Dr Lorenzo Tomassini

Profile

I'm an Associate Professor in the School of Mathematics at the University of Leeds, and a Senior Scientist at the UK Met Office. I started my scientific journey with a degree in Mathematics and a Ph.D. in Mathematical Physics at the Swiss Federal Institute of Technology (ETH) in Zurich, Switzerland.

After a postdoctoral position at Northwestern University in Chicago, I decided to move into environmental sciences and began working on uncertainty quantification in climate modelling, employing Markov chain Monte Carlo methods in a Robust Bayesian framework. Investigating uncertainties in future climate projections made me aware of the importance of better understanding the physical processes involved.

After changing to a scientist post at the Max Planck Institute for Meteorology in Hamburg, Germany, my focus became the study of moist convection and the interaction of moist diabatic processes with the atmospheric circulation. In this context, it became clear that we need to try to resolve physical processes in numerical models of the atmosphere where possible, and I got involved in high-resolution, convection-resolving and convection-permitting modelling.

I joined the Global Atmospheric Model Development group of the Met Office in 2015. The group develops the global atmospheric model that the Met Office uses across a wide range of time and spatial scales, and I was particularly engaged in developing a km-scale resolution, convection-permitting configuration of that global model. Moreover, I'm convinced that in order to make progress in better understanding and predicting weather and climate, a hierarchy of models of different complexities needs to be considered and studied.

More recently, I have also returned to some of my roots in data science, and I'm interested in collaborating on applying machine learning techniques to problems in atmospheric and climate science, in particular in conjunction with convective-scale modelling.

Research interests

Steep advances in computational capabilities and data science are revolutionising weather and climate science. I'm interested in combining ultra-high resolution numerical modelling and novel data science techniques to advance this digital revolution in atmospheric and climate science with a focus on convective scales and processes.

In particular, my research covers the interaction between moist convective processes and the atmospheric circulation on a wide range of scales. The interaction between moist convection and the large-scale atmospheric dynamics is at the heart of gaps in our current understanding of weather and climate. How do environmental conditions influence convective development, and how does smaller-scale moist convection feed back onto the larger scales? These questions are also key in the context of better understanding and predicting climate change.

Moreover, I'm interested in applying Bayesian statistics and new machine learning techniques to atmospheric and climate science with a processed-based view. I'm using new satellite observations to fuse observations and high-resolution numerical models, and I'm interested in the quantification of uncertainty in weather and climate modelling. Another focus of mine is the development and utilisation of a hierarchy of models of different complexities in atmospheric and climate science for better process understanding.

Qualifications

• 1990: Baccalaureat in Philosophie (Hochschule fuer Philosophie, Munich)
• 1998: Diplom Mathematiker ETH
• 2002: Ph.D. in Mathematics (ETH Zurich)
• 2007: Ph.D. in Natural Sciences (ETH Zurich)