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In this paper, the impact of urban surface roughness length

With the intensified human activities and urbanization processes, the impacts of urbanization on atmospheric environment (e.g., urban heat islands and urban breezes) at the local to regional scale have drawn considerable attention during recent years [

Generally, turbulent exchanges over urban areas are much more complicated and geometry-dependent [

A number of numerical simulations have demonstrated the importance of

Theoretically,

This paper aims to evaluate the role of the improved

The mesoscale atmospheric model used in this study is the WRF model with Advanced Research WRF (ARW) dynamic core version 3.2 [

In the framework of UCM, the considerable complexity of the urban land surface is reduced to a street canyon, where a road is bordered by two facing building walls. As shown in Figure

Schematic representation of the urban canyon geometry in WRF-UCM.

As described in Section

It is worth mentioning that the calculation of urban energy budget is split into two parts in UCM: one for the canyon (floor and wall) and another for the roof. In this context, there are in total three parameters in UCM reflecting the effects of

Macdonald et al. [

Based upon Shao and Yang [

The WRF-UCM model was run at 9, 3, and 1 km horizontal grid spacings with

(a) Domain configuration of the WRF model simulation with terrain height (shaded, unit: m). (b) Land use/land cover in the innermost domain. The red color points to the urban and built-up areas, and the yellow color represents the croplands. The text “T” and “F” indicate the location of Beijing 325 m Meteorological (B325) Tower and Fangzhuang, respectively.

Two numerical experiments using different

Besides, as our main objective is to demonstrate the role of the improved

Generally, there are two mechanisms for

Figure

Spatial distribution of the three roughness parameters (

Friction velocity

(a) Diurnal variation of the friction velocity

The main differences between the results from the EXP run and the CTL run lie in the magnitude. As depicted in Figure

For urban grids, sensible heat flux

The same as Figure

Similar to the change of

At Fangzhuang, a tethered balloon for obtaining detailed meteorological soundings in the lower 1 km of the atmosphere was made every 3 hour to measure the wind, temperature, and humidity profiles. Figure

(a) Observed and the simulated mean wind speed

At 2000 LST (Figure

Figure

(a) Observed and the simulated mean temperature (°C) profile at Fangzhuang and (b) differences between the EXP run and the CTL run at 0200 LST 28 February 2001. (c) and (d) are, respectively, the same as (a) and (b), but for 1400 LST 28 February 2001.

At 1400 LST (Figure

Bulk transfer coefficients are key parameters for determining the transfer efficiency of momentum, heat, and moisture from the underlying surface to atmospheric reference height. In the WRF-UCM model, the expression for the bulk transfer coefficient of heat (

For the expression of the bulk transfer coefficient of momentum (

Figure

The momentum bulk transfer coefficient (

Turbulent kinetic energy (TKE) is a measure of turbulence intensity, which is closely related to the transport of momentum, heat, and moisture through the boundary layer. Figure

Diurnal variation of turbulent kinetic energy

On the whole, the EXP run simulates a stronger development of TKE than the CTL run. The differences between both runs become apparent after 1200 LST, with the most substantial positive difference (0.33 m^{2}

By introducing a new

The comparison between simulated results shows that all the roughness parameters in the EXP run are larger than those in the CTL run, which indicates that, with the inclusion of building height variability, the SY08N scheme gives a higher estimation of

The comparison with observations demonstrates relatively better simulation of urban boundary-layer structures and land surface-atmosphere exchanges by the EXP run. For the boundary-layer temperature structures, both the EXP run and the CTL run reproduce the observed vertical profile and also the inversion layer at nighttime. For the boundary-layer wind structures, the EXP run reasonably reproduces the nocturnal low-level jet at around 200 m while the CTL run fails. Besides, the magnitude of land surface-atmosphere exchanges (e.g., friction velocity and sensible heat flux) as simulated by the EXP run is much closer to the observation. All these indicate that the SY08N scheme can improve the model performance in simulating the urban atmospheric environment.

Nevertheless, it is worth mentioning that we used the default settings for urban land use information in the CTL run and the EXP run. The urban land use information given by the default settings is “imaginary” and might not be representative of the Greater Beijing area. For example, roughness parameters were homogeneously distributed over the entire urban region in both runs (Figure

Aerodynamic roughness length (m)

Building width (m), as illustrated in Figure

Road width (m), as illustrated in Figure

Average building height (m), as illustrated in Figure

Building height variability (m), as illustrated in Figure

Plan area index

Frontal area index

Frontal area index

Canyon (floor and wall) aerodynamic roughness length (m)

Canyon (floor and wall) zero-plane displacement height (m)

Roof aerodynamic roughness length (m)

Von Karman constant (—)

Friction velocity

Sensible heat flux

Sensible heat flux

Sensible heat flux

Sensible heat flux

Bulk transfer coefficient of heat (—)

Mean wind speed

Friction velocity at the height of 2 m

Roughness length for heat (m)

Universal function of heat (—)

Background surface roughness length (m)

Empirical constant in the expression for

Roughness Reynolds number (—)

Kinematic molecular viscosity

Bulk transfer coefficient of momentum (—)

Atmospheric reference height (m)

Universal function of momentum (—)

Drag coefficient (—) in the MGH98 scheme

Empirical coefficient (—) in the MGH98 scheme

Empirical coefficient (—) in the MGH98 scheme

Reference height (m) in the SY08N scheme

Mean speed at the reference height

Friction velocity

Roughness element drag coefficient at

Surface drag coefficient at

Surface drag coefficient at

Roughness length for bare surface

Roughness length for fully covered surface in the SY08N scheme

Correction factor for drag coefficient (—) in the SY08N scheme

Correction factor for drag coefficient (—) in the SY08N scheme

Correction factor for drag coefficient (—) in the SY08N scheme

Empirical constant (—) in the SY08N scheme

Empirical constant (—) in the SY08N scheme

Empirical constant (—) in the SY08N scheme

Total drag

Pressure drag

Ground-surface drag

Roughness-element-surface skin drag (kg m

Ratio of

Empirical constant (—) in the SY08N scheme for

Empirical constant (—) in the SY08N scheme for

The authors declare that there is no conflict of interests regarding the publication of this paper.

This research is jointly supported by the “Strategic Priority Research Program” of the Chinese Academy of Sciences (Grant no. XDA05110000) and the National Natural Science Foundation of China (Grant no. 41305093). The authors thank Professor Zhiqiu Gao for providing the observational datasets for validation.