[세미나] Prof. Kai Qin

Prof. Kai Qin (China University of Mining and Technology)

2022년 3월 8일 (화) 16:00

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Abstract

Satellite remote sensing observations are essential for understanding the atmospheric environment, especially in regions such as China where air quality needs to be improved. Here, the speaker will present research progress of his group in five aspects. (1) Absorbing aerosol optical depth from OMI/TROPOMI based on the GBRT algorithm and AERONET data in Asia. To quickly obtain AAOD with high-resolution and high-accuracy, the GBRT method based on the joint data from OMI, MODIS and AERONET is used for TROPOMI. Compared with the ground-based data, the correlation coefficient of the results is greater than 0.67 and the difference are generally within ±0.03. (2) Himawari-8-derived aerosol optical depth using an improved time series algorithm over eastern China. The large amount of spectral data with differing observation geometries requires re-formulation of the surface reflectance correction to utilize the Himawari-8 data. This was achieved by using an improved version of the time series technique based on the assumption that the ratio of the surface reflectance in different spectral bands does not change between any two scan times within an hour. (3) A higher-coverage tropospheric NO2 dataset by merging OMI and GOME-2. Tropospheric NO2 columns retrieved from OMI are widely used, even though there is a significant loss of spatial coverage due to multiple factors. This work will introduce a framework for reconstructing gaps in the OMI NO2 data by using machine learning and an adaptive weighted temporal fitting method with NO2 measurements from GOME-2B, and surface measurements. (4) Model-free Mass-Conserving Inversion of Daily NOx Emissions from TROPOMI. This work will introduce a new approach to compute NOx emissions, specifically based on daily remotely sensed column measurements of NO2 from TROPOMI at 3.5km x 5km spatial resolution, reanalysis 3-hourly wind fields, and a 4-term approximation of mass conservation. This technique permits a robust calculation of the impacts of the underlying thermodynamics driving the emissions and production of the NOx/NO2 ratio, the dynamical transport in-situ, and a first order approximation of the underlying chemical destruction of NOx in-situ. (5) Significant contributions of TROPOMI CO, NO2 and HCHO column measurements on surface ozone prediction. Due to the sensitivity of satellite sensors to the stratospheric ozone signal, it is difficult to estimate near-surface ozone variability using only the total ozone concentration from satellite observations. Eclectically, the remotely sensed columns of NO2 and HCHO were used for inferring surface ozone. Higher importance of CO, moderate impact of NO2 and a small number of relevant contributions from HCHO were found.