Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering,
Nanjing University of Information Science and Technology, Nanjing, China
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering,
Nanjing University of Information Science and Technology, Nanjing, China
Arnold Heemink
Delft Institute of Applied Mathematics, Delft University of Technology, Delft, the Netherlands
Richard Kranenburg
TNO, Department of Climate, Air and Sustainability, Utrecht, the Netherlands
Data assimilation provides a powerful tool to estimate emission inventories by feeding observations. This emission inversion relies on the correct assumption about the emission uncertainty, which describes the potential spatiotemporal spreads of sources. However, an unrepresentative uncertainty is unavoidable. Especially in the complex dust emission, the uncertainties can hardly all be taken into account. This study reports how adjoint can be used to detect errors in the emission uncertainty.
Data assimilation provides a powerful tool to estimate emission inventories by feeding...
