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Impact of Turbulent Mixing in the Stratocumulus-Topped Boundary Layer on Numerical Weather Prediction

  • Eun-Hee Lee
  • Eunjung Lee
  • Raeseol Park
  • Young Cheol Kwon
  • Song-You Hong
Article
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Abstract

The impact of enhanced turbulent mixing induced by radiative cooling at the top of the stratocumulus-topped boundary layer (STBL) on numerical weather prediction is examined. An additional term involving top-down turbulent mixing via in-cloud radiative cooling is applied to the Yonsei University (YSU) planetary boundary layer (PBL) parameterization scheme using a top-down diffusivity profile and cloud-top entrainment. The modified scheme is evaluated in an advection fog case over the Yellow Sea of Korea using the Weather Research and Forecasting (WRF) model and in global medium-range forecasts using the Global/Regional Integrated Model system (GRIMs). In the fog case simulation, consideration of the additional top-down mixing parameterization in the YSU PBL simulates less formation and more rapid dispersion of the fog. As a result, the modified scheme simulates a drier and warmer boundary layer and a moister and cooler layer above the PBL. The modified algorithm also improves surface temperature prediction over the Yellow Sea accompanying early dissipation of the fog. In the global medium-range forecast experiment, the modified scheme simulates overall enhanced PBL mixing over the STBL in the tropics and subtropical ocean, showing drier and warmer regions near the surface and moister and cooler regions above the PBL, resulting in prediction of reduced low level cloud amount and increased downward shortwave radiation at the surface. The modified scheme appears to improve systematic bias in temperature and humidity in the lower troposphere compared to the control simulation.

Key words

Fog dissipation numerical weather prediction planetary boundary layer parameterization stratocumulus-topped boundary layer 

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References

  1. Akimoto, H., 2003: Global air quality and pollution. Science, 302, 1716-1719.CrossRefGoogle Scholar
  2. Bae, S. Y., S.-Y. Hong, and K.-S. Lim, 2016: Coupling WRF doublemoment 6-class microphysics schemes to RRTMG radiation scheme in weather research forecasting model. Adv. Meteorol., 2016, 5070154, doi:10.1155/5070154.CrossRefGoogle Scholar
  3. Baek, S. H., 2017: A revised radiation package of G-packed McICA and two-stream approximation: Performance evaluation in a global weather forecasting model. J. Adv. Model. Earth Syst., 9, 1628-1640, doi:10. 1002/2017MS000994.CrossRefGoogle Scholar
  4. Bergot, T., E. Terradellas, J. Cuxart, A. Mira, O. Liechti, M. Mueller, and N. W. Nielsen, 2007: Intercomparison of single-column numerical models for the prediction of radiation fog. J. Appl. Meteor. Climatol., 46, 504-521.CrossRefGoogle Scholar
  5. Braun, S. A., and W.-K. Tao, 2000: Sensitivity of high-resolution simulations of Hurricane Bob (1991) to planetary boundary layer parameterizations. Mon. Wea. Rev., 128, 3941-3961.CrossRefGoogle Scholar
  6. Bretherton, C. S., and S. Park, 2009: A new moist turbulence parameterization in the community atmosphere model. J. Climate, 22, 3422-3448.CrossRefGoogle Scholar
  7. Byun, Y.-H., and S.-Y. Hong, 2004: Impact of boundary layer processes on simulated tropical rainfall. J. Climate, 17, 4032-4044.CrossRefGoogle Scholar
  8. Chen, F., and J. Dudhia, 2001: Coupling an advanced land surfacehydrology model with the Penn State-NCAR MM5 modeling system. Part I: model implementation and sensitivity. Mon. Wea. Rev. 129, 569-585.Google Scholar
  9. Choi, H.-J. and H.-Y. Chun, 2011: Momentum flux spectrum of convective gravity waves. Part I: An update of a parameterization using mesoscale simulations. J. Atmos. Sci., 68, 739-759, doi:10.1175/2010JAS3552.1.Google Scholar
  10. Choi, H.-J, and S.-Y. Hong, 2015: An updated subgrid orographic parameterization for global atmospheric forecast. J. Geophys. Res., 120, 12445-12457, doi:10.1002/2015JD024230.Google Scholar
  11. Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and J. D. Tarpley, 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: 10.5194/gmd-8-975-2015.CrossRefGoogle Scholar
  12. Flemming, J., and Coauthors, 2015: Tropospheric chemistry in the Integrated Forecasting System of ECMWF, Geosci. Model Dev., 8, 975-1003, doi:10.5194/gmd-8-975-2015, 2015.CrossRefGoogle Scholar
  13. Ghonima, M. S., H. Yang, C. K. Kim, T. Heus, and J. Kleissl, 2017: Evaluation of WRF SCM simulations of stratocumulus-topped marine and coastal boundary layers and improvements to turbulence and entrainment parameterizations. J. Adv. Model. Earth Syst., 9, 2635-2653, doi:10.1002/2017MS001092.CrossRefGoogle Scholar
  14. Gultepe, I., and Coauthors, 2007: Fog research: A review of past achievements and future perspectives. Pure Appl. geophys. 164, 1121-1159.CrossRefGoogle Scholar
  15. Han, J., and H.-L. Pan, 2011: Revision of convection and vertical diffusion schemes in the NCEP Global Forecast System. Wea. Forecasting, 26, 520-533, doi:10.1175/WAF-D-10-05038.1.CrossRefGoogle Scholar
  16. Han, J.-Y., S.-Y. Hong, K.-S. S. Lim, and J. Han, 2016: Sensitivity of a cumulus parameterization scheme to precipitation production representation and its impact on a heavy rain event over Korea. Mon. Wea. Rev., 144, 2125-2135, doi:10.1175/MWR-D-15-0255.1.CrossRefGoogle Scholar
  17. Hartmann, D. L., and D. A. Short, 1980: On the use of earth radiation budget statistics for studies of clouds and climate. J. Atmos. Sci., 37, 1233-1250.CrossRefGoogle Scholar
  18. Hong, S.-Y., 2010: A new stable boundary-layer mixing scheme and its impact on the simulated East Asian summer monsoon. Quart. J. Roy. Meteor. Soc., 136, 1481-1496, doi:10.1002/qj.665.CrossRefGoogle Scholar
  19. Hong, S.-Y., and H.-L. Pan, 1996: Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev., 124, 2322-2339.CrossRefGoogle Scholar
  20. Hong, S.-Y., and J. Jang, 2018: Impacts of shallow convection processes on a simulated boreal summer climatology in a global atmospheric model (in press). Asia-Pac. J. Atmos. Sci., 54, doi:10.1007/s13143-018-0013-3.Google Scholar
  21. Hong, S.-Y., J. Dudhia, and S.-H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103-120.CrossRefGoogle Scholar
  22. Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 2318-2341.CrossRefGoogle Scholar
  23. Hong, S.-Y., J. Choi, E.-C. Chang, H. Park, and Y.-J. Kim, 2008: Lowertropospheric enhancement of gravity wave drag in a global spectral atmospheric forecast model, Wea. Forecasting, 23, 523-531.CrossRefGoogle Scholar
  24. Hong, S.-Y., and Coauthors, 2013: The global/regional integrated model system (GRIMs). Asia-Pac. J. Atmos. Sci., 49, 219-243, doi:10.1007/s13143-013-0023-0.CrossRefGoogle Scholar
  25. Hong, S.-Y., and Coauthors, 2018: The Korean Integrated Model (KIM) system for global weather forecasting (in press). Asia-Pac. J. Atmos.Sci., 54, doi:10.1007/s13143-018-0028-9.Google Scholar
  26. Iacono, M. J., J. S. Delamere, E. J. Mlawer, M. W. Shephard, S. A. Clough, and W. D. Collins, 2008: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res., 113, D13103.CrossRefGoogle Scholar
  27. Jiménez, P. A., J. Dudhia, J. F. González-Rouco, J. Navarro, J. P. Montávez, and E. García-Bustamante, 2012: A revised scheme for the WRF surface layer formulation. Mon. Wea. Rev. 140, 898-918, doi:10.1175/MWR-D-11-00056.1.CrossRefGoogle Scholar
  28. Kain, J. S. and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: the Kain-Fritsch scheme. The representation of cumulus convection in numerical models, Emanuel, K. A., and D. J. Raymond, Ed., Amer. Meteor. Soc., 246 pp.Google Scholar
  29. Kim, C. K., and S. S. Yum, 2012: A numerical study of sea-fog formation over cold sea surface using a one-dimensional turbulent model coupled with the weather research and forecasting model. Boundary-Layer Meteorol., 143, 481-505, doi:10.1007/s10546-012-9706-9.CrossRefGoogle Scholar
  30. Kim, E.-J., and S.-Y. Hong, 2010: Impact of air-sea interaction on East Asian summer monsoon climate in WRF. J. Geophys. Res., 115, D19118, doi:10.1029/2009JD013253.CrossRefGoogle Scholar
  31. Koo, M.-S., S. Baek, K.-H. Seol, and K. Cho, 2017: Advances in land surface modeling of KIAPS based on the Noah land surface model. Asia-Pac. J. Atmos. Sci., 53, 361-373, doi:10.1007/s13143-017-0043-2.CrossRefGoogle Scholar
  32. Korain, D., J. Lewis, W. T. Thompson, C. E. Dorman, and J. A. Businger, 2001: Transition of stratus into fog along the California coast: observations and modeling. J. Atmos. Sci. 58, 1714-1731.CrossRefGoogle Scholar
  33. Kwon, Y. C., and S.-Y. Hong, 2017: A mass-flux cumulus parameterization scheme across gray-zone resolutions. Mon. Wea. Rev., 145, 583-598, doi:10.1175/MWR-D-16-0034.1.CrossRefGoogle Scholar
  34. Li, X., and Z. Pu, 2008: Sensitivity of numerical simulation of early rapid intensification of hurricane Emily (2005) to cloud microphysical and planetary boundary layer parameterizations}. Mon. Wea. Rev., 136, 4819-4838.CrossRefGoogle Scholar
  35. Lim, K.-S. S., and S.-Y. Hong, 2010: Development of an effective doublemoment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Mon. Wea. Rev., 138, 1587-1612, doi:10.1175/2009MWR2968.1.CrossRefGoogle Scholar
  36. Lim, K.-S. S., S.-Y. Hong, J.-H. Yoon, and J. Han, 2014: Simulation of the summer monsoon rainfall over East Asia using the NCEP GFS cumulus parameterization at different horizontal resolutions. Wea. Forecasting, 29, 1143-1154, doi:10.1175/WAF-D-13-00143.1.CrossRefGoogle Scholar
  37. Lock, A. P., A. R. Brown, M. R. Bush, G. M. Martin, and R. N. B. Smith, 2000: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests. Mon. Wea. Rev., 128, 3187-3199.Google Scholar
  38. Martin, G. M., M. R. Bush, A. R. Brown, A. P. Lock, and R. N. B. Smith, 2000: A new boundary-layer mixing scheme. Part II: Tests in climate and mesoscale models. Mon. Wea. Rev. 128, 3200-3217.Google Scholar
  39. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 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.CrossRefGoogle Scholar
  40. Musson-Genon, L., 1987: Numerical simulation of a fog event with a onedimensional boundary layer model. Mon. Wea. Rev. 115, 592-607.CrossRefGoogle Scholar
  41. Nicholls, S., and J. D. Turton, 1986: An observational study of the structure of stratiform cloud sheets. Part II: Entrainment. Quart. J. Roy. Meteor. Soc., 112, 461-480, doi:10.1002/621qj.49711247210.Google Scholar
  42. Pagowski, M, I. Gultepe and P. King, 2004: Analysis and modeling of an extremely dense fog event in southern Ontario. J. Appl. Meteorol. 43, 3-16.CrossRefGoogle Scholar
  43. Park, R.-S., J.-H. Chae, and S.-Y. Hong, 2016: A revised prognostic cloud fraction scheme in a global forecasting system. Mon. Wea. Rev., 114, 1219-1229, doi:10.1175/MWR-D-15-0273.1.CrossRefGoogle Scholar
  44. Randall, D. A., J. A. Coakley, C. W. Fairall, R. A., Kropfli, and D. H. Lenschow, 1984: Outlook for research on subtropical marine stratification clouds. Bull. Amer. Meteor. Soc., 65, 1290-1301.CrossRefGoogle Scholar
  45. Richter, J. H., F. Sassi, and R. R. Garcia, 2010: Toward a physically based gravity wave source parameterization in a general circulation model. J. Atmos. Sci., 67, 136-156, doi:10.1175/2009JAS3112.1.CrossRefGoogle Scholar
  46. Skamarock, W., J. B. Klemp, J. Dudhia, D. O. Gill, D. Barker, D. M. Duda, X. Huang, W. Wang, and J. G. Powers, 2008: A description of the advanced research WRF version 3. NCAR Tech. note NCAR/TN-475+STR, 113pp.Google Scholar
  47. Shin, H. H., and S.-Y. Hong, 2015: Representation of the subgrid-scale turbulent transport in convective boundary layers at gray-zone resolutions. Mon. Wea. Rev., 143, 250-271, doi: 10.1175/MWR-D-14-00116.1.CrossRefGoogle Scholar
  48. Steeneveld, G. J., R. J. Ronda, and A. A. M. Holtslag, 2015: The challenge of forecasting the onset and development of radiation fog using mesoscale atmospheric models. Boundary-Layer Meteorol., 152, 265-289, doi:10.1007/s10546-014-9973-8.CrossRefGoogle Scholar
  49. Syed, F. S., H. Körnich, and M. Tjernström, 2012: On the fog variability over south Asia. Climate Dyn., 39, 2993-3005, doi:10.1007/s00382-012-1414-0.CrossRefGoogle Scholar
  50. Teixeira, J., 1999: Simulation of fog with the ECMWF prognostic cloud scheme. Quart. J. Roy. Meteor. Soc., 125, 529-552.CrossRefGoogle Scholar
  51. van der Velde, I. R., G. J. Steeneveld, B. G. J. Wichers Schreur, and A. A. M. Holtslag, 2010: Modeling and forecasting the onset and duration of severe radiation fog under frost conditions. Mon. Wea. Rev., 138, 4237-4253, doi:10.1175/2010MWR3427.1.CrossRefGoogle Scholar
  52. Wilson, T. H., and R. G. Fovell, 2018: Modeling the evolution and life cycle of radiative cold pools and fog. Wea. Forecasting, 33, 203-220, doi:10.1175/WAF-D-17-0109.1.CrossRefGoogle Scholar

Copyright information

© Korean Meteorological Society and Springer Nature B.V. 2018

Authors and Affiliations

  • Eun-Hee Lee
    • 1
    • 2
  • Eunjung Lee
    • 1
  • Raeseol Park
    • 1
  • Young Cheol Kwon
    • 1
  • Song-You Hong
    • 1
  1. 1.Korea Institute of Atmospheric Prediction Systems (KIAPS)SeoulKorea
  2. 2.Korea Institute of Atmospheric Prediction SystemsSeoulKorea

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