Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 1.558
IF1.558
IF 5-year value: 1.475
IF 5-year
1.475
CiteScore value: 2.8
CiteScore
2.8
SNIP value: 0.921
SNIP0.921
IPP value: 1.56
IPP1.56
SJR value: 0.571
SJR0.571
Scimago H <br class='widget-line-break'>index value: 55
Scimago H
index
55
h5-index value: 22
h5-index22

Editorial board

Editors of this journal work on a purely voluntary basis without remuneration in line with the not-for-profit philosophy of the EGU.
Executive editors

Ana M. Mancho

Consejo Superior de Investigaciones Científicas
ICMAT
Spain

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality

Daniel Schertzer

U. Paris-Est
Ecole des Ponts ParisTech
HM&Co
France

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events

Olivier Talagrand

École Normale Supérieure
Géosciences, Laboratoire de Météorologie Dynamique
France

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Stéphane Vannitsem

Royal Meteorological Institute of Belgium
Meteorological and Climatological Research
Belgium

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events
Editors

Amit Apte

Tata Institute of Fundamental Research
International Centre for Theoretical Sciences
India

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Jörg Büchner

Max-Planck-Institut für Sonnensystemforschung
Germany

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality

Vincenzo Carbone

Università della Calabria
Dipartimento di Fisica
Italy

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality

Alberto Carrassi

University of Reading
United Kingdom

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems

Mickael D. Chekroun

UCLA
Department of Atmospheric and Oceanic Sciences
United States

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Time series, machine learning, networks, stochastic processes, extreme events

Jinqiao Duan

Illinois Institute of Technology
College of Computing
Department of Applied Mathematics
United States

Subject areas

Subject areas

Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events

Wansuo Duan

Institute of Atmospheric Physics, Chinese Academy of Sciences
LASG
China

Subject areas

Subject areas

Predictability, probabilistic forecasts, data assimilation, inverse problems

Nikolai S. Erokhin

Space Research Institute of RAS
Cosmogeophysics Department
Russian Federation

Subject areas

Subject areas

Scaling, multifractals, turbulence, complex systems, self-organized criticality

Harindra Joseph Fernando

University of Notre Dame
Department of Civil Engineering and Geological Sciences
United States

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality

Ulrike Feudel

University of Oldenburg
Institute for Chemistry and Biology of the Marine Environment
Germany

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality

Christian Franzke

Universität Hamburg
Meteorologisches Institut
Center For Earth System Research and Sustainability
Germany

Subject areas

Subject areas

Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events

Behzad Ghanbarian

University of Texas at Austin
United States

Subject areas

Subject areas

Scaling, multifractals, turbulence, complex systems, self-organized criticality

Richard Gloaguen

Helmholtz Institute Freiberg for Resource Technology
Exploration
Germany

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Allen G. Hunt

Wright State University
Physics and Earth & Environmental Sciences
United States

Subject areas

Subject areas

Scaling, multifractals, turbulence, complex systems, self-organized criticality

Kayo Ide

University of Maryland
Department of Atmospheric and Oceanic Science
United States

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Jürgen Kurths

Potsdam Institute for Climate Impact Research
Germany

Subject areas

Subject areas

Time series, machine learning, networks, stochastic processes, extreme events

Giovanni Lapenta

KU Leuven
Center for Mathematical Plasma Astrophysics
Wiskunde
Belgium

Subject areas

Subject areas

Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events

Shaun Lovejoy

McGill University
Department of Physics
Canada

Subject areas

Subject areas

Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events

Valerio Lucarini

University of Reading
Department of Mathematics and Statistics
United Kingdom

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Ana M. Mancho

Consejo Superior de Investigaciones Científicas
ICMAT
Spain

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality

Norbert Marwan

Potsdam Institute for Climate Impact Research
Complexity Science
Germany

Subject areas

Subject areas

Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Takemasa Miyoshi

Japan

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Balasubramanya Nadiga

Los Alamos National Lab.
United States

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

William I. Newman

University of California
Department of Earth and Space Sciences
United States

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality

Joachim Peinke

Carl-von-Ossietzky University Oldenburg
Institute of Physics and ForWind - Center for Wind Energy Research
Germany

Subject areas

Subject areas

Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events

Vicente Perez-Munuzuri

University of Santiago de Compostela
Physics
Spain

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Stefano Pierini

Universita' di Napoli Parthenope
Dipartimento di Scienze e Tecnologie
Italy

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events

Jose M. Redondo

Universitat Politecnica de Catalunya (UPC)
PELNoHT-ERCOFTAC
Dept. Fisica
Spain

Juan Restrepo

Oregon State University
Mathematics
United States

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems

A. Surjalal Sharma

University of Maryland
Goddard Planetary Heliophysics Institute
Astronomy
United States

Subject areas

Subject areas

Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Victor Shrira

Keele University
School of Computing andf Mathematics
United Kingdom

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality

Olivier Talagrand

École Normale Supérieure
Géosciences, Laboratoire de Météorologie Dynamique
France

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Ioulia Tchiguirinskaia

Ecole des Ponts ParisTech, HM&Co
France

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events

Luciano Telesca

National Research Council
Institute of Methodologies for Environmental Analysis
Italy

Subject areas

Subject areas

Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events

Zoltan Toth

NOAA/OAR/ESRL
Global Systems Laboratory
United States

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Bruce Tsurutani

Pasadena Associates
Heliospheric Physics
United States

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events

Stéphane Vannitsem

Royal Meteorological Institute of Belgium
Meteorological and Climatological Research
Belgium

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Time series, machine learning, networks, stochastic processes, extreme events

Vasiliy I. Vlasenko

The University of Plymouth
School of Marine Science and Engineering
United Kingdom

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Scaling, multifractals, turbulence, complex systems, self-organized criticality

Ilya Zaliapin

University of Nevada, Reno
Department of Mathematics and Statistics
United States

Subject areas

Subject areas

Bifurcation, dynamical systems, chaos, phase transition, nonlinear waves, pattern formation
Predictability, probabilistic forecasts, data assimilation, inverse problems
Scaling, multifractals, turbulence, complex systems, self-organized criticality
Time series, machine learning, networks, stochastic processes, extreme events
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