Slides03. Hw1. Lecture 03 -The Linear Model I. Hw1 sol. Jinbow/Octopus.
OceanColor Home Page. FSLE D'OVIDIO. Francesco d'Ovidio My main interest is dynamical system theory and nonlinear dynamics in applications, especially where quantitative modeling is possible. I have worked on the emergence of collective properties in populations of natural and artificial units (respectively, yeast cells and electronic circuits). Currently I am focused on the problem of transport and mixing in geophysical flows and on the interaction between turbulence and marine ecosystems. My new address is: LOCEAN - IPSL, Université Pierre et Marie Curie 4 place Jussieu, 75252 Paris Cedex 5 (France) francesco.dovidio AT locean-ipsl.upmc.fr Please visit my new homepage: www.locean-ipsl.upmc.fr/~dovidio Research done at ISC (2008-2010) Nonlinear ocean dynamics The ocean is characterized by the interaction of spatial and temporal scales that span several orders of magnitude.
Currently, I am focused on the role of filaments in the ocean. Synchronization and emergence of collective properties: Dynamical quorum sensing Collaborations. Submesoscale. For the formation of any layer, and would lead to prediction as fluxes are either directly resolved or parameterized in numerical models. The vertical turbulent fluxes in the transition layer are an important topic for research in the coming years. This region is challenging, as both internal waves and turbulence are strong, its depth changes as it follows the undulations of the mixed-layer base, and any strict assumption of one-dimensionality is unlikely to be satisfactory.
A long-standing problem in biological oceanography is that turbulent fluxes, determined over the years from physical measurements, have never seemed sufficient to describe the observed production. For example, throughout the oligotrophic ocean dissolved inorganic carbon is depleted in the mixed layer each summer by biological processes, yet there are almost no detectable nutrients to support the carbon consumption.
FSLE. Legos FSLE. Obtaining data This dataset can be used freely for any scientific applications provided that (i) one or more of the papers listed below are cited (please choose the most relevant in respect to your application) and (ii) CTOH is acknowledged. To obtain data, please use the Filament Product Request Form. References More information about this implementation of the FSLE, their scientific application and some validation can be found in the following papers. Interpretation of the FSLEs in terms of mixing and of dynamical system theory : F. d'Ovidio, C.
López, E. Comparison of FSLE ridges with chlorophyll fronts from ocean color satellite images : Y. Comparison of the FSLEs with SST infrared satellite images in the East Mediterranean : F. d'Ovidio, J. Comparison of the FSLEs with drifter trajectories : L. Comparison of fronts detected by FSLEs with CTD in situ measurements : A. Use of FSLEs for understanding the impact of the horizontal stirring on the phytoplankton biogeography : F. d'Ovidio, S.
On the nudging terms at open boundaries in regional ocean models. LCS Tutorial: Brief Overview. 1 Brief Overview This tutorial explains the application of finite-time Lyapunov exponents (FTLE) for studying time-dependent dynamical systems. The emphasis here is on dynamical systems with arbitrary time dependence, since there is already a nice repertory of tools to tackle time-independent and time-periodic systems. A leading source for time-dependent dynamical systems are fluid flow problems. So while the ideas stated in this tutorial are expressed in terms of a general dynamical system, we often assume that the system represents a fluid flow. The evolution of such systems is often governed by partial differential equations, yet it is often acceptable to represent such systems by ordinary differential equations when interest is on large scale transport. This is typically accomplished by either numerically solving an approximation of the Navier-Stokes equation, or taking direct measurements of the fluid.
Signal filtering (Butterworth filter) | Ocean Python. Machine Learning Video Library - Learning From Data (Abu-Mostafa) The art of writing science - Iceweasel. Recupere - Iceweasel.