Nonlinear Processes in Geophysics

The role of time-varying external factors in the intensification of tropical cyclones
Samuel Watson and Courtney Quinn
Nonlin. Processes Geophys., 31, 381–394, https://doi.org/10.5194/npg-31-381-2024, 2024
The intensification of tropical cyclones (TCs) is explored through a conceptual model derived from geophysical principals. Focus is put on the behaviour of the model with parameters which change in time. The rates of change cause the model to either tip to an alternative stable state or recover the original state. This represents intensification, dissipation, or eyewall replacement cycles (ERCs). A case study which emulates the rapid intensification events of Hurricane Irma (2017) is explored.

Statistical and neural network assessment of climatological features of fog and mist at Pula airport in Croatia: from local to synoptic scale
Marko Zoldoš, Tomislav Džoić, Jadran Jurković, Frano Matić, Sandra Jambrošić, Ivan Ljuština, and Maja Telišman Prtenjak
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2024-18,2024
Preprint under review for NPG (discussion: open, 0 comments)
Fog can disrupt aviation by causing accidents and delays due to low visibility, yet it remains under-researched in Croatia. This study examined fog and mist at Pula Airport using 20 years of data and machine learning techniques. There is a declining trend in fog, linked to changing weather patterns. Fog mainly occurs from October to March. These findings enhance knowledge about fog in Croatia and can improve weather forecasts, increasing safety at the airport.

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, 2024
A methodology for directly predicting the time evolution of the assumed parameters for the distribution densities based on the Liouville equation, as proposed earlier, is extended to multidimensional cases and to cases in which the systems are constrained by integrals over a part of the variable range. The extended methodology is tested against a convective energy-cycle system as well as the Lorenz strange attractor.

Negative Differential Resistance, Instability, and Critical Transition in Lightning Leader
Xueqiang Gou, Chao Xin, Liwen Xu, Ping Yuan, Yijun Zhang, and Mingli Cheng
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2024-15,2024
Preprint under review for NPG (discussion: open, 0 comments)
Our research examines lightning's complex behavior by studying changes in its electrical pathways under different conditions. We found that lightning channels switch between stable and unstable states based on their length and surrounding electric fields. This helps explain why lightning often reactivates after a brief pause and offers new insights into these processes. Our findings could improve lightning prediction and protection, benefiting scientific understanding and public safety.

Simulation characteristics of seismic translation and rotation under the assumption of nonlinear small deformation
Wei Li, Yun Wang, Chang Chen, and Lixia Sun
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2024-17,2024
Preprint under review for NPG (discussion: open, 2 comments)
In contrast to classical elastodynamics, which assumes linear small deformations, we develop new seismic elastic wave equations using the Green strain tensor and explore nonlinearity as a source of observed disparities. We simulate different seismic sources to analyze translational and rotational components, revealing significant errors in linear approximations. Our results show that nonlinear effects are pronounced in rotational motions during strong earthquakes.

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, 2024
A novel approach, optimal transport data assimilation (OTDA), is introduced to merge DA and OT concepts. By leveraging OT's displacement interpolation in space, it minimises mislocation errors within DA applied to physical fields, such as water vapour, hydrometeors, and chemical species. Its richness and flexibility are showcased through one- and two-dimensional illustrations.

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, 2024
Recently, scientists have been looking into ways to control the weather to lead to a desirable direction for mitigating weather-induced disasters caused by torrential rainfall and typhoons. This study proposes using the model predictive control (MPC), an advanced control method, to control a chaotic system. Through numerical experiments using a low-dimensional chaotic system, we demonstrate that the system can be successfully controlled with shorter forecasts compared to previous studies.

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, 2024
The goal of this paper is to weight several dynamic models in order to improve the representativeness of a system. It is illustrated using a set of versions of an idealized model describing the Atlantic Meridional Overturning Circulation. The low-cost method is based on data-driven forecasts. It enables model performance to be evaluated on their dynamics. Taking into account both model performance and codependency, the derived weights outperform benchmarks in reconstructing a model distribution.

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, 2024
Forecasts have uncertainties. It is thus essential to reduce these uncertainties. Such reduction requires uncertainty quantification, which often means running costly models multiple times. The cost limits the number of model runs and thus the quantification’s accuracy. This study proposes a technique that utilizes users’ knowledge of forecast uncertainties to improve uncertainty quantification. Tests show that this technique improves uncertainty reduction.

Multi-dimensional, Multi-Constraint Seismic Inversion of Acoustic Impedance Using Fuzzy Clustering Concepts
Saber Jahanjooy, Hosein Hashemi, and Majid Bagheri
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2024-12,2024
Preprint under review for NPG (discussion: open, 3 comments)
This manuscript introduces a new method of using the objective function of fuzzy clustering in seismic inversion. Multiple constraints on the data misfit, allow the operator to apply different conditions on the results. The solution is simple. New concepts that are the results of the inversion methods are good sources for interpretation.

A quest for precipitation attractors in weather radar archives
Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis V. Sideris, Urs Germann, and Isztar Zawadzki
Nonlin. Processes Geophys., 31, 259–286, https://doi.org/10.5194/npg-31-259-2024, 2024
We compared two ways of defining the phase space of low-dimensional attractors describing the evolution of radar precipitation fields. The first defines the phase space by the domain-scale statistics of precipitation fields, such as their mean, spatial and temporal correlations. The second uses principal component analysis to account for the spatial distribution of precipitation. To represent different climates, radar archives over the United States and the Swiss Alpine region were used.

Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations
John Bjørnar Bremnes, Thomas N. Nipen, and Ivar A. Seierstad
Nonlin. Processes Geophys., 31, 247–257, https://doi.org/10.5194/npg-31-247-2024, 2024
During the last 2 years, tremendous progress has been made in global data-driven weather models trained on reanalysis data. In this study, the Pangu-Weather model is compared to several numerical weather prediction models with and without probabilistic post-processing for temperature and wind speed forecasting. The results confirm that global data-driven models are promising for operational weather forecasting and that post-processing can improve these forecasts considerably.

Quantum data assimilation: a new approach to solving data assimilation on quantum annealers
Shunji Kotsuki, Fumitoshi Kawasaki, and Masanao Ohashi
Nonlin. Processes Geophys., 31, 237–245, https://doi.org/10.5194/npg-31-237-2024, 2024
In Earth science, data assimilation plays an important role in integrating real-world observations with numerical simulations for improving subsequent predictions. To overcome the time-consuming computations of conventional data assimilation methods, this paper proposes using quantum annealing machines. Using the D-Wave quantum annealer, the proposed method found solutions with comparable accuracy to conventional approaches and significantly reduced computational time.

Characterisation of Dansgaard-Oeschger events in palaeoclimate time series using the Matrix Profile
Susana Barbosa, Maria Eduarda Silva, and Denis-Didier Rousseau
Nonlin. Processes Geophys. Discuss., https//doi.org/10.5194/npg-2024-13,2024
Revised manuscript accepted for NPG (discussion: closed, 4 comments)
The characterisation of abrupt transitions in palaeoclimate records allows the understanding of millennial climate variability and of potential tipping points in the context of current climate change. In our study an algorithmic method, the matrix profile, is employed to characterise abrupt warmings designated as Dansgaard-Oeschger (DO) events and to identify the most similar transitions in the palaeoclimate time series.