Mars Global Climate Models (GCMs)  provide researchers with realistic and reliable models of the Martian atmosphere. Yet, existing modelling techniques are still inefficient for predicting regional/global dust events on Mars, which is one of the biggest topics in Mars science today . Machine Learning in general, and Deep Learning (DL) in particular, are increasingly used in climate predictions , and this PhD project will investigate DL solutions for climate prediction. It will leverage the OU OpenMars data to build DL solutions to forecast Mars weather, focusing on dust storms. Our aim is to develop the capability to predict global-scale dust storms on Mars, a capability that is critical to the human exploration of Mars.
The project will be supervised by a multi-disciplinary team of researchers with expertise in software engineering and machine learning as well as expertise in modelling the Martian atmosphere.
For students with background in physics
For students with background in computer science
 Forget, F., Hourdin, F., Fournier, R., Hourdin, C., Talagrand, O., Collins, M., Lewis, S.R., Read, P.L., & Huot, J.-P. (1999). Improved general circulation models of the Martian atmosphere from the surface to above 80 km. J. Geophys. Res. 104 (E10), 24155–24176.
 Kahre, M. A., Murphy, J. R., Newman, C. E., Wilson, R. J., Cantor, B. A., Lemmon, M. T., & Wolff, M. J. (2017). The Mars dust cycle. In R.M. Haberle (Ed.), The atmosphere and climate of Mars (pp. 229–294). Cambridge: Cambridge University Press.
 Toms, B. A., Barnes, E. A. & Hurrell, J.W. (2021). Assessing Decadal Predictability in an Earth-System Model Using Explainable Neural Networks. Geophys. Res. Lett., 48(12), e2021GL093842.
For more information about the project, please contact the follwoing academisc: