Model Setup and Experimental Design

The dust transport model used here is based on a regional meteorological model, the Advanced Regional Prediction System (ARPS),11 which was developed by The University of Oklahoma Center for Analysis and Prediction of Storms. The emission and transport processes of dust aerosol have been built into the ARPS model.12 The model is configured in an idealized way as in the study of Takemi et al.6'7 in order to focus on the fundamental dynamics of convective dust transport under a fair weather condition. The meteorological model includes full physics parameterizations, i.e. cloud microphysics,13 subgrid-scale (SGS) turbulence mixing,14 land-surface physics, and radiative transfer.15 In the dust module, the vertical dust flux at the surface is determined as the fourth power of friction velocity,16 and the atmospheric transport is computed by the velocities obtained from the atmospheric model. The threshold of the friction velocity for dust emission is set to be 60 cm s~1. The dust property is represented as a mixing ratio, and a single size bin of 1.0-yu,mradius is assumed.

In order to perform high-resolution simulations that would explicitly resolve boundary-layer eddies, a large-eddy simulation (LES) model of Deardorff14 is used for the parameterization of SGS turbulence mixing. The turbulence length-scale depends on the stability, and has the same value for both the horizontal and the vertical directions.

A high-resolution simulation with the horizontal grid spacing (Ax) of 100 m and the vertical grid spacings of 20-240 m (85 levels) is conducted in a mesoscale domain of 40 km (east-west, the x-axis) x 10 km (north-south, the y-axis) x 11km (vertical, the z-axis). Although the spacing of 100 m seems to be relatively larger for LES, recent studies using this grid size have been successful in representing both shallow and deep convection;17'18 therefore, we consider that the 100 m grid is sufficient for simulating both shallow and deep convection and the associated dust transport. A periodic condition is imposed at all the lateral boundaries, and the upper boundary is a rigid lid with a Rayleigh-type damping layer above the 9 km height. This 100 m grid simulation is referred to as the control.

In addition to the 100 m grid simulation, a series of experiments examining the sensitivity to horizontal grid spacing in the range of cloud-resolving simulations are performed. We set Ax = 250 m, 500 m, 1km, 2 km, and 4 km. These simulations are conducted in a larger computational domain of 80 km x 20 km x 11 km, since a coarser-grid simulation will require a larger domain for resolving cloud-scales, i.e. a couple of kilometers. We have confirmed that the difference in computational area does not affect significantly the convection and transport processes by comparing the results with the two different computational areas in the case of 250 m grid. In addition, coarser vertical grid spacings of 20-650 m (36 levels) are used. In order to enhance the vertical mixing with these vertical grids, a type of nonlocal mixing scheme19 that modify the turbulence length-scale in the vertical is employed.

The initial base state is set to the horizontal averages over the computational area after a three-day spin-up run with the 250-m grid starting with the vertical profile of Yinchuan, China, in the southern Gobi Desert, at 0600 LT (local time at this longitude) on 13 April 2002. After this spin-up run, a clear diurnal variation was represented. This base state, shown in Fig. 1, is used for initializing the model with random temperature perturbations added below the 1 km height, and has been used in our previous studies.6'7 The model is integrated in time for 12h for all the simulation cases. The analyses are conducted for the model outputs at 300-s interval.

Fig. 1. Initial vertical profiles of (a) potential temperature (in K); (b) water vapor mixing ratio (in kgkg~-*-); and (c) horizontal wind speed (in ms~1, ^-component).
Renewable Energy Eco Friendly

Renewable Energy Eco Friendly

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable.

Get My Free Ebook

Post a comment