https://npg.copernicus.org/articles/ Combined list of the recent articles of the journal Nonlinear Processes in Geophysics and the recent discussion forum Nonlinear Processes in Geophysics Discussions https://doi.org/10.5194/npg-33-303-2026 <b>Nonlinear quantitative relationship between the duration and occurrence frequency of droughts</b><br> Pengcheng Yan, Guolin Feng, Cailing Zhao, Ping Yang, Hao Wu, and Dongdong Zuo<br> Nonlin. Processes Geophys., 33, 303–312, https://doi.org/10.5194/npg-33-303-2026, 2026<br> In this study, we examine the relationship between drought duration and frequency across China using daily data. We find a clear double-logarithmic relationship between the duration and the frequency. We also show that droughts in dry northwestern areas tend to last for months, while those in wet southeastern regions are shorter but more frequent. This pattern holds across all drought intensities. Overall, our findings offer a simple tool for drought risk assessment and water management. 2026-06-08T15:12:56+02:00 https://doi.org/10.5194/npg-33-267-2026 <b>Structural joint modeling of magnetotelluric data and Rayleigh wave dispersion curves using Pareto-based particle swarm optimization: an example to delineate the crustal structure of the southeastern part of the Biga Peninsula in western Anatolia</b><br> Ersin Büyük, Ekrem Zor, and Mustafa Cengiz Tapırdamaz<br> Nonlin. Processes Geophys., 33, 267–302, https://doi.org/10.5194/npg-33-267-2026, 2026<br> We introduce a Pareto-based multi-objective particle swarm optimization framework for joint modeling of magnetotelluric and Rayleigh wave dispersion data from the southeastern Biga Peninsula. The approach uses a shared structural parameterization without enforcing a fixed petrophysical link between resistivity and velocity. The study shows that magnetotelluric data are more affected by model trade-offs, whereas Rayleigh wave dispersion is more sensitive in data space. 2026-06-02T15:12:56+02:00 https://doi.org/10.5194/npg-33-233-2026 <b>Boosting ensembles for statistics of tails at conditionally optimal advance split times</b><br> Justin Finkel and Paul A. O'Gorman<br> Nonlin. Processes Geophys., 33, 233–265, https://doi.org/10.5194/npg-33-233-2026, 2026<br> Estimating small probabilities of high-impact extreme weather events is a persistent computational challenge, motivating techniques such as rare event sampling and ensemble boosting: lightly perturbing simulated moderate events into more extreme ones. We formulate a new, flexible sampling strategy and characterizes a critical parameter – the advance split time, dictating when to perturb – in a simple atmospheric turbulence model, with generalizable entropy-based criteria. 2026-05-28T15:12:56+02:00 https://doi.org/10.5194/npg-33-197-2026 <b>Sandy beaches' chaos: shoreline-sandbar coupling inferred from observational time series</b><br> Marius Aparicio, Sylvain Mangiarotti, Salomé Frugier, Laurent Lacaze, Marcan Graffin, and Rafael Almar<br> Nonlin. Processes Geophys., 33, 197–231, https://doi.org/10.5194/npg-33-197-2026, 2026<br> We studied how sandy beaches evolve by tracking the shoreline and offshore sandbars from satellites over many years. By rebuilding beach behavior directly from observations, we show that beaches follow organized but chaotic motion shaped by internal feedbacks. Beyond the seasonal rhythm imposed by waves, shorelines and sandbars exchange energy through the surf zone, producing repeated erosion and recovery cycles with limited predictability, explaining why beaches remain difficult to forecast. 2026-04-21T15:12:56+02:00 https://doi.org/10.5194/npg-33-173-2026 <b>Bayesian inference based on algorithms: MH, HMC, MALA and Lip-MALA for prestack seismic inversion</b><br> Richard Perez-Roa, Saba Infante, Gabriel Barragan, and Raul Manzanilla<br> Nonlin. Processes Geophys., 33, 173–195, https://doi.org/10.5194/npg-33-173-2026, 2026<br> We explored four methods to improve how underground rock properties are estimated from seismic data. By comparing these methods on both simulated and real-world oilfield data, we found that techniques using gradient information give better accuracy but require more computing time. Our results help guide the choice of method depending on whether speed or precision is more important in subsurface exploration. 2026-04-20T15:12:56+02:00 https://doi.org/10.5194/npg-33-157-2026 <b>Spatiotemporal variation in rainfall predictability in Serbia under a changing climate</b><br> Tatijana Stosic, Ivana Tošić, Antonio Samuel Alves da Silva, Vladimir Djurdjević, and Borko Stosic<br> Nonlin. Processes Geophys., 33, 157–172, https://doi.org/10.5194/npg-33-157-2026, 2026<br> In this work we address the change in rainfall predictability in Serbia due to climate change, using a novel entropy-based method that highlights both small and large fluctuations. The study is performed on data from 14 stations from 1961–2020. While rainfall average remains rather stable between two subperiods, the predictability of large and small fluctuations has changed, suggesting that climate change has affected rainfall dynamics in ways not observable by standard statistical methods. 2026-03-24T15:12:56+01:00 https://doi.org/10.5194/npg-33-123-2026 <b>Beyond static forecasts: a dynamic stress gradient framework for high-resolution aftershock prediction and mitigation</b><br> Boi-Yee Liao<br> Nonlin. Processes Geophys., 33, 123–155, https://doi.org/10.5194/npg-33-123-2026, 2026<br> After major earthquakes, smaller shocks often follow, yet predicting where they will occur remains difficult. This study introduces a new method for tracking changes in underground stress after a large earthquake. Using the 2018 Hualien earthquake in Taiwan as a case study, we found that areas with strong stress differences provide clearer signals of future aftershocks than stress magnitude alone. This approach can improve short-term earthquake risk assessment and disaster response planning. 2026-03-19T15:12:56+01:00 https://doi.org/10.5194/npg-33-103-2026 <b>Inferring the role of Interdecadal Pacific Oscillation phase on tropical-extratropical teleconnection dependencies</b><br> Mark A. Collier, Dylan Harries, and Terence J. O'Kane<br> Nonlin. Processes Geophys., 33, 103–122, https://doi.org/10.5194/npg-33-103-2026, 2026<br> Here we apply Bayesian methods to reconstructed and simulated climate model data over past decades to determine the role of long timescale phase dependencies, and extratropical teleconnections, on the major drivers of tropical climate variability. 2026-03-04T15:12:56+01:00 https://doi.org/10.5194/npg-33-73-2026 <b>MESMER-RCM: a probabilistic climate emulator for regional warming projections</b><br> Hao Pan, Lukas Gudmundsson, Mathias Hauser, Jonas Schwaab, Yann Quilcaille, and Sonia I. Seneviratne<br> Nonlin. Processes Geophys., 33, 73–83, https://doi.org/10.5194/npg-33-73-2026, 2026<br> Existing regional climate model (RCM) emulators mainly provide deterministic emulations, while internal RCM variability is typically not represented. We develop MESMER-RCM, a probabilistic RCM emulator for annual 2-m temperature, using a simple and physically interpretable approach. We demonstrate its ability to emulate both RCM trends and internal variability in a high-dimensional spatial setting, where existing approaches typically struggle. 2026-02-12T15:12:56+01:00 https://doi.org/10.5194/npg-33-85-2026 <b>Dynamic mode decomposition of extreme events</b><br> Maša Ann, Jörn Behrens, and Jana Sillmann<br> Nonlin. Processes Geophys., 33, 85–102, https://doi.org/10.5194/npg-33-85-2026, 2026<br> We present a new framework based on Dynamic Mode Decomposition (DMD) to better detect outliers and model extremes. Unlike standard DMD, which focuses on average system behaviour, our approach targets rare, exceptional dynamics. Applied to climate data, it improves extreme event approximation and reveals meaningful spatiotemporal patterns. The method may generalise to other types of extremes. 2026-02-12T15:12:56+01:00 https://doi.org/10.5194/npg-33-51-2026 <b>On transversality and the characterization of finite time hyperbolic subspaces in chaotic attractors</b><br> Terence J. O'Kane and Courtney R. Quinn<br> Nonlin. Processes Geophys., 33, 51–72, https://doi.org/10.5194/npg-33-51-2026, 2026<br> Mathematical concepts and measures from dynamical systems theory are applied to identify commonalities across a diverse set of chaotic attractors to better understand the relationship between predictability, directions and rates of expansion and contraction of instabilities over finite time forecast horizons, and dimensionality. The patterns that emerge have broad implications for understanding many dynamical features of geophysical flows. 2026-02-11T15:12:56+01:00 https://doi.org/10.5194/npg-33-33-2026 <b>Localization in the mapping particle filter</b><br> Juan M. Guerrieri, Manuel Pulido, Takemasa Miyoshi, Arata Amemiya, and Juan J. Ruiz<br> Nonlin. Processes Geophys., 33, 33–49, https://doi.org/10.5194/npg-33-33-2026, 2026<br> This work extends the Mapping Particle Filter to account for local dependencies. Two localization methods are tested: a global particle flow with local kernels, and iterative local mappings based on correlation radius. Using a two-scale Lorenz-96 truth and a one-scale forecast model, experiments with full/partial and linear/nonlinear observations show Root Mean Square Error reductions using localized Gaussian mixture priors, achieving competitive performance against Gaussian filters. 2026-01-26T15:12:56+01:00 https://doi.org/10.5194/npg-33-17-2026 <b>Exploring urban heat islands with a simple thermodynamic model</b><br> Mijeong Jeon, Kyeongjoo Park, Woosok Moon, Jae-Jin Kim, and Jong-Jin Baik<br> Nonlin. Processes Geophys., 33, 17–32, https://doi.org/10.5194/npg-33-17-2026, 2026<br> Using a simple day-night thermodynamic model based on surface energy balance, this study explains key mechanisms of the urban heat island (UHI): reduced diurnal temperature range (DTR) due to high heat capacity and increased mean temperature from low albedo. The model captures the stronger nighttime UHI and reproduces observed patterns, showing its value in understanding UHI dynamics and urban effects. 2026-01-06T15:12:56+01:00 https://doi.org/10.5194/npg-33-1-2026 <b>Impact of reduced non-Gaussianity on analysis and forecast accuracy by assimilating every-30 s radar observation with ensemble Kalman filter: idealized experiments of deep convection</b><br> Arata Amemiya and Takemasa Miyoshi<br> Nonlin. Processes Geophys., 33, 1–16, https://doi.org/10.5194/npg-33-1-2026, 2026<br> The accurate estimation of atmospheric state variables from radar observation in rapidly growing deep convection, which causes heavy thunderstorms, is a major challenge. This study examines the advantage of incorporating radar observation data with very high frequency such as 30 s compared with the conventional case of 5 min, from a theoretical perspective. 2026-01-05T15:12:56+01:00 https://doi.org/10.5194/npg-32-489-2025 <b>Nonlinear wavefield characteristics of seismic translation and rotation in small-strain deformation from moment tensor simulations</b><br> Wei Li, Yun Wang, Chang Chen, and Lixia Sun<br> Nonlin. Processes Geophys., 32, 489–501, https://doi.org/10.5194/npg-32-489-2025, 2025<br> This study uses numerical simulations to investigate geometric nonlinearity in six-component seismic motions. Our results reveal the characteristic of these nonlinear effects: while their pattern in body waves is universal, their excitation efficiency shows a strong source-type dependency. It demonstrates that rotational components are highly sensitive to effects driven by S-waves, and that surface waves may be the primary carriers of these features. 2025-12-05T15:12:56+01:00 https://doi.org/10.5194/npg-32-471-2025 <b>On process-oriented conditional targeted covariance inflation (TCI) for 3D-volume radar data assimilation</b><br> Klaus Vobig, Roland Potthast, and Klaus Stephan<br> Nonlin. Processes Geophys., 32, 471–488, https://doi.org/10.5194/npg-32-471-2025, 2025<br> We present a novel approach to targeted covariance inflation (TCI) which aims to improve the assimilation of 3D radar reflectivity and, possibly, short-term forecasts of reflectivity and precipitation. Using an operational numerical weather prediction framework, our numerical results show that TCI makes the system accurately generate new reflectivity cells and significantly improves the fractional skill score of forecasts over lead times of up to 6 h by up to 10 %. 2025-11-24T15:12:56+01:00 https://doi.org/10.5194/npg-32-457-2025 <b> Bottom–up approach for mitigating extreme events with limited intervention options: a case study with Lorenz 96 model</b><br> Takahito Mitsui, Shunji Kotsuki, Naoya Fujiwara, Atsushi Okazaki, and Keita Tokuda<br> Nonlin. Processes Geophys., 32, 457–469, https://doi.org/10.5194/npg-32-457-2025, 2025<br> Extreme weather poses serious risks, making prevention crucial. Using the Lorenz 96 model as a testbed, we propose a bottom-up approach to mitigate extreme events via local interventions guided by multi-scenario ensemble forecasts. Unlike control-theoretic methods, our approach selects the best control scenario from available options. It achieves a high success rate of 99.4% while maintaining reasonable costs, offering a practical strategy to reduce extremes under limited control. 2025-11-04T15:12:56+01:00 https://doi.org/10.5194/npg-32-439-2025 <b>Exploring the influence of spatio-temporal scale differences in coupled data assimilation</b><br> Lilian Garcia-Oliva, Alberto Carrassi, and François Counillon<br> Nonlin. Processes Geophys., 32, 439–456, https://doi.org/10.5194/npg-32-439-2025, 2025<br> We used a simple coupled model and a data assimilation method to find the correct initialisation for climate predictions. We aim to clarify when weakly or strongly coupled data assimilation (WCDA or SCDA) is best, depending on the system's dynamical characteristics (spatio-temporal) and data coverage. We found that WCDA is better in full data coverage. When we have a partially observed system, SCDA is better. This result depends on the temporal and spatial scale of the observed quantity. 2025-10-24T15:12:56+02:00 https://doi.org/10.5194/npg-32-411-2025 <b>Quantifying variability in Lagrangian particle dispersal in ocean ensemble simulations: an information theory approach</b><br> Claudio M. Pierard, Siren Rühs, Laura Gómez-Navarro, Michael Charles Denes, Florian Meirer, Thierry Penduff, and Erik van Sebille<br> Nonlin. Processes Geophys., 32, 411–438, https://doi.org/10.5194/npg-32-411-2025, 2025<br> Particle-tracking simulations compute how ocean currents transport material. However, initializing these simulations is often ad hoc. Here, we explore how two different strategies (releasing particles over space or over time) compare. Specifically, we compare the variability in particle trajectories to the variability of particles computed in a 50-member ensemble simulation. We find that releasing the particles over 20 weeks gives variability that is most like that in the ensemble. 2025-10-20T15:12:56+02:00 https://doi.org/10.5194/npg-32-397-2025 <b>Improving dynamical climate predictions with machine learning: insights from a twin experiment framework</b><br> Zikang He, Julien Brajard, Yiguo Wang, Xidong Wang, and Zheqi Shen<br> Nonlin. Processes Geophys., 32, 397–409, https://doi.org/10.5194/npg-32-397-2025, 2025<br> Climate prediction is challenging due to systematic errors in traditional climate models. We addressed this by training a machine learning model to correct these errors and then integrating it with the traditional climate model to form an AI-physics hybrid model. Our study demonstrates that the hybrid model outperforms the original climate model on both short-term and long-term predictions of the atmosphere and ocean. 2025-10-20T15:12:56+02:00