亞洲大氣污染物之整合監測與其對環境及氣候之衝擊研究-國際參與(II)-整合衛星與地面觀測氣膠參數在空氣品質預報三維初始場之建構與應用(總計畫及子計畫六)

Quantifying aerosol compositions from remotely sensed measurements by spaceborne, suborbital and ground-based platforms is a challenging task. In terms of particle size distribution and complex refractive index, the normalized first- & second-order spectral derivatives of spectral aerosol optical depths (Normalized Derivative Aerosol Indexes, NDAIs) are explored to partition the major components of aerosols. The intrinsic values from NDAIs are characterized for collective models of aerosol types, including mineral dust (DS), biomass burning (BB) and anthropogenic pollutants (AP), and stretching out to their weightings with a unique pattern clearly exhibited for bounding the major aerosol components; in turn, fractions of the total AOD (fAOD) for major aerosolcomponents can be identified. The subtlety of NDAI method validated by in situ typical aerosol cases indicated highly practicable for quantifying fAOD among mixed-type aerosols with normalized AOD spectral derivatives. The vertical distribution of aerosols is significant for accurate surface PM2.5 retrieval and the initial modeling forecasts of air pollution, while the observation of aerosol profiles on the regional scale is seriously limited. Concerning the uncertainty of assuming well-mixed aerosols within the planetary boundary layer, a sensible aerosol vertical distribution described by a log-normal fitting function is proposed to provide more realistic single-peak extinction profile with a decadal Micro Pulse LiDAR (MPL) in situ dataset. These approaches currently apply on Terra- and Aqua-MODIS observation to generate 3-dimensional aerosol distributions twice aday. To satisfy the requirement in high temporal monitor, this research will aim to (1) apply this approach on Himawari-8 to provide every 10 minutes 3-dimension aerosols information and the evaluate with ground base monitoring in the first year. (2) The second year will focus on applying 3-dimension aerosols information to improve initial condition and forecasting for air quality based on WRF-chem model.