Articles | Volume 22, issue 6
https://doi.org/10.5194/npg-22-663-2015
https://doi.org/10.5194/npg-22-663-2015
Research article
 | 
11 Nov 2015
Research article |  | 11 Nov 2015

Local finite-time Lyapunov exponent, local sampling and probabilistic source and destination regions

A. E. BozorgMagham, S. D. Ross, and D. G. Schmale III

Related authors

Data generated during the 2018 LAPSE-RATE campaign: an introduction and overview
Gijs de Boer, Adam Houston, Jamey Jacob, Phillip B. Chilson, Suzanne W. Smith, Brian Argrow, Dale Lawrence, Jack Elston, David Brus, Osku Kemppinen, Petra Klein, Julie K. Lundquist, Sean Waugh, Sean C. C. Bailey, Amy Frazier, Michael P. Sama, Christopher Crick, David Schmale III, James Pinto, Elizabeth A. Pillar-Little, Victoria Natalie, and Anders Jensen
Earth Syst. Sci. Data, 12, 3357–3366, https://doi.org/10.5194/essd-12-3357-2020,https://doi.org/10.5194/essd-12-3357-2020, 2020
Short summary
Surfaces of silver birch (Betula pendula) are sources of biological ice nuclei: in vivo and in situ investigations
Teresa M. Seifried, Paul Bieber, Laura Felgitsch, Julian Vlasich, Florian Reyzek, David G. Schmale III, and Hinrich Grothe
Biogeosciences, 17, 5655–5667, https://doi.org/10.5194/bg-17-5655-2020,https://doi.org/10.5194/bg-17-5655-2020, 2020
Macromolecular fungal ice nuclei in Fusarium: effects of physical and chemical processing
Anna T. Kunert, Mira L. Pöhlker, Kai Tang, Carola S. Krevert, Carsten Wieder, Kai R. Speth, Linda E. Hanson, Cindy E. Morris, David G. Schmale III, Ulrich Pöschl, and Janine Fröhlich-Nowoisky
Biogeosciences, 16, 4647–4659, https://doi.org/10.5194/bg-16-4647-2019,https://doi.org/10.5194/bg-16-4647-2019, 2019
Short summary
Comprehensive characterization of an aspen (Populus tremuloides) leaf litter sample that maintained ice nucleation activity for 48 years
Yalda Vasebi, Marco E. Mechan Llontop, Regina Hanlon, David G. Schmale III, Russell Schnell, and Boris A. Vinatzer
Biogeosciences, 16, 1675–1683, https://doi.org/10.5194/bg-16-1675-2019,https://doi.org/10.5194/bg-16-1675-2019, 2019
Short summary
Birch leaves and branches as a source of ice-nucleating macromolecules
Laura Felgitsch, Philipp Baloh, Julia Burkart, Maximilian Mayr, Mohammad E. Momken, Teresa M. Seifried, Philipp Winkler, David G. Schmale III, and Hinrich Grothe
Atmos. Chem. Phys., 18, 16063–16079, https://doi.org/10.5194/acp-18-16063-2018,https://doi.org/10.5194/acp-18-16063-2018, 2018
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
Prognostic assumed-probability-density-function (distribution density function) approach: further generalization and demonstrations
Jun-Ichi Yano
Nonlin. Processes Geophys., 31, 359–380, https://doi.org/10.5194/npg-31-359-2024,https://doi.org/10.5194/npg-31-359-2024, 2024
Short summary
Bridging classical data assimilation and optimal transport: the 3D-Var case
Marc Bocquet, Pierre J. Vanderbecken, Alban Farchi, Joffrey Dumont Le Brazidec, and Yelva Roustan
Nonlin. Processes Geophys., 31, 335–357, https://doi.org/10.5194/npg-31-335-2024,https://doi.org/10.5194/npg-31-335-2024, 2024
Short summary
Leading the Lorenz 63 system toward the prescribed regime by model predictive control coupled with data assimilation
Fumitoshi Kawasaki and Shunji Kotsuki
Nonlin. Processes Geophys., 31, 319–333, https://doi.org/10.5194/npg-31-319-2024,https://doi.org/10.5194/npg-31-319-2024, 2024
Short summary
Selecting and weighting dynamical models using data-driven approaches
Pierre Le Bras, Florian Sévellec, Pierre Tandeo, Juan Ruiz, and Pierre Ailliot
Nonlin. Processes Geophys., 31, 303–317, https://doi.org/10.5194/npg-31-303-2024,https://doi.org/10.5194/npg-31-303-2024, 2024
Short summary
Improving ensemble data assimilation through Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC)
Man-Yau Chan
Nonlin. Processes Geophys., 31, 287–302, https://doi.org/10.5194/npg-31-287-2024,https://doi.org/10.5194/npg-31-287-2024, 2024
Short summary

Cited articles

Abarbanel, H. D., Brown, R., and Kennel, M. B.: Local Lyapunov Exponents Computed from Observed Data, J. Nonlin. Sci., 2, 343–365, 1992.
Batchelor, G. K.: An Introduction to Fluid Dynamics, Cambridge University Press, 2000.
BozorgMagham, A. E. and Ross, S. D.: Atmospheric Lagrangian Coherent Structures Considering Unresolved Turbulence and Forecast Uncertainty, Commun. Nonlin. Sci. Numer. Simul., 22, 964–979, 2015.
BozorgMagham, A. E., Ross, S. D., and Schmale, D. G.: Real-time Prediction of Atmospheric Lagrangian Coherent Structures Based on Uncertain Forecast Data: An Application and Error Analysis, Physica D, 258, 47–60, 2013.
Branicki, M. and Wiggins, S.: Finite-Time Lagrangian Transport Analysis: Stable and Unstable Manifolds of Hyperbolic Trajectories and Finite-Time Lyapunov Exponents, arXiv preprint arXiv:0908.1129, 2009.
Download
Short summary
In this paper a new interpretation of the local finite-time Lyapunov exponent is proposed. This concept can practically assist in field experiments where samples are collected at a fixed location and it is necessary to attribute long-distance transport phenomena and location of source points to the characteristic variation of the sampled particles. Also, results of this study have the potential to aid in planning of optimal local sampling of passive particles.