Abstract
The terrain of Central Asia is complex and rugged over mountains. Consequently, wind speed is overestimated over mountains and plains when using the Weather Research Forecast (WRF) model in winter. To solve this problem, three different simulations (named as control simulation (CRTL), gravity waves (GWD), and flow-blocking drag (FBD), respectively) were designed to investigate the impact of sub-grid orography (gravity waves and flow-blocking drag) on wind forecasts. The results illustrated that near-surface wind-speed overestimations were alleviated when sub-grid orographic drag was used in GWD, though the upper-level wind fields at 500 hPa were excessively reduced compared to CRTL. Thus, we propose eliminating the gravity wave breaking at the upper level to improve upper-level wind underestimations and surface wind speeds at the same time. The sub-grid orographic drag stress of the vertical profile over mountains was reduced when only the flow-blocking drag was retained in FBD. This alleviated underestimations of the upper-level wind speed and near-surface wind, which both have the same positive effects as the gravity wave and flow-blocking total. The mean bias and root mean squared error reduced by 32.76% and 9.39%, respectively, compared to CRTL. Moreover, the temperature and specific humidity in the lower troposphere were indirectly improved. The results of the study demonstrate that it is better to remove sub-grid orographic gravity wave drag when using the gravity wave drag scheme of the WRF model.
Highlights
The effects of orography can be represented by various means in large-scale models of the atmosphere, and their inclusion is crucial for successful simulations and forecasts of weather and climate [1]
We evaluated the performance of sub-grid orographic parameterizations to determine which one is more suitable for the complex terrain of Central Asia
We focus on period of atmospheric stratification stable, so the simulation period was selected as January 2019
Summary
The effects of orography can be represented by various means in large-scale models of the atmosphere, and their inclusion is crucial for successful simulations and forecasts of weather and climate [1]. FBD exerts an important effect on the circulation of the low-tropospheric atmosphere over mountains in complex terrain It directly controls wind fields, which further modulate water vapor and energy transport. The Weather Research Forecast (WRF) model parameterizes sub-grid gravity wave drag and FBD together as dynamical processes [1,9,10,11] to reduce the bias in wind speeds over complex terrain. This improves the performance of winds forecast at 10 m [12]. We discuss how to reduce upper-level wind underestimations based on this dynamic mechanism
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