Numerical Simulation of Near-Surface Wind during a Severe Wind Event in a Complex Terrain by Multisource Data Assimilation and Dynamic Downscaling

De Zhang,Luyuan Chen,Juan Tan,Chenghai Wang,Feimin Zhang,Nir Y Krakauer

doi:10.1155/2020/7910532

Abstract

Accurate forecast and simulation of near-surface wind is a great challenge for numerical weather prediction models due to the significant transient and intermittent nature of near-surface wind. Based on the analyses of the impact of assimilating in situ and Advanced Tiros Operational Vertical Sounder (ATOVS) satellite radiance data on the simulation of near-surface wind during a severe wind event, using the new generation mesoscale Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system, the dynamic downscaling of near-surface wind is further investigated by coupling the microscale California Meteorological (CALMET) model with the WRF and its 3DVAR system. Results indicate that assimilating in situ and ATOVS radiance observations strengthens the airflow across the Alataw valley and triggers the downward transport of momentum from the upper atmosphere in the downstream area of the valley in the initial conditions, thus improving near-surface wind simulations. Further investigations indicate that the CALMET model provides more refined microtopographic structures than the WRF model in the vicinity of the wind towers. Although using the CALMET model achieves the best simulation of near-surface wind through dynamic downscaling of the output from the WRF and its 3DVAR assimilation, the simulation improvements of near-surface wind speed are mainly within 1 m s−1. Specifically, the mean improvement proportions of near-surface wind speed are 64.8% for the whole simulation period, 58.7% for the severe wind period, 68.3% for the severe wind decay period, and 75.4% for the weak wind period. The observed near-surface wind directions in the weak wind conditions are better simulated in the coupled model with CALMET downscaling than in the WRF and its 3DVAR system. It is concluded that the simulation improvements of CALMET downscaling are distinct when near-surface winds are weak, and the downscaling effects are mainly manifested in the simulation of near-surface wind directions.

Highlights

Severe wind, which is defined as the instantaneous wind speed reaches or exceeds 17 m s−1, is a frequently experienced extreme weather event in arid and semi-arid regions of Northwest China [1]
This study aims to (1) investigate the impacts of in situ and Advanced Tiros Operational Vertical Sounder (ATOVS) radiance data assimilation on the simulation of a severe wind event and (2) examine the dynamic downscaling effects of local microtopography in the California Meteorological (CALMET) model, especially when near-surface wind is strong and weak. ese will be done by numerical simulation of a severe wind episode that happened in the Alataw valley and its vicinity area. is area is focused because it is among the areas in China that most frequently experience severe wind [1] and is an important wind energy base in China and hosts the railway crossings of the second Eurasian continental bridge
Results indicate that CALMET downscaling overall improves near-surface wind speed simulation for different wind periods, the mean improvement proportions of near-surface wind speed are 64.8% for the whole simulation period, 58.7% for the severe wind period, 68.3% for the severe wind decay period, and 75.4% for the weak wind period. ese results suggest that the simulation improvements of CALMET downscaling are distinct when near-surface winds are weak

Summary

Introduction

Severe wind, which is defined as the instantaneous wind speed reaches or exceeds 17 m s−1, is a frequently experienced extreme weather event in arid and semi-arid regions of Northwest China [1]. Improving the forecast accuracy of near-surface wind (wind speed and wind direction), especially over a complex terrain, is very important for wind farm safety and air pollutant prediction etc. Such forecasting, remains a major challenge for numerical weather prediction models [2, 3]. E case study based on WRF simulation showed that the downward momentum transport caused by atmospheric baroclinicity and local atmospheric instability induced by surface diabatic heating, are closely related to severe wind in North-west China [4] Results of Wang et al [3] indicated that the mesoscale Weather Research and Forecasting (WRF) model produced a better simulation of nearsurface wind speed in April when surface diabatic heating is stronger, compared with January. e case study based on WRF simulation showed that the downward momentum transport caused by atmospheric baroclinicity and local atmospheric instability induced by surface diabatic heating, are closely related to severe wind in North-west China [4].

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Results

Conclusion