Impacts of subgrid-scale orography parameterization on simulated atmospheric fields over Korea using a high-resolution atmospheric forecast model
- 241 Downloads
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.
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.
- Hong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151Google Scholar
- 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
- 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
- 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
- Wark K, Warner CF, Davis WT (1998) Air pollution its origin and control, 3rd edn. Addison Wesley Longman, Reading, pp 286–287Google Scholar
- Werner M (2001) Shuttle radar topography mission (SRTM), mission overview. J Telecommun (Frequenz) 55:75–79Google Scholar
- World Meteorological Organization (2008) Guide to meteorological instruments and methods of observation, 7th edn. WMO-No.8, GenevaGoogle Scholar