Development of an Effective Double-Moment Cloud Microphysics Scheme with Prognostic Cloud Condensation Nuclei (CCN) for Weather and Climate Models

Abstract

A new double-moment bulk cloud microphysics scheme, the Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) Microphysics scheme, which is based on the WRF Single-Moment 6-class (WSM6) Microphysics scheme, has been developed. In addition to the prediction for the mixing ratios of six water species (water vapor, cloud droplets, cloud ice, snow, rain, and graupel) in the WSM6 scheme, the number concentrations for cloud and rainwater are also predicted in the WDM6 scheme, together with a prognostic variable of cloud condensation nuclei (CCN) number concentration. The new scheme was evaluated on an idealized 2D thunderstorm test bed. Compared to the simulations from the WSM6 scheme, there are greater differences in the droplet concentration between the convective core and stratiform region in WDM6. The reduction of light precipitation and the increase of moderate precipitation accompanying a marked radar bright band near the freezing level from the WDM6 simulation tend to alleviate existing systematic biases in the case of the WSM6 scheme. The strength of this new microphysics scheme is its ability to allow flexibility in variable raindrop size distribution by predicting the number concentrations of clouds and rain, coupled with the explicit CCN distribution, at a reasonable computational cost.

High‐resolution simulation over central Europe: assimilation experiments during COPS IOP 9c

“…Currently the focuses are set on planetary boundary-layer (PBL) schemes (e.g. Hong et al, 2006;Hong, 2007), new two-moment cloud microphysics schemes predicting not only hydrometeor quantities but also their number concentrations (Thompson et al, 2008;Morrison et al, 2009;Lim and Hong, 2010) and land-surface models (LSMs; e.g. Ek et al, 2003).…”

Implementation of aerosol–cloud interactions in the regional atmosphere–aerosol model COSMO-MUSCAT(5.0) and evaluation using satellite data

“…For example, the Weather Research and Forecasting (WRF) model is available with different types of two-moment microphysical schemes (Thompson et al, 2008;Morrison and Gettelman, 2008;Lim and Hong, 2010). Morrison et al (2009) showed that using a two-moment scheme in the WRF model produced more trailing stratiform precipitation in an idealized two-dimensional squall case, which is more consistent with observations.…”

“…In another study, Li et al (2008) investigated the effect of aerosol on cloud microphysical processes with a two-moment microphysical scheme in the WRF model. Also, Lim and Hong (2010) have included a prognostic equation for cloud water and cloud condensation nuclei number concentration (C ccn ), which could reduce the uncertainty in investigating the aerosol effect on cloud properties and the precipitation process in the WRF model. Seifert et al (2012) and Weverberg et al (2014) compared the operational one-moment and two-moment microphysical schemes in the Consortium for Small-Scale Modeling atmospheric model (COSMO).…”

Numerical simulation of the heavy rainfall caused by a convection band over Korea: a case study on the comparison of WRF and CReSS

“…The Advanced Research WRF solver developed at the National Center for Atmospheric Research was used for the dynamic core, which is a fully compressible and non-hydrostatic model. The physical parameterizations include the 5-layer thermal diffusion land-surface model (Chen and Dudhia 2001), the Yonsei University (YSU) planetary boundary layer scheme (Hong et al 2006), the Rapid Radiative Transfer Model (RRTMG) longwave radiation scheme (Malwer et al 1997;Iacono et al 2008), Goddard shortwave radiation scheme (Tao et al 1989), the Double-Moment (WDM) 6-class microphysics scheme (Lim and Hong 2010) and none cumulus parameterization scheme. We selected this combination of the physical schemes based on previous studies to optimize the WRF performance over the domain, including the Korean peninsula throughout various sensitivity experiments (e.g., Hong and Lee 2009;Lim and Hong 2010;Cho and Lee 2006).…”