. Castell, et al., 2012: Modeling and surface observations of arsenic dispersion from a large Cu-smelter in southwestern Europe. Atmos. Environ., 49, 114–122, doi: 10.1016/j.atmosenv.2011.12.014.
Chen H. S., and Y. Zhang, 2013: Sensitivity experiments of impacts of large-scale urbanization in East China on East Asian winter monsoon. Chinese Sci. Bull., 58, 809–815, doi: 10.1007/s11434-012-5579-z.
Chen H. S., X. Li, and W. J. Hua, 2015: Numerical simulation of the impact of land use/land cover change over China on regional climates during the last 20 years. Chinese J. Atmos. Sci., 39, 357–369, doi: 10.3878/j.issn.1006-9895.1404.14114. (in Chinese)
Cheng F. Y., Y. C. Hsu, P. L. Lin, et al., 2013: Investigation of the effects of different land use and land cover patterns on mesoscale meteorological simulations in the Taiwan area. J. Appl. Meteor. Climatol., 52, 570–587, doi: 10.1175/JAMC-D-12-0109.1.
Cheng W. Y. Y., and W. J. Steenburgh, 2005: Evaluation of surface sensible weather forecasts by the WRF and the Eta models over the western United States. Wea. Forecasting, 20, 812–821, doi: 10.1175/WAF885.1.
Comarazamy D. E., J. E. González, J. C. Luvall, et al., 2013: Climate impacts of land-cover and land-use changes in tropical islands under conditions of global climate change. J. Climate, 26, 1535–1550, doi: 10.1175/JCLI-D-12-00087.1.
Dudhia J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 3077–3107, doi: 10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.
Ek M. B., K. E. Mitchell, Y. Lin, et al., 2003: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model. J. Geophys. Res., 108, 8851, doi: 10.1029/2002JD003296.
Gómez-Navarro J. J., C. C. Raible, and S. Dierer, 2015: Sensitivity of the WRF model to PBL parametrisations and nesting techniques: Evaluation of surface wind over complex terrain. Geosci. Model Dev., 8, 3349–3363, doi: 10.5194/gmd-8-3349-2015.
Hanna S. R., and R. X. Yang, 2001: Evaluations of mesoscale models’ simulations of near-surface winds, temperature gradients, and mixing depths. J. Appl. Meteor., 40, 1095–1104, doi: 10.1175/1520-0450(2001)040<1095:EOMMSO>2.0.CO;2.
He J. J., Y. Yu, N. Liu, et al, 2014: Impact of land surface information on WRF’s performance in complex terrain area. Chinese J. Atmos. Sci., 38, 484–498, doi: 10.3878/j.issn.1006-9895. 2013.13186. (in Chinese)
Hirsch, A L., A. J. Pitman, J. Kala, et al., 2015: Modulation of land-use change impacts on temperature extremes via land–atmosphere coupling over Australia. Earth Interactions, 19, 1–24, doi: 10.1175/EI-D-15-0011.1.
Hu, X.-M., P. M. Klein, and M. Xue, 2013: Evaluation of the updated YSU planetary boundary layer scheme within WRF for wind resource and air quality assessments. J. Geophys. Res., 118, 10490–10505, doi: 10.1002/jgrd.50823.
Jiménez P. A., and J. Dudhia, 2012: Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model. J. Appl. Meteor. Climatol., 51, 300–316, doi: 10.1175/JAMC-D-11-084.1.
Jiménez P. A., and J. Dudhia, 2013: On the ability of the WRF model to reproduce the surface wind direction over complex terrain. J. Appl. Meteor. Climatol., 52, 1610–1617, doi: 10.1175/JAMC-D-12-0266.1.
Jin L. L., Z. J. Li, Q. He, et al., 2016: Observation and simulation of near-surface wind and its variation with topography in Urumqi, West China. J. Meteor. Res., 30, 961–982, doi: 10.1007/s13351-016-6012-3.
Kabat P., M. Claussen, S. Whitlock, et al., 2004: Vegetation, Water, Humans and the Climate: A New Perspective on an Interactive System. Springer, Berlin Heidelberg, 566 pp, doi: 10.1007/978-3-642-18948-7.
Kain J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170–181, doi: 10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.
Kim Y., and G. L. Wang, 2007: Impact of vegetation feedback on the response of precipitation to antecedent soil moisture anomalies over North America. J. Hydrometeor., 8, 534–550, doi: 10.1175/JHM612.1.
Lee S. H., S. W. Kim, W. M. Angevine, et al., 2011: Evaluation of urban surface parameterizations in the WRF model using measurements during the Texas Air Quality Study 2006 field campaign. Atmos. Chem. Phys., 11, 2127–2143, doi: 10.5194/acp-11-2127-2011.
Lee T. J., R. A. Pielke, R. C. Kessler, et al., 1989: Influence of cold pools downstream of mountain barriers on downslope winds and flushing. Mon. Wea. Rev., 117, 2041–2058, doi: 10.1175/1520-0493(1989)117<2041:IOCPDO>2.0.CO;2.
Lim Y. K., M. Cai, E. Kalnay, et al., 2008: Impact of vegetation types on surface temperature change. J. Appl. Meteor. Climatol., 47, 411–424, doi: 10.1175/2007JAMC1494.1.
Liu J. Y., W. H. Kuang, Z. X. Zhang, et al., 2014: Spatiotemporal characteristics, patterns and causes of land-use changes in China since the late 1980s. Acta Geogra. Sinica, 69, 3–14, doi: 10.11821/dlxb201401001. (in Chinese)
Lorente-Plazas R., P. A. Jiménez, J. Dudhia, et al., 2016: Evaluating and improving the impact of the atmospheric stability and orography on surface winds in the WRF model. Mon. Wea. Rev., 144, 2685–2693, doi: 10.1175/MWR-D-15-0449.1.
Mass C. F., D. Ovens, K. Westrick, et al., 2002: Does increasing horizontal resolution produce more skillful forecasts? Bull. Amer. Meteor. Soc., 83, 407–430, doi: 10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.
Meng X. H., J. P. Evans, and M. F. McCabe, 2014: The impact of observed vegetation changes on land–atmosphere feedbacks during drought. J. Hydrometeor., 15, 759–776, doi: 10.1175/JHM-D-13-0130.1.
Mesinger F., G. DiMego, E. Kalnay, et al., 2006: North American regional reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360, doi: 10.1175/BAMS-87-3-343.
Mlawer E. J., S. J. Taubman, P. D. Brown, et al., 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16663–16682, doi: 10.1029/97JD00237.
Ngan F., D. Byun, H. Kim, et al., 2012: Performance assessment of retrospective meteorological inputs for use in air quality modeling during TexAQS 2006. Atmos. Environ., 54, 86–96, doi: 10.1016/j.atmosenv.2012.01.035.
Ngan F., H. Kim, P. Lee, et al., 2013: A study of nocturnal surface wind speed overprediction by the WRF-ARW model in southeastern Texas. J. Appl. Meteor. Climatol., 52, 2638–2653, doi: 10.1175/JAMC-D-13-060.1.
Oke T. R., 1987: Boundary Layer Climates. Cambridge University Press, Cambridge, 435 pp.
Pan X. D., X. Li, Y. H. Ran, et al., 2012: Impact of underlying surface information on WRF modeling in Heihe River basin. Plateau Meteor., 31, 657–667. (in Chinese)
Pei L. S., N. Moore, S. Y. Zhong, et al., 2014: WRF model sensitivity to land surface model and cumulus parameterization under short-term climate extremes over the Southern Great Plains of the United States. J. Climate, 27, 7703–7724, doi: 10.1175/JCLI-D-14-00015.1.
Pleim J. E., 2007a: A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 46, 1383–1395, doi: 10.1175/JAM2539.1.
Pleim J. E., 2007b: A combined local and nonlocal closure model for the atmospheric boundary layer. Part II: Application and evaluation in a mesoscale meteorological model. J. Appl. Meteor. Climatol., 46, 1396–1409, doi: 10.1175/JAM2534.1.
Price J. C., 1977: Thermal inertia mapping: A new view of the earth. J. Geophys. Res., 82, 2582–2590, doi: 10.1029/JC082i018p02582.
Price J. C., 1980: The potential of remotely sensed thermal infrared data to infer surface soil moisture and evaporation. Water Resour. Res., 16, 787–795, doi: 10.1029/WR016i004p00787.
Pu Z. X., H. L. Zhang, and J. A. Anderson, 2013: Ensemble Kalman filter assimilation of near-surface observations over complex terrain: Comparison with 3DVAR for short-range fore-casts. Tellus A, 65, 19620, doi: 10.3402/tellusa.v65i0.19620.
Rife D. R., C. A. Davis, Y. B. Liu, et al., 2004: Predictability of low-level winds by mesoscale meteorological models. Mon. Wea. Rev., 132, 2533–2569, doi: 10.1175/MWR2801.1.
Rowell D. P., and J. R. Milford, 1993: On the generation of African squall lines. J. Climate, 6, 1181–1193, doi: 10.1175/1520-0442(1993)006<1181:OTGOAS>2.0.CO;2.
Ruiz J. J., C. Saulo, and J. Nogués-Paegle, 2010: WRF model sensitivity to choice of parameterization over South America: Validation against surface variables. Mon. Wea. Rev., 138, 3342–3355, doi: 10.1175/2010MWR3358.1.
Santos-Alamillos F. J., D. Pozo-Vázquez, J. A. Ruiz-Arias, et al., 2013: Analysis of WRF model wind estimate sensitivity to physics parameterization choice and terrain representation in Andalusia (southern Spain). J. Appl. Meteor. Climatol., 52, 1592–1609, doi: 10.1175/JAMC-D-12-0204.1.
Scheitlin K. N., and P. G. Dixon, 2010: Diurnal temperature range variability due to land cover and airmass types in the Southeast. J. Appl. Meteor. Climatol., 49, 879–888, doi: 10.1175/2009JAMC2322.1.
Sheng L. F., K. H. Schlunzen, and Z. M. Wu, 2000: Three-dmensional numerical simulation of the mesoscale wind structure over Shandong Peninsula. Acta Meteor. Sinica, 14, 98–107.
Siuta D., G. West, and R. Stull, 2017: WRF hub-height wind forecast sensitivity to PBL scheme, grid length, and initial condition choice in complex terrain. Wea. Forecasting, 32, 493–509, doi: 10.1175/WAF-D-16-0120.1.
Smith R. B., and Y.-L. Lin, 1982: The addition of heat to a stratified airstream with application to the dynamics of orographic rain. Quart. J. Roy. Meteor. Soc., 108, 353–378, doi: 10.1002/qj.49710845605.
Stull R. B., 1988: An Introduction to Boundary Layer Meteorology. Springer, Netherlands, 666 pp, doi: 10.1007/978-94-009-3027-8.
Tao S. Y., 1980: Heavy Rainfalls in China. Science Press, Beijing, 225 pp. (in Chinese)
Wang C. H., and S. L. Jin, 2013: Error features and their possible causes in simulated low-level winds by WRF at a wind farm. Wind Energy, 17, 1315–1325, doi: 10.1002/we.1635.
Whiteman C. D., 2000: Mountain Meteorology: Fundamentals and Applications. Oxford University Press, Oxford, 355 pp.
Wu Z. M., and K. H. Schlunzen, 1992: Numerical study on the local wind structures forced by the complex terrain of Qingdao area. Acta Meteor. Sinica, 6, 355–366.
Xin J. Y., C. S. Gong, S. G. Wang, et al., 2016: Aerosol direct radiative forcing in desert and semi-desert regions of northwestern China. Atmos. Res., 171, 56–65, doi: 10.1016/j.atmosres. 2015.12.004.
Yucel I., 2006: Effects of implementing MODIS land cover and albedo in MM5 at two contrasting U. S. regions. J. Hydrometeor., 7, 1043–1060, doi: 10.1175/JHM536.1.
Zhang, D.-L., and W. Z. Zheng, 2004: Diurnal cycles of surface winds and temperatures as simulated by five boundary layer parameterizations. J. Appl. Meteor., 43, 157–169, doi: 10.1175/ 1520-0450(2004)043<0157:DCOSWA>2.0.CO;2.
Zhang F. M., Y. Yang, and C. H. Wang, 2015: The effects of assimilating conventional and ATOVS data on forecasted nearsurface wind with WRF-3DVAR. Mon. Wea. Rev., 143, 153–164, doi: 10.1175/MWR-D-14-00038.1.
Zhang H. L., Z. X. Pu, and X. B. Zhang, 2013: Examination of errors in near-surface temperature and wind from WRF numerical simulations in regions of complex terrain. Wea. Forecasting, 28, 893–914, doi: 10.1175/WAF-D-12-00109.1.
Zhang Y., X. Y. Wen, and C. J. Jang, 2010: Simulating chemistry–aerosol–cloud–radiation–climate feedbacks over the continental U.S. using the online-coupled Weather Research Forecasting Model with chemistry (WRF/Chem). Atmos. Environ., 44, 3568–3582, doi: 10.1016/j.atmosenv.2010.05.056.
Zheng D., R. van der Velde, Z. Su, et al., 2014: Assessment of roughness length schemes implemented within the Noah land surface model for high-altitude regions. J. Hydrometeor., 15, 921–937, doi: 10.1175/JHM-D-13-0102.1.
Zheng D., R. van der Velde, Z. Su, et al., 2017: Assessment of Noah land surface model with various runoff parameterizations over a Tibetan river. J. Geophy. Res. Atmos., 122, 1488–1504, doi: 10.1002/2016JD025572.
Zheng D., R. van der Velde, Z. Su, et al., 2017: Evaluation of Noah frozen soil parameterization for application to a Tibetan Meadow Ecosystem. J. Hydrometeor., doi: 10.1175/JHM-D-16-0199.1.
Zhong S. Y., and J. Fast, 2003: An evaluation of the MM5, RAMS, and Meso-Eta models at subkilometer resolution using VTMX field campaign data in the Salt Lake valley. Mon. Wea. Rev., 131, 1301–1322, doi: 10.1175/1520-0493(2003)131<1301:AEOTMR>2.0.CO;2.
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Supported by the China Meteorological Administration Special Public Welfare Research Fund (GYHY201506001) and Northwest Regional Numerical Forecasting Innovation Team Fund (GSQXCXTD-2017-02).
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Duan, H., Li, Y., Zhang, T. et al. Evaluation of the Forecast Accuracy of Near-Surface Temperature and Wind in Northwest China Based on the WRF Model. J Meteorol Res 32, 469–490 (2018). https://doi.org/10.1007/s13351-018-7115-9
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DOI: https://doi.org/10.1007/s13351-018-7115-9