I mainly use Python (primary libraries: Numpy, xarray, Pandas, Matplotlib, Jupyter, Dask) to analyse the large amounts of data produced by complex air quality models.
I provided training for scientific computing (Python, Linux, and GitHub). For example, explaining how to speed up Python code.
Complex air quality model¶
I taught and provided support for a complex air quality model, WRFChem (Bash and Fortran).
Emulation of complex air quality model¶
Here I used machine learning models (Gaussian process emulation trained from ~20 TB of simulated data) to predict optimum emission reduction strategies to improve air quality and public health in China.