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In this study, ground-based S-band Doppler radars (KWK, GDK, IIA) operated by the Korea Meteorological Administration (KMA) were used to retrieve three-dimensional wind fields (Fig. 1). To avoid errors in the remotely sensed data, non-meteorological targets (birds, sea clutter and other unreasonable values) were eliminated using the method proposed by Zhang et al. (2004). The preprocessed radar data were interpolated to a Cartesian grid using the interpolation scheme proposed by Cressman (1959). The intervals of the horizontal and vertical grids were 1 and 0.25 km, respectively, with effective radii of 1.5 and 1.0 km, respectively. The amount of surface rainfall was recorded by the Automatic Weather System (AWS) of the KMA. The spatial distribution of rainfall is shown as the observation through a minimum curvature method (Smith and Wessel, 1990).
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Using variational-based wind analysis to treat the problem involved in the specification of the boundary condition between the top and bottom level has advantages for retrieving the appropriate vertical velocity (Liou and Chang, 2009). Moreover, Liou et al. (2012) suggested advanced radar wind synthesis of the three-dimensional wind field on a non-flat surface, and it was implemented without conversion to the terrain-following coordinate system by using the immersed boundary method (Tseng and Ferziger, 2003). Consequently, we created three-dimensional wind-field data using an algorithm designed by Liou et al. (2012), named WISSDOM (Wind Synthesis System using Doppler Measurement). It can estimate reasonable horizontal and vertical wind patterns considering terrain forcing, which is suitable for the wind field along the baseline, courtesy of resolving the anelastic continuity equation and the simplified vertical vorticity equation.
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The configuration of the Cloud-Resolving Storm Simulator (CReSS; Tsuboki and Sakakibara, 2002) V3.4 is given in Table 1. CReSS is based on the non-hydrostatic and compressible equation. The prognostic equation solves for the following five variables: (i) vector (u, v and w), (ii) perturbation of pressure, (iii) perturbation of potential temperature, (iv) mixing ratio (six categories) and (v) turbulent kinetic energy (TKE). The microphysics parameterization scheme is implemented by the bulk method of cold rain suggested by Lin et al. (1983), Ikawa and Saito (1991) and Murakami et al. (1994). This bulk scheme covers six types of hydrometeor—namely, water vapor, rain, cloud, ice, snow and graupel.
Feature Configuration Initial/boundary condition MSM forecast (3 h) Projection Lambert conformal Grid Arakawa C-type Microphysics Bulk cold rain with mass Top/bottom boundary conditions Rigid boundary Sponge layer Above 14 km AGL Horizontal/vertical advection Forth-order/second-order scheme Time splitting HE-VI Advection scheme Forth-order scheme (horizontal) Turbulent parameterization 1.5-order turbulence closure Surface processes Energy/momentum fluxes Table 1. Model configuration in CReSS.
The parameterization scheme for the surface boundary layer (SBL) is that suggested by Segami et al. (1989). CReSS simulates physical processes in the SBL for a shorter integration time than those of other well-known cloud resolving models (RAMS and ARPS), and is therefore appropriate for analyzing changes in the SBL. The potential temperature flux (θflux) used in the present study is vertically constant in the SBL and is expressed on the basis of the mixing-length theory of Prandtl (1925) as
where Hflux (J m−2 s−1) is the sensible heat flux, ρ is the air density (kg m−3) and Cp is the specific heat (1,004 J K kg−1). On the right-hand side of Eq. (1), Ch is the bulk coefficient of sensible heat and potential temperature (non-dimensional; Louis et al., 1982) and is formed by the roughness length Z0, which follows the Global Land Cover Characterization (GLCC) data for each type of surface (Table 2). The difference
$\left({\bar \theta - {\theta _{\rm{G}}}} \right)$ is the difference in θ between the SBL and the ground, and$\bar u$ is the mean zonal wind component of the SBL. The Reynolds stress to express surface momentum energy is given asDescription Albedo Z0,m Z0,h Evapotranspiration efficiency Water bodies 0.06 2.4×10−4 2.4×10−4 1.0 Urban and built-up land 0.25 0.5 0.1 0.05 Dry cropland and pasture 0.2 0.12 0.1 0.15 Irrigated cropland and pasture 0.1 0.075 0.1 0.6 Mixed dry/irrigated cropland and pasture 0.2 0.5 0.1 0.3 Cropland/grassland mosaic 0.2 0.4 0.1 0.5 Cropland/woodland mosaic 0.2 0.4 0.1 0.5 Table 2. Land description and surface constant (GLCC).
where τzx and τzy are the horizontal shear stress in the zonal and meridional direction, respectively.
We used the CReSS model to simulate the convective system from 0900 LST 26 July 2011 to 0600 LST 27 July 2011. The simulation domain is shown in Fig. 2. The mesoscale model (MSM) that is produced every 3 h by the Japan Meteorological Agency was used as the initial condition (0900 LST 26 July 2011). The control (CTL) and no-land (NL) experiments were nested to a 1-km grid from the D1 to D2 (2 km) domains. The experiments with 1-km resolution had a large time step of 2 s, contained 81 vertical levels and had a high resolution of 100 m below 1.5 km above the surface layer (ASL). To clarify the influence of surface discontinuity, we compared CTL with NL. The NL experiment was conducted by replacing the land region of the Korean Peninsula (below 38°N) with sea. Such an experiment with change in land cover is suitable for analyzing the impact of surface discontinuity (Baidya Roy and Avissar, 2002; Pielke Sr et al., 2007). The NL and CTL experiments both used the average MGDSST (Merged satellite and in-situ data Global Daily Sea Surface Temperature) of the Yellow Sea on 26 July 2011. In order to investigate the impact of surface discontinuity only, the NL experiment used the same topography as CTL.
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To quantify the processes that increase the precipitation in coastal regions, we used vertically integrated factors within effective layers. The factors considered were those parameters (i.e., turbulent flux, water vapor, TKE and potential temperature perturbation) that are recognized as substantial elements for nearshore precipitation. The formulas for the vertically integrated water-vapor turbulent flux (IWF) and the surface change factor (SCF) are
where V is the horizontal atmospheric motion vector,
${\overline { V}}$ is the spatial mean of the horizontal atmospheric motion, qv is the water vapor (kg m−3), g is the gravitational acceleration (m s−2), Z1 is the lowest level of the air (SBL level; 50 m) and Z2 is the maximum height at which the TKE reacts and energy transfer is possible in the lower layer (2000 m).