During the 1950s, Lorenz became skeptical of the appropriateness of the linear statistical models in meteorology, as most atmospheric phenomena involved in weather forecasting are non-linear. Modern numerical weather prediction has answered Lorenz’ challenge through combining sophisticated physics based numerical models with increasingly accurate high-dimensional data. This process is called data assimilation and it is performed every day at all major operational centres across the world. Data assimilation (DA) requires massive computing capabilities as realistic atmosphere-ocean models typically have billions of degrees of freedom. The objective of the ongoing research that I am involved (see details at
https://www.imperial.ac.uk/ocean-dynamics-synergy/ ) is to drastically decrease the required DA computational effort by reducing the dimension of the models involved and use stochastic perturbations to account for the unresolved scales. The incorporation of observation data is done using particle filters suitably adapted to solve high-dimensional problems.
This work is part of the Mathematics for Planet Earth programme which the speaker will introduce briefly.
Dan Crisan is a Professor of Mathematics at Imperial College London. Crisan’s research lies at the interface between Mathematics Analysis and Probability Theory. He is particularly interested in studying macroscopic models such as solutions of partial differential equations through their microscopic and stochastic counterparts. Some of his key contributions relevant to the proposed research include: the theoretical justiﬁcation for particle approximations for linear parabolic SPDEs; a sequential Monte Carlo method stable in the state space dimension; a reﬁned analysis of the smoothness of solutions of semi-linear PDEs, a new McKean-Vlasov approximation for the Kushner-Stratonovich equation. His research is acknowledged by the scientiﬁc community at large, as illustrated by his many invitations at pure/applied mathematics, engineering and statistics conferences. He has pioneered the application of particle filters in data assimilation.
Crisan first came to Imperial in 1995 as a postdoctoral fellow. After a brief spell at the Statistical Laboratory in Cambridge, Crisan returned to Imperial in 2000, where he was awarded a Governors' Lectureship. Since then, he has assiduously promoted Stochastic Analysis in the Department of Mathematics, across the College and beyond. In December 2002, Crisan initiated the Stochastic Analysis (SA) group at Imperial College London. The SA group is now one of the largest and most successful research groups in the UK.
In 2013, Crisan became the Director of the newly founded Centre for Doctoral Training in the Mathematics of Planet Earth. For his work in establishing the Centre, Crisan was awarded the 2018 President’s Award for Excellence in Research Supervision. Crisan has worked continuously not just to ensure the success of the Centre but also to promote the new research area of Mathematics of Planet Earth. Crisan is one of the founding editors of the new series of Springer Briefs in Mathematics of Planet Earth. Weather, Climate, Oceans. Crisan is the recipient of a 2018 Chair of Excellence to held at Universidad Carlos III de Madrid.
Dan Crisan was appointed Senior Coordinator at the International Mathematics Olympiad (IMO) 2019. In 2019, the UK hosted the 60th edition of the International Mathematics Olympiad (IMO) see https://www.imo2019.uk . The IMO is the largest and most prestigious of all of the international Olympiads. Initiated by Romania in 1959, the IMO has grown from the original seven countries to over a hundred to-date. The United Kingdom has participated since 1967 and has played host to the competition on two previous occasions (in 1979 and 2002). Dan Crisan was appointed Senior Coordinator also at IMO1999 and IMO2002.
In October 2019, Dan Crisan was awarded a £10 mil ERC Synergy grant jointly with Bertrand Chapron (Ifremer), Darryl Holm (Imperial) and Etienne Memin (Inria). This was the result of nearly two years’ hard work for the four PI, passing through several selection stages.