Dr Omar Jamil
Omar works on radiative transfer and improving the physics parametrisations using machine learning.
Areas of expertise
- High resolution atmospheric radiative transfer
- Machine learning and statistical methods
- Parametrisation developments
Omar is a Senior Scientist working on ways to improve the parametrizations using machine learning and statistical techniques. He also works on improving the physics in the atmospheric radiative transfer scheme, with a specialism in line-by-line modelling.
One of the areas of research involves improving the spectral resolution in the radiative transfer model and assessing the impact of widely used low-resolution parametrizations on the weather and climate models. The resolution of the radiation scheme can have a direct impact on the atmospheric radiative forcing which is particularly important for the climate models.
The other focus of research is to use statistical and machine learning techniques to use the most accurate physics in our models, but at the lowest possible compute cost. In order to achieve this, Omar is using the latest machine learning frameworks with techniques such as deep learning.
Omar joined the Met Office in 2012. Prior to joining the Met Office, he was a post-doctoral research fellow in theoretical astrophysics at Ohio University. He holds a PhD in Physics from University of Southampton and an MPhys from University of Warwick.