Advertisement

Meteorology and Atmospheric Physics

, Volume 131, Issue 4, pp 975–985 | Cite as

Impacts of subgrid-scale orography parameterization on simulated atmospheric fields over Korea using a high-resolution atmospheric forecast model

  • Kyo-Sun Sunny LimEmail author
  • Jong-Myoung Lim
  • Hyeyum Hailey Shin
  • Jinkyu Hong
  • Young-Yong Ji
  • Wanno Lee
Original Paper
  • 241 Downloads

Abstract

A substantial over-prediction bias at low-to-moderate wind speeds in the Weather Research and Forecasting (WRF) model has been reported in the previous studies. Low-level wind fields play an important role in dispersion of air pollutants, including radionuclides, in a high-resolution WRF framework. By implementing two subgrid-scale orography parameterizations (Jimenez and Dudhia in J Appl Meteorol Climatol 51:300–316, 2012; Mass and Ovens in WRF model physics: problems, solutions and a new paradigm for progress. Preprints, 2010 WRF Users’ Workshop, NCAR, Boulder, Colo. http://www.mmm.ucar.edu/wrf/users/workshops/WS2010/presentations/session%204/4-1_WRFworkshop2010Final.pdf, 2010), we tried to compare the performance of parameterizations and to enhance the forecast skill of low-level wind fields over the central western part of South Korea. Even though both subgrid-scale orography parameterizations significantly alleviated the positive bias at 10-m wind speed, the parameterization by Jimenez and Dudhia revealed a better forecast skill in wind speed under our modeling configuration. Implementation of the subgrid-scale orography parameterizations in the model did not affect the forecast skills in other meteorological fields including 10-m wind direction. Our study also brought up the problem of discrepancy in the definition of “10-m” wind between model physics parameterizations and observations, which can cause overestimated winds in model simulations. The overestimation was larger in stable conditions than in unstable conditions, indicating that the weak diurnal cycle in the model could be attributed to the representation error.

Notes

Acknowledgements

This work was performed under the auspices of the Ministry of Science and ICT (MSIT) of Korea, NRF Contract No. 2017M2A8A4015256. The authors would like to express their gratitude to Dr. Eun-Han Kim and Dr. Byung-Il Min, who provided the observation data at KAERI and RDAPS generated by the Korea Meteorological Administration.

References

  1. Chang JC, Franzese P, Chayantrakom K, Hanna SR (2003) Evaluations of CALPUFF, HPAC, and VLSTRACK with two mesoscale field datasets. J Appl Meteorol 42:453–466CrossRefGoogle Scholar
  2. Chen F, Dudhia J (2001) Coupling and advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon Weather Rev 129:569–585CrossRefGoogle Scholar
  3. Cheng WYY, Steenburgh WJ (2005) Evaluation of surface sensible weather forecast by the WRF and the Eta Models over the western United States. Weather Forecast 20:812–821CrossRefGoogle Scholar
  4. Dudhia J, Hong SY, Lim KSS (2008) A new method for representing mixed-phase particle fall speeds in bulk microphysics parameterizations. J Meteorol Soc Jpn 86A:33–44CrossRefGoogle Scholar
  5. Georgelin M et al (2000) The second COMPARE exercise: a model intercomparison using a case of a typical mesoscale orographic flow, the PYREX IOP3. Q J R Meteorol Soc 126:991–1029CrossRefGoogle Scholar
  6. Han HS, Cho WK, Park UJ, Hong YD, Park KB (2003) Current status and future plan for the production of radioisotopes using HANARO Research Reactor. J Radioanal Nucl Chem 257:47–51CrossRefGoogle Scholar
  7. Hong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151Google Scholar
  8. Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Weather Rev 134:2318–2341CrossRefGoogle Scholar
  9. Iacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD (2008) Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. J Geophys Res 113:D13103.  https://doi.org/10.1029/2008JD009944 CrossRefGoogle Scholar
  10. Jimenez PA, Dudhia J (2012) Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model. J Appl Meteorol Climatol 51:300–316CrossRefGoogle Scholar
  11. Jimenez PA et al (2012) A revised scheme for the WRF surface layer formulation. Mon Weather Rev 140:898–918CrossRefGoogle Scholar
  12. Kain JS (2004) The Kain–Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181CrossRefGoogle Scholar
  13. Kain JS, Fritsch JM (1990) A one-dimensional entraining/detraining plume model and its application in convective parameterization. J Atmos Sci 47:2784–2802CrossRefGoogle Scholar
  14. Lee HJ, Lee KO, Won GM, Lee HW (2009) Application of the latest use data for numerical simulation of urban thermal environment in the Daegu. J Korean Soc Atmos Environ 25:196–210CrossRefGoogle Scholar
  15. Lee SJ, Kim J, Kang M, Malla-Thakuri B (2014) Numerical simulation of local atmospheric circulations in the valley of Gwangneung KoFlux sites. Korean J Agric Forecast Meteorol 16:246–260CrossRefGoogle Scholar
  16. Lee J, Shin HH, Hong SY, Jiménez PA, Dudhia J, Hong J (2015) Impacts of subgrid-scale orography parameterization on simulated surface layer wind and monsoonal precipitation in the high-resolution WRF model. J Geophys Res 120:644–653.  https://doi.org/10.1002/2014JD022747 Google Scholar
  17. Lorente-Plazas R, Jiménez PA, Dudhia J, Montávez JP (2016) Evaluating and improving the impact of the atmospheric stability and orography on surface winds in the WRF Model. Mon Weather Rev 144:2685–2693CrossRefGoogle Scholar
  18. Mass C, Ovens D (2010) WRF model physics: problems, solutions and a new paradigm for progress. Preprints, 2010 WRF Users’ Workshop, NCAR, Boulder, Colo. http://www2.mmm.ucar.edu/wrf/users/workshops/WS2010/presentations/session%204/4-5_TEMF_CA_fcst_WRF10.pdf. Accessed 8 June 2018
  19. Mass C, Ovens D (2011) Fixing WRF’s high speed wind bias: a new subgrid scale drag parameterization and the role of detailed verification. Preprints, 24th Conference on Weather and Forecasting/20th Conference on Numerical Weather Prediction, Seattle, WA, American Meteorological Society, 9B.6. https://ams.confex.com/ams/91Annual/webprogram/Paper180011.html. Accessed 8 June 2018
  20. Miller MJ, Palmer TN, Swinbank R (1989) Parameterization and influence subgrid-scale orography in general circulation and numerical weather prediction models. Meteorol Atmos Phys 40:84–109CrossRefGoogle Scholar
  21. Morcrette JJ, Barker HW, Cole JNS, Iacono MJ, Pincus R (2008) Impact of a new radiation package, McRad, in the ECMWF integrated forecasting system. Mon Weather Rev 136:4773–4798CrossRefGoogle Scholar
  22. Niu GY et al (2011) The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. J Geophys Res 116:D12109.  https://doi.org/10.1029/2010jd015139 CrossRefGoogle Scholar
  23. Rabus B, Eineder M, Am R, Bamler R (2004) The shuttle radar topography mission—a new class of digital elevation models acquired by space borne radar. J Photogramm Remote Sens 57:241–262CrossRefGoogle Scholar
  24. Rontu L (2006) A study on parameterization of orography-related momentum fluxes in a synoptic-scale NWP model. Tellus 58A:69–81CrossRefGoogle Scholar
  25. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang XY, Wang W, Powers JG (2008) A description of the advanced research WRF version 3. NCAR Tech Rep TN-4751STR, p 113Google Scholar
  26. Wark K, Warner CF, Davis WT (1998) Air pollution its origin and control, 3rd edn. Addison Wesley Longman, Reading, pp 286–287Google Scholar
  27. Werner M (2001) Shuttle radar topography mission (SRTM), mission overview. J Telecommun (Frequenz) 55:75–79Google Scholar
  28. World Meteorological Organization (2008) Guide to meteorological instruments and methods of observation, 7th edn. WMO-No.8, GenevaGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Nuclear Emergency and Environmental Protection DivisionKorea Atomic Energy Research InstituteDaejeonRepublic of Korea
  2. 2.NOAA/Geophysical Fluid Dynamics LaboratoryPrincetonUSA
  3. 3.Cooperative Programs for the Advancement of Earth System ScienceUniversity Corporation for Atmospheric ResearchBoulderUSA
  4. 4.Ecosystem-Atmosphere Process Lab, Department of Atmospheric SciencesYonsei UniversitySeoulRepublic of Korea

Personalised recommendations