Skip to main content
Log in

Evaluation of the Forecast Accuracy of Near-Surface Temperature and Wind in Northwest China Based on the WRF Model

  • Special Collection on Weather and Climate Under Complex Terrain and Variable Land Surfaces: Observations and Numerical Simulations
  • Published:
Journal of Meteorological Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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)

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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)

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Oke T. R., 1987: Boundary Layer Climates. Cambridge University Press, Cambridge, 435 pp.

    Google Scholar 

  • 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)

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Price J. C., 1977: Thermal inertia mapping: A new view of the earth. J. Geophys. Res., 82, 2582–2590, doi: 10.1029/JC082i018p02582.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Stull R. B., 1988: An Introduction to Boundary Layer Meteorology. Springer, Netherlands, 666 pp, doi: 10.1007/978-94-009-3027-8.

    Book  Google Scholar 

  • Tao S. Y., 1980: Heavy Rainfalls in China. Science Press, Beijing, 225 pp. (in Chinese)

    Google Scholar 

  • 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.

    Google Scholar 

  • Whiteman C. D., 2000: Mountain Meteorology: Fundamentals and Applications. Oxford University Press, Oxford, 355 pp.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Acknowledgments

We appreciate the constructive comments and suggestions from the two anonymous reviewers.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haixia Duan.

Additional information

Supported by the China Meteorological Administration Special Public Welfare Research Fund (GYHY201506001) and Northwest Regional Numerical Forecasting Innovation Team Fund (GSQXCXTD-2017-02).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13351-018-7115-9

Key words

Navigation