DRF: Thesis SL-DRF-17-0773 - instn

DRF: Thesis SL-DRF-17-0773
RESEARCH FIELD
Climate modelling / Earth and environmental science
TITLE
Improving stochastic generators of climate/financial data using the underlying dynamics
ABSTRACT
Forecasting and understanding the behavior of complex systems such as turbulence, climate and finance is a challenging task. To tackle
this problem, various tools have been developed using dynamical systems theory, statistical mechanics or stochastic fits to the data using
e.g. Auto Regressive Moving Average (ARMA) processes. Such approaches are however limited by the presence of multiple metastable
states, that can trap the system in non-equilibrium quasi-steady state, or attract the trajectories in the phase space.
Examples of such meta-stable states are blocked and zonal flows in the mid-latitude atmospheric dynamics, crises and period of growth in
economy and finance. At present time, there is no general theory that allows the prediction of the plausibility of, time-life of or dynamics
around such meta-stable states. Improved description of the system dynamics of the trajectories in the presence of metastable states
have been recently obtained by splitting the original time-series in short subsamples that obey basic ARMA processes [1,2,3]. In those
papers, several indicators have been derived to analyze the data. They provide information about the number of degrees of freedom
active in the systems and the probability of jumps towards other metastable states.
The goal of this PhD study is to go one step further, and use this method to forecast the behavior of complex systems. The PhD candidate
will construct stochastic generators of plausible turbulent, climate and financial fields by including the underlying dynamical properties as
derived by the previous indicators. She/he will assess the quality of the generated fields by comparing the results on real data. During the
PhD thesis, the candidate will acquire competences in statistics, fundamental physics, climate dynamics and finance. She/he will develop
numerical tools and models with the analysis of time-series.
The Phd Thesis require a good knowledge of stochastic processes and therefore a background on applied statistics or theoretical physics.
The candidate should know how to use statistical analysis software as R, Matlab and/or Python. She/he should have a good level of
understanding of English language, to work in an international environment.
[1] Davide Faranda, Gabriele Messori and Pascal Yiou. Dynamical proxies of North Atlantic predictability and extremes. Accepted for
publication in Scientific Reports, 2017.
[2] Guillaume Nevo, Nikki Vercauteren, Amandine Kaiser, Berengere Dubrulle, Davide Faranda. A statistical-mechanical approach to
study the hydrodynamic stability of stably stratified atmospheric boundary layer. Submitted, 2017.
[3] Davide Faranda and Dimitri Defrance: A wavelet-based-approach to detect climate change on the coherent and turbulent component of
the atmospheric circulation. Earth System Dynamics, 7 517-523, 2016.
LOCATION
Institut rayonnement et matière de Saclay
Commissariat à l'énergie atomique et aux énergies alternatives
Institut national des sciences et techniques nucléaires
www­instn.cea.fr
1
Service de Physique de l'Etat Condensé
Systèmes Physiques Hors-équilibre, hYdrodynamique, éNergie et compleXes
Place: Saclay
Start date of the thesis: 01/09/2017
CONTACT PERSON
Davide Faranda
CEA
DRF/LSCE (DSM)
CEA Saclay
Phone number: +33 1 69 08 52 32
Email: [email protected]
UNIVERSITY / GRADUATE SCHOOL
Paris-Saclay
Ondes et Matière
FIND OUT MORE
http://iramis.cea.fr/spec/Phocea/Membres/Annuaire/index.php?uid=dfaranda
http://iramis.cea.fr/spec/sphynx/
http://iramis.cea.fr/Pisp/berengere.dubrulle/index.html
THESIS SUPERVISOR
Bérengère DUBRULLE
CNRS
DRF/IRAMIS/SPEC/SPHYNX
CEA/Saclay
Commissariat à l'énergie atomique et aux énergies alternatives
Institut national des sciences et techniques nucléaires
www­instn.cea.fr
Powered by TCPDF (www.tcpdf.org)
2