Davide Faranda
01.69.08.85.41 Bat 714 - Piece 1007 Orme des Merisiers
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Continuous Time Analysis of North Atlantic Circulation
Matlab Script
R Scripts
Python Scripts
I am a CNRS (permanent) Researcher in Climate Sciences at the LSCE laboratory of the University of Paris-Saclay, and the coordinator (chef d'equipe) of the ESTIMR group. My main expertise is the attribution of weather extreme events to climate change. Since September 2017, I am also external fellow of the London Mathematical Laboratory, London, United Kingdom and of the Laboratoire de Meteorologie Dynamique de l'Ecole Normale Superieure in Paris.
Attribution of Weather Extremes events to Climate Change
My contributions in this fields of research are directed towards the understanding of the relation between the recurrences of climate extreme events and the processes that modify their dynamics with the climate change. I am particularly interested in cold and snowy spells, heatwaves and extreme convective events.
Critical Phenomena in Climate & Complex Systems
The understanding of the mechanism regulating the transitions between different attracting states in complex systems is a general problem in statistical mechanics. Systems which feature critical phenomena range from spin glasses up to finance, the climate systems and epidemiology. I have been involved in developing rigorous statistical methods for detecting the transition thresholds in datasets and in the modeling of systems at bifurcation points via the so called ARMA (Auto Regressive Moving Average) processes technique.
Dynamical Systems methods for the analysis for Turbulent and Geophysical flows
Providing a statistical description of turbulence, by combining theoretical findings with high quality experimental datasets, is helping in understanding several features of turbulent flows as the dissipation anomaly or the existence of singularities in the Navier Stokes equations. I am actually contributing to this research field by developing statistical techniques based on the Extreme Value Theory and the ARMA process analysis which allows for quantify the distance between observations and theoretical models in a rich model-parameter space.
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