1 |
Byun, U.-Y., Hong, J., Hong, S.-Y., and Shin, H. H., 2015, Numerical simulations of heavy rainfall over central Korea on 21 September 2010 using the WRF model. Advances in Atmospheric Science, 32(6), 855-869.
DOI
|
2 |
Chen, F., and Dudhia, J., 2001, Coupling and advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity. Monthly Weather Review, 129, 569-585.
DOI
|
3 |
Dee, D.P., and Coauthors, 2011, The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137, 553-597.
DOI
|
4 |
Field, P. R., Hogan, R. J., Brown, P. R. A., Illingworth, A. J., Choularton, T. W., and Cotton, R. J. 2005, Parameterization of ice-particle size distributions for mid-latitude stratiform cloud. Quarterly Journal of the Royal Meteorological Society, 131, 1997-2017.
DOI
|
5 |
Fovell, R. G., Mullendore, G. L., and Kim, S.-H., 2006, Discrete propagation in numerically simulated nocturnal squall lines. Monthly Weather Review, 134, 3735-3752.
DOI
|
6 |
Tapiador, F. J., Sánchez, J. L., and Garcia-Ortega, E., 2019, Empirical values and assumptions in the microphysics of numerical models. Atmospheric Research, 215, 214-238.
DOI
|
7 |
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D., 2008, Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Imple- mentation of a new snow parameterization. Monthly Weather Review, 136, 5095-5115.
DOI
|
8 |
Yang, Q., Dai, Q., Han, D., Chen, Y., Zhang, S., 2019, Sensitivity analysis of raindrop size distribution parameterizations in WRF rainfall simulation. Atmospheric Research, 228, 1-13.
DOI
|
9 |
Hong, S.-Y., Lim, K.-S. S., Kim, J.-H., Lim, J.-O. J., and Dudhia, J., 2009, Sensitivity study of cloud-resolving convective simulations with WRF using two bulk microphysical parameterizations: Ice-phase microphysics versus sedimentation effects. Journal of Applied Meteorology and Climatology, 48, 61-76.
DOI
|
10 |
Hagos, S., Feng, Z., Burleyson C., Lim, K.-S. S., Long, C. L., Wu, D., and Thompson, G., 2014, Evaluation of high resolution simulations of cloud populations associated with Madden-Julian Oscillation using data collected during AMIE/DYNAMO field campaign. Journal of Geophysical Research, 119, 12,052-12,068.
DOI
|
11 |
Hong, S.-Y., and Lim, J.-O. J., 2006, The WRF singlemoment 6-class microphysics scheme (WSM6). Journal of Korean Meteorological Society, 42, 129-151.
|
12 |
Hong, S.-Y., Noh, Y., and Dudhia, J., 2006, A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 134, 2318-2341.
DOI
|
13 |
Hong, S.-Y., Dudhia, J., and Chen, S.-H., 2004, A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Monthly Weather Review, 132, 103-120.
DOI
|
14 |
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D., 2008, Radiative forcing by long-lived greenhouse gases: calculations with the AER radiative transfer models. Journal of Geophysical Research, 113, D13103.
DOI
|
15 |
Kain, J. S., 2004, The Kain-Fritsch convective parameterization: An update. Journal of Applied Meteorology, 43, 170-181.
DOI
|
16 |
Lim, K.-S. S., 2019, Bulk-type cloud microphysics parameterization in atmospheric models. Atmosphere, 29(2), 1-13.
|
17 |
Kain, J. S., and Fritsch, J. M., 1990, A one-dimensional entraining/detraining plume model and its application in convective parameterization. Journal of Atmospheric Science, 47, 2784-2802.
DOI
|
18 |
Lee, G., and Kim, K., 2019, International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018). AGU fall meeting, San Francisco, CA, USA, Moscone center.
|
19 |
Lei, H., Guo, J., and Chen, D., 2020, Systematic bias in the prediction of warm-rain hydrometeors in the WDM6 microphysics scheme and modifications. Journal of Geophysical Research, in press.
|
20 |
Lim, K.-S. S., Chang, E.-C., and Sun, R., 2019, Evaluation of cloud microphysics parameterizations during the ICEPOP field campaign in Korea. AGU fall meeting, San Francisco, CA, USA, Moscone center.
|
21 |
Lim, K.-S. S., Hong, S.-Y., Yoon, J.-H., and Han, J., 2014, Simulation of the summer monsoon rainfall over East Asia using the NCEP GFS cumulus parameterization at different horizontal resolutions. Weather and Forecasting, 29, 1143-1154.
DOI
|
22 |
Lim, K.-S. S., and Hong, S.-Y., 2012, Investigation of aerosol indirect effects on simulated flash-flood heavy rainfall over Korea. Meteorology and Atmospheric Physics, 118, 199-214.
DOI
|
23 |
Lim, K.-S. S., and Hong, S.-Y., 2010, Development of an effective double-moment cloud microphysics scheme with prognostic Cloud Condensation Nuclei (CCN) for weather and climate models. Monthly Weather Review, 138, 1587-1612.
DOI
|
24 |
McMillen, J. D., and Steenburgh, W. J., 2015, Impact of microphysics parameterizations on simulations of the 27 October 2010 Great Salt Lake-effect snowstorm. Weather and Forecasting, 30, 136-152.
DOI
|
25 |
Morrison, H., Curry, J. A., and Khvorostyanov, V. I., 2005, A new double-moment microphysics parameterization for application in cloud and climate models. Part I:Description. Journal of Atmospheric Science, 62, 1665-1677.
DOI
|
26 |
Milbrandt, J. A., and Yau, M. K., 2005, A multimoment bulk microphysics parameterization. Part I: Analysis of the role of the spectral shape parameter. Journal of Atmospheric Science, 62, 3051-3064.
DOI
|
27 |
Morcrette, J. J., Barker, H. W., Cole, J. N. S., Iacono, M. J., and Pincus, R., 2008, Impact of a new radiation package, McRad, in the ECMWF integrated forecasting system. Monthly Weather Review, 136, 4773-4798.
DOI
|
28 |
Morrison, H., Thompson, G., and Tatarskii, V., 2009, Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Monthly Weather Review, 137, 991-1007.
DOI
|
29 |
Notaros, B. M., and Coauthors, 2016, Accurate characterization of winter precipitation using multi-angle snowflake camera, visual hull, advanced scattering methods and polarimetric radar. Atmosphere, 7(6), 81.
DOI
|
30 |
Skamarock, W. C., and Coauthors, 2008, A description of the advanced research WRF version 3. NCAR Technical Note NCAR/TN-4751STR, 113 pp.
|
31 |
Stanford, M., Morrison, H., Varble, A., Berner, J., Wu, W., McFarquhar, G., and Milbrandt, J., 2019, Sensitivity of simulated deep convection to a stochastic ice microphysics framework. Journal of Advances in Modeling Earth Systems, 11, 3362-3389.
DOI
|
32 |
Song, H.-J., and Sohn, B. J., 2018, An evaluation of WRF microphysics schemes for simulating the warm-type heavy rain over the Korean peninsula. Asia-Pacific Journal of Atmospheric Sciences, 54, 1-12.
DOI
|