Pbl Data Assimilation and Its Impact on Near-Surface Prediction( I )

Project Details


This project aims to establish a high-resolution ensemble data assimilation system for the planetary boundary layer over the Taiwan complex terrain (PBL EDA system) by combining the intense observations in PBL and high-resolution meteorology and air-quality model. We plan to assimilate surface data, UAV, aerosol lidar, and regular and ship-based radiosondes. By taking advantage of the high vertical resolution of the observations, prediction initialized from the PBL EDA analysis can better represent the PBL variations under the complex terrain of Taiwan. Besides, the ensemble simulation helps to establish the sensitivity of near-surface prediction, which allows us to explore the possibility of installing the regular PBL observations.Based on the PBL DA framework, the 4-dimensional analysis product provides the potential to understand the development of PBL under the complex terrain, the variability of leeside flow, and how these may modulate the transportation of the air pollutant in the PBL. The PBL EDA system allows building the key to improve air quality and wind energy prediction. In order to mitigate the near-surface model bias, this project will develop the bias correction methods based on multi-variable regression and neural networks. These methods will be integrated into the PBL data assimilation framework.
Effective start/end date1/08/2031/08/22

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):

  • SDG 6 - Clean Water and Sanitation
  • SDG 11 - Sustainable Cities and Communities
  • SDG 17 - Partnerships for the Goals


  • Data assimilation
  • Ensemble Kalman filter
  • near-surface prediction
  • PBL
  • air quality prediction


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