The study proposes to utilize the ensemble data assimilation system to assimilate the comprehensive planetary boundary layer observation datasets into Weather Research and Forecasting (WRF)model and to develop a high-resolution boundary layer analysis dataset.The intensive field observations will be conducted to establish the atmospheric planetary boundary layer observations including the wind profiler, sounding, unmanned aerial vehicle (UAV), lidar, surface datasets and satellite datasets. The sounding and UAV observation will be deployed over the ocean, western Taiwan, and the inland mountainous area. The high-resolution analysis datasets are expected to provide a comprehensive understanding of the development of the atmospheric dynamic/thermodynamic processes over the complex topography, and to figure out the formation mechanisms of the leeside wake/vortex/land-sea breeze/mountain-valley flow structures. The interaction between the wind circulations and complex terrain will be investigated through the analysis of the fine scale meteorological and air quality modeling and the observed datasets. Furthermore, the air pollution transport processes can be illustrated in a detailed manner.Moreover, the data science and deep learning algorithms will be conducted to study the air pollution dispersion processes that is unable to be resolved from the dynamic model. The objective of this study is to provide a high-resolution planetary boundary layer analysis datasets; furthermore, to propose a data assimilation strategy that is helpful to enhance the weather and air quality forecasting system predictability, and to support the assessment of the use of the green energy.
|Effective start/end date||1/08/20 → 31/07/21|
UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):
- atmospheric planetary boundary layer
- ensemble data assimilation system
- high resolution meteorological model
- air quality
- air pollution dispersions
- green energy
- deep learning
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