Nonlinear Processes in Geophysics

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Combined list of the recent articles of the journal Nonlinear Processes in Geophysics and the recent discussion forum Nonlinear Processes in Geophysics Discussions
Updated: 1 day 8 hours ago

The adaptive particle swarm optimization technique for solving microseismic source location parameters

Mon, 07/15/2019 - 16:42
The adaptive particle swarm optimization technique for solving microseismic source location parameters
Hong-Mei Sun, Jian-Zhi Yu, Xing-Li Zhang, Bin-Guo Wang, and Rui-Sheng Jia
Nonlin. Processes Geophys., 26, 163-173, https://doi.org/10.5194/npg-26-163-2019, 2019
An adaptive PSO optimization method is proposed based on the average population velocity in order to solve for location parameters of the seismic source in a location model. Combined with the actual need to solve for seismic source parameters, the model constraints of inertia weight, accelerating constants, the maximum flight velocity of particles, and other parameters are discussed in order to improve the optimization capacity of the PSO algorithm and avoid being trapped in a local optimum.

Magnitude correlations in a self-similar aftershock rates model of seismicity

Mon, 07/15/2019 - 16:42
Magnitude correlations in a self-similar aftershock rates model of seismicity
Andres F. Zambrano Moreno and Jörn Davidsen
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-19,2019
Manuscript under review for NPG (discussion: open, 0 comments)
We study a model containing the characteristic of self-similarity (invariance under scale) which allows for scaling between lab experiments and geographical scale seismicity. Particular to this model is the dependency of the earthquake rates on the magnitude difference between events that are causally connected. We present results of a statistical analysis of magnitude correlations for the model along with its implications for the ongoing efforts in earthquake forecasting.

Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models

Wed, 07/10/2019 - 16:42
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models
Marc Bocquet, Julien Brajard, Alberto Carrassi, and Laurent Bertino
Nonlin. Processes Geophys., 26, 143-162, https://doi.org/10.5194/npg-26-143-2019, 2019
This paper describes an innovative way to use data assimilation to infer the dynamics of a physical system from its observation only. The method can operate with noisy and partial observation of the physical system. It acts as a deep learning technique specialised to dynamical models without the need for machine learning tools. The method is successfully tested on chaotic dynamical systems: the Lorenz-63, Lorenz-96, and Kuramoto–Sivashinski models and a two-scale Lorenz model.

A comprehensive model for the kyr and Myr timescales of Earth's axial magnetic dipole field

Mon, 07/08/2019 - 16:42
A comprehensive model for the kyr and Myr timescales of Earth's axial magnetic dipole field
Matthias Morzfeld and Bruce A. Buffett
Nonlin. Processes Geophys., 26, 123-142, https://doi.org/10.5194/npg-26-123-2019, 2019
We discuss how to calibrate simplified models of the geomagnetic dipole field to geomagnetic observations collected over the past 2 Myr over two different timescales.

BP Neural Network and improved Particle Swarm Optimization for Transient Electromagnetic Inversion

Tue, 07/02/2019 - 16:42
BP Neural Network and improved Particle Swarm Optimization for Transient Electromagnetic Inversion
Huaiqing Zhang, Ruiyou Li, Nian Yu, Ruiheng Li, and Qiong Zhuang
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-36,2019
Manuscript under review for NPG (discussion: open, 0 comments)
The COPSO-BP algorithm is proposed for transient electromagnetic inversion. The BP's initial weight and threshold parameters were trained by COPSO algorithm, which overcomes the shortcoming of single BP falls easily into local optimum. The layered geoelectric model inversion showed that COPSO-BP method has better accuracy, stability and relative less training times. It is expected to be used in 1-D direct current sounding, 1-D magnetotelluric sounding, seismic wave impedance, source detection.

A Bayesian approach to multivariate adaptive localization in ensemble-based data assimilation with time-dependent extensions

Fri, 06/14/2019 - 16:42
A Bayesian approach to multivariate adaptive localization in ensemble-based data assimilation with time-dependent extensions
Andrey A. Popov and Adrian Sandu
Nonlin. Processes Geophys., 26, 109-122, https://doi.org/10.5194/npg-26-109-2019, 2019
This work has to do with a small part of existing algorithms that are used in applications such as predicting the weather. We provide empirical evidence that our new technique works well on small but representative models. This might lead to creation of a better weather forecast and potentially save lives as in the case of hurricane prediction.

Statistical hypothesis testing in wavelet analysis: theoretical developments and applications to Indian rainfall

Fri, 06/14/2019 - 16:42
Statistical hypothesis testing in wavelet analysis: theoretical developments and applications to Indian rainfall
Justin A. Schulte
Nonlin. Processes Geophys., 26, 91-108, https://doi.org/10.5194/npg-26-91-2019, 2019
Statistical hypothesis tests in wavelet analysis are used to asses the likelihood that time series features are noise. The choice of test will determine which features emerge as a signal. Tests based on area do poorly at distinguishing abrupt fluctuations from periodic behavior, unlike tests based on arclength that do better. The application of the tests suggests that there are features in Indian rainfall time series that emerge from background noise.

Generalization properties of neural networks trained on Lorenzsystems

Fri, 06/14/2019 - 16:42
Generalization properties of neural networks trained on Lorenzsystems
Sebastian Scher and Gabriele Messori
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-23,2019
Manuscript under review for NPG (discussion: open, 2 comments)
Neural networks are a technique that is widely used to predict the time-evolution of physical systems. For this the neural network is shown past evolution of the system – it is "trained" – and then can be used to predict the evolution in the future. We show some limitations in this approach for certain systems that are important to consider when using neural networks for climate and weather-related applications.

Numerical Bifurcation Methods applied to Climate Models: Analysis beyond Simulation

Wed, 06/12/2019 - 16:42
Numerical Bifurcation Methods applied to Climate Models: Analysis beyond Simulation
Henk A. Dijkstra
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-29,2019
Manuscript under review for NPG (discussion: open, 3 comments)
I provide a personal view on the role of bifurcation analysis of climate models in the development of a theory of variability in the climate system. By outlining the state-of-the-art of the methodology and by discussing what has been done and what has been learned from a hierarchy of models, I will argue that there are low-order phenomena of climate variability, such as El Nino and the Atlantic Multidecadal Oscillation.

Prediction and variation of auroral oval boundary based on deep learning model and space physical parameters

Wed, 06/05/2019 - 16:42
Prediction and variation of auroral oval boundary based on deep learning model and space physical parameters
Yiyuan Han, Bing Han, Zejun Hu, Xinbo Gao, Lixia Zhang, Huigen Yang, and Bin Li
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-28,2019
Manuscript under review for NPG (discussion: open, 1 comment)
We design a new nonlinear model to construct the accurate relationship between auroral oval boundaries and 18 space physical parameters, and explore the influence of every single space physical parameter on auroral oval boundary in this paper. As a result, we found the combination of some space physical parameters can strengthen each other’s influence on aurora oval boundary prediction, and this model can achieve the best performance when only partial space physical parameters are used as input.

Statistical post-processing of ensemble forecasts of the height of new snow

Wed, 06/05/2019 - 16:42
Statistical post-processing of ensemble forecasts of the height of new snow
Jari-Pekka Nousu, Matthieu Lafaysse, Matthieu Vernay, Joseph Bellier, Guillaume Evin, and Bruno Joly
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-27,2019
Manuscript under review for NPG (discussion: open, 2 comments)
Forecasting the height of new snow is crucial for avalanche hazard, roads viability, ski resorts and tourism. The numerical models suffer from systematic and significant errors which are misleading for the final users. Here, we applied for the first time a state-of-the-art statistical method to correct ensemble numerical forecasts of the height of new snow from their statistical link with measurements in French Alps and Pyrenees. Thus, the realism of automatic forecasts can be quickly improved.

A Parallel Hybrid Intelligence Algorithm for Solving Conditional Nonlinear Optimal Perturbation to Identify Optimal Precursors of North Atlantic Oscillation

Tue, 06/04/2019 - 16:42
A Parallel Hybrid Intelligence Algorithm for Solving Conditional Nonlinear Optimal Perturbation to Identify Optimal Precursors of North Atlantic Oscillation
Bin Mu, Jing Li, Shijin Yuan, Xiaodan Luo, and Guokun Dai
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-25,2019
Manuscript under review for NPG (discussion: open, 1 comment)
The North Atlantic Oscillation (NAO) phenomenon has a significant impact on the global climate. In this paper, we propose a hybrid algorithm to identify the perturbations that trigger NAO events. The result indicates that the perturbations solved by our method can trigger the NAO mode successfully. Moreover, using the parallel framework, the speedup ratio of the parallel algorithm achieves 40 compared to the serial version.

Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales

Fri, 05/24/2019 - 16:42
Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales
Michiel Van Ginderachter, Daan Degrauwe, Stéphane Vannitsem, and Piet Termonia
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-26,2019
Manuscript under review for NPG (discussion: open, 3 comments)
A generic methodology is developed to estimate the model error and simulate the model uncertainty related to a specific physical process. The method estimates the model error by comparing two different representations of the physical process in otherwise identical models. The found model error can then be used to perturb the model and simulate the model uncertainty. When applying this methodology to deep convection an improvement in the probabilistic skill of the ensemble forecast is found.

Compacting the Description of a Time-Dependent Multivariable System and Its Time-Dependent Multivariable Driver by Reducing the System and Driver State Vectors to Aggregate Scalars: The Earth’s Solar-Wind-Driven Magnetosphere

Mon, 05/20/2019 - 16:42
Compacting the Description of a Time-Dependent Multivariable System and Its Time-Dependent Multivariable Driver by Reducing the System and Driver State Vectors to Aggregate Scalars: The Earth’s Solar-Wind-Driven Magnetosphere
Joseph E. Borovsky and Adnane Osmane
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-2,2019
Manuscript under review for NPG (discussion: final response, 2 comments)
A methodology is developed to simplify the mathematical description of activity in a time-dependent driven system. The method describes the response in the system that is most-closely related to the driver. This reduced description has advantages: low noise, high prediction efficiency, linearity in the described system response to the driver, and compactness. The analysis of the Earth’s magnetospheric system is demonstrated.

Unraveling the spatial diversity of Indian precipitation teleconnections via nonlinear multi-scale approach

Fri, 05/17/2019 - 16:42
Unraveling the spatial diversity of Indian precipitation teleconnections via nonlinear multi-scale approach
Jürgen Kurths, Ankit Agarwal, Norbert Marwan, Maheswaran Rathinasamy, Levke Caesar, Raghvan Krishnan, and Bruno Merz
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-20,2019
Revised manuscript under review for NPG (discussion: final response, 4 comments)
We examined the spatial diversity of Indian rainfall teleconnection at different time scales, first by identifying homogenous communities and later by computing nonlinear linkages between the identified communities (spatial regions) and dominant climatic patterns, represented by climatic indices such as El-Nino Southern Oscillation, Indian Ocean Dipole, North Atlantic Oscillation, Pacific Decadal Oscillation and Atlantic multi-decadal Oscillation.

Negentropy anomaly analysis of the borehole strain associated with the Ms 8.0 Wenchuan earthquake

Tue, 05/14/2019 - 16:42
Negentropy anomaly analysis of the borehole strain associated with the Ms 8.0 Wenchuan earthquake
Kaiguang Zhu, Zining Yu, Chengquan Chi, Mengxuan Fan, and Kaiyan Li
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-22,2019
Manuscript under review for NPG (discussion: open, 2 comments)
The borehole strain data of the Guza station are used to study the negentropy anomalies before the Wenchuan earthquake. We observed the distribution of anomalies in the skewness-kurtosis domain and accumulated the anomaly frequency over time. Our results show: 1) The earthquake moment is proved to be a critical time during the whole earthquake process. 2) Two cumulative acceleration phases are corresponding to the two crustal stress releases, which may be the precursors to the earthquake.

CNOP based on ACPW for Identifying Sensitive Regions of Typhoon Target Observations with WRF Model

Thu, 05/09/2019 - 16:42
CNOP based on ACPW for Identifying Sensitive Regions of Typhoon Target Observations with WRF Model
Bin Mu, Linlin Zhang, Shijin Yuan, and Wansuo Duan
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-24,2019
Manuscript under review for NPG (discussion: final response, 3 comments)
In this paper, we rewrite the adaptive cooperation co-evolution of parallel particle swarm optimization and wolf search algorithm based on principal component analysis (ACPW) and applied it to solve conditional nonlinear optimal perturbation (CNOP) in the WRF-ARW for identifying sensitive areas of typhoon target observations. The experimental results show that the ACPW is meaningful, feasible and effective.

Lyapunov analysis of multiscale dynamics: the slow bundle of the two-scale Lorenz 96 model

Tue, 05/07/2019 - 16:42
Lyapunov analysis of multiscale dynamics: the slow bundle of the two-scale Lorenz 96 model
Mallory Carlu, Francesco Ginelli, Valerio Lucarini, and Antonio Politi
Nonlin. Processes Geophys., 26, 73-89, https://doi.org/10.5194/npg-26-73-2019, 2019
We explore the nature of instabilities in a well-known meteorological toy model, the Lorenz 96, to unravel key mechanisms of interaction between scales of different resolutions and time scales. To do so, we use a mathematical machinery known as Lyapunov analysis, allowing us to capture the degrees of chaoticity associated with fundamental directions of instability. We find a non-trivial group of such directions projecting significantly on slow variables, associated with long term dynamics.

On the nonlinear and Solar-forced nature of the Chandler wobble in the Earth's pole motion

Fri, 04/26/2019 - 16:42
On the nonlinear and Solar-forced nature of the Chandler wobble in the Earth's pole motion
Dmitry M. Sonechkin
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-12,2019
Revised manuscript under review for NPG (discussion: final response, 8 comments)
I look for a combination of some external periodicities, which period coincides with Chandler's period. My predecessors considered a model of the linear oscillator with a viscosity and several periodic external forces. Necessary and sufficient condition for emergence of a peak in power spectrum at Chandler's period is nonlinearity of the oscillator being considered. The main achievement of my work is the proof that it is necessary to consider the raw nonlinear equations of L. Euler.

Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data

Tue, 04/23/2019 - 16:42
Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data
Fei Lu, Nils Weitzel, and Adam H. Monahan
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2019-16,2019
Revised manuscript accepted for NPG (discussion: final response, 5 comments)
ll-posedness of the inverse problem and sparse noisy data are two major challenges in the modeling of high-dimensional spatiotemporal processes. We present a Bayesian inference method with a strongly regularized posterior to overcome these challenges, enabling joint state-parameter estimation and quantifying uncertainty in the estimation. We demonstrate the method on a physically motivated nonlinear stochastic partial differential equation arising from paleoclimate construction.

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