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.
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Akimoto, H., 2003: Global air quality and pollution. Science, 302, 1716-1719.
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.
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.
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.
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.
Bretherton, C. S., and S. Park, 2009: A new moist turbulence parameterization in the community atmosphere model. J. Climate, 22, 3422-3448.
Byun, Y.-H., and S.-Y. Hong, 2004: Impact of boundary layer processes on simulated tropical rainfall. J. Climate, 17, 4032-4044.
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.
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.
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.
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.
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.
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.
Gultepe, I., and Coauthors, 2007: Fog research: A review of past achievements and future perspectives. Pure Appl. geophys. 164, 1121-1159.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Musson-Genon, L., 1987: Numerical simulation of a fog event with a onedimensional boundary layer model. Mon. Wea. Rev. 115, 592-607.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Teixeira, J., 1999: Simulation of fog with the ECMWF prognostic cloud scheme. Quart. J. Roy. Meteor. Soc., 125, 529-552.
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.
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.
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Lee, EH., Lee, E., Park, R. et al. Impact of Turbulent Mixing in the Stratocumulus-Topped Boundary Layer on Numerical Weather Prediction. Asia-Pacific J Atmos Sci 54 (Suppl 1), 371–384 (2018). https://doi.org/10.1007/s13143-018-0024-0
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DOI: https://doi.org/10.1007/s13143-018-0024-0